Ai-powered platform for generation of materials and prediction of desired parameters, characteristics, qualities or properties thereof

Abstract

The invention encompasses innovative AI-powered platforms that predict various parameters (e.g. compostability, sustainability, material mechanical properties, formulate solutions properties, economic factors, etc.) and generates formulations for sustainable materials, while accounting for the various physical and non-physical properties as wells the applications they serve. The invention encompasses systematic search algorithms and / or machine learning techniques for identification of biomaterials that are predicted to have applicable properties. This invention encompasses a powerful method and tool to streamline the design and development of materials that are sustainable. In various embodiments, by employing advanced data processing, polymer chemistry knowledge and predictive modeling, the embodiments of the present invention provides precise and efficient recommendations, enhancing the research and development of sustainable biomaterials. Leveraging evolutionary and machine learning methodologies, the embodiments of the present invention identify polymer materials that adhere to real-world renewable constraints and other parameters, emphasizing a multitude of pertinent physical and non-physical properties.

Description

[0001] AI-POWERED PLATFORM FOR GENERATION OF MATERIALS AND PREDICTION OF DESIRED PARAMETERS, CHARACTERISTICS, QUALITIES OR PROPERTIES THEREOF

[0002] FIELD OF THE INVENTION

[0003] 0001 The present invention is directed to Al-driven systems, methods, apparatus and platforms that can predict desired characteristics, qualities or properties (e.g. mechanical, chemical, sustainability, functional and other physical and non-physical properties, etc.) and assist in the production of desired materials (e. compounds, compositions, etc.) for diverse applications. In a preferred embodiment, the present invention is directed to Al-driven systems, methods, apparatus and platforms that can predict desired parameters or characteristics and assist in the production of materials having these parameters or characteristics across diverse applications, including catering to both rigid and flexible usage scenarios. The present invention encompasses evolutionary and / or machine learning techniques for identification of composites having a plurality of properties related to physical and non-physical parameters, including, but not limited to, compostability, sustainability, economic factors, etc. In various embodiments, by employing advanced, polymer chemistry knowledge data processing and predictive / generative modeling, the Al-powered platform provides precise and efficient recommendations, enhancing and accelerating the research and development of materials with desired physical and non-physical characteristics and / or properties. Through sophisticated data analysis and predictive modeling, the invention offers accurate and effective suggestions, thereby boosting the advancement the research, development and adoption of sustainable materials.

[0004] BACKGROUND OF THE INVENTION

[0005] 0002 Substituting traditional fossil-derived plastics with sustainable alternatives like biobased polymers could serve as a crucial strategy in fostering a circular materials economy and mitigating greenhouse gas emissions stemming from polymer manufacturing. Projections suggest that these emissions could escalate from 5 to 15% of the global carbon budget between 2015 and 2050. Despite this potential, biobased polymers currently represent less than 1% of the plastics market, with polylactic acid being the most widely produced variant at 282 kilotons annually as of 2021. Enhancing the market penetration of polymers containing biobased constituents stands to significantly contribute to climate and sustainability targets. Achieving this entails advancing properties essential for performance and production while maintaining competitive pricing.

[0006] 0003 Creating sustainable, compostable and / or eco-friendly materials poses significant hurdles in the field of material science. Conventional techniques for forecasting physical and non-physical traits and refining formulations depend on laborious experimental methods and existing market conditions, resulting in inefficiencies. An evident requirement arises for a pioneering approach that expedites the forecasting and formulation processes for compostable materials, ultimately streamlining research and development endeavors. Traditional methods for predicting desired physical and non-physical properties and optimizing / generating formulations based on those desired properties tend to rely on time-consuming experimental approaches, leading to inefficiencies. There is a clear need for an innovative solution that accelerates the prediction and formulation generation process for compostable materials, thereby streamlining research and development efforts.

[0007] 0004 Achieving a balance in performance across various physical and non-physical properties remains a daunting task in the realm of polymer exploration and enhancement. By capitalizing on the inherent chemical functionalities provided by biobased resources, among other things, there exists an opportunity to enhance polymer characteristics, aiming to optimize the desired characteristics and ultimately to stimulate market acceptance. However, the scope for material exploration is expansive, with an abundance of monomers derived from biological and chemical transformations of biobased sources, surpassing >1 x 10A5, which can be combined in numerous permutations to create polymers. Consequently, there arises a pressing need for swift and precise tools for predicting properties, facilitating the development of biobased polymers.

[0008] 0005 The emerging regulations and policies and the growing demand for more sustainable and environmentally-friendly materials as well as the environmental crisis such as global warming, also makes the development of compostable materials necessary. Innovating new compostable materials to meet all industry and government requirements involves many challenges such as time-consuming certification tests, cost of materials and experiments. Using conventional design of experiment method for optimizing the composites based on their compostability, economic and / or sustainability features, therefore, is prohibitively time-consuming. 0006 Due to the long duration of the compostability tests as well as their costs, there are limited data for the compostability and / or sustainability of many biobased materials. Therefore, developing machine learning algorithms based on a variety of factors, including the chemical structure of the materials, cost of materials, etc., can increase the capabilities and scalability of the machine learning models used for compostability and / or sustainability properties predictions.

[0009] 0007 There is a clear need for this invention as it will increase the process of formulation generation containing biobased materials. This process will be streamlined as more biobased materials can be investigated based on their structural properties.

[0010] SUMMARY OF THE INVENTION

[0011] 0008 The design and manufacturing of bio-based compostable materials represent a crucial endeavor in combating the environmental crisis. These materials offer distinct advantages over traditional petrochemicals, such as improved sustainability and reduced environmental impact. However, their development presents significant challenges, including the need to ensure effective compostability within specific time limits and resource constraints. Addressing these challenges requires a deep understanding of a multiple of factors, both physical (e.g. chemical structure, chemical reactively, etc.) and non-physical (e.g. economic factors such as engineering, cost, regulatory approval, etc.), involved in creating compostable materials. Key to this understanding is the identification of critical properties or parameters that influence compostability, such as the chemical makeup and molecular structure of polymers, the inclusion of organic and inorganic additives, cost of materials, etc. In a preferred embodiment, these physical properties may impact important thermal characteristics like glass transition temperature and molecular weight, which in turn influence the breakdown of materials in compost environments.

[0012] 0009 Various physical parameters influence the breakdown of materials under composting conditions, encompassing both disintegration and biodegradation processes. These physical parameters include chemical structural components (e.g., esters, etc.), composting conditions (e.g., temperature, duration, etc.). For example, composting temperatures may be correlated with the thermal properties of polymers, such as the glass transition temperature (Tg), which must be exceeded to initiate the composting process. 0010 Various non-physical parameters influence and impact the adoption of materials for the purpose of composting, including, but not limited to; (a) infrastructure requirements - composting and recycling infrastructure vary city to city sometimes, resulting in potential mismatch between materials and waste management systems, impacting uptake; (b) regulations - different geographies have different policies surrounding which materials are defined as compostable, and which ones are acceptable; (c) scalability and volume - due to reduced investment in the space, and its nascency, some emerging solutions do not. yet have the ability to scale to commercial volumes required to service the industry alone; (d) equipment compatibility - emerging technologies often require custom machinery and equipment to develop the material, resulting in a mismatch between the novel material, and industry infrastructure; (f) cost / cost to own: aforementioned obstacles, coupled with unoptimized inputs can lead to material costs that can sometimes be 3 - 5x the cost of traditional plastics.

[0013] 0011 This invention encompasses Al (artificial intelligence) / ML (machine learning)-powered algorithms, that facilitate and address the process of biobased formulation prediction and formulation generation algorithms. This invention may facilitate the process of formulation prediction and formulation generation by accounting for the structural, functional, economic information or characteristics of biobased materials.

[0014] 0012 Several critical challenges will be addressed within this biobased plastic industry. The process development of plastics traditionally relies on costly and time-consuming material discovery techniques involving iterative experimental and manufacturing timelines and long certification processes.

[0015] 0013 The ML algorithm of the present invention, based on structural, chemical and economic characteristics or parameters, make the process of formulation generation more efficient. The embodiments of the present invention can increase efficiencies and decrease generation time for composites and / or formulations with desired metrics. Therefore, a lot of resources may be saved by using the embodiments of the present invention. The embodiments of the present invention accounting for chemical structure and function as well as other economic factors, such as supply chain issues, cost of material, cost production, etc. has increased capabilities as it can recommend more and / or better materials compared to other systems. 0014 The preferred embodiments of the present invention have been developed using historical data. In various embodiments, a combination of regression models including machine learning algorithms were used to build the predictive / generative algorithms. The embodiment of the present invention may also calculate biobased carbon content and Global Warming Potential of composites. Using these algorithms and calculations of the present invention, the required time for developing biobased, compostable products will be reduced as the corresponding models are accurate and can predict the compostability and / or sustainability properties and sustainability metrics accurately. The experiments to measure these compostability and / or sustainability properties and sustainability metrics are usually long reducing the efficiency of material development processes without using Al or Machine learning algorithms.

[0016] 0015 In various embodiments, these inventive system and platform encompass a machine learning algorithm for predicting one or more compostability and / or sustainability properties. In one or more embodiments, a user can obtained predicted values for compostability and / or sustainability metrics and properties including, but not limited to, disintegration or biodegradation of selected biobased materials.

[0017] 0016 In other embodiments, the inventive system and platform can use a machine learning algorithm or a combination of several machine learning algorithms including, but not limited to XGboost, deep learning, support vector machines or linear models.

[0018] 0017 In certain embodiments, the system and platform include a combination of machine learning models and an empirical regression model to improve the model prediction quality. In certain embodiments, the user may choose the properties of the materials including the weight percentage of materials and their grades. In certain embodiments, the user may choose the physical properties of the composite of interest.

[0019] 0018 In various embodiments, the invention allows the user to select from within a list of 100% biobased materials, partially biobased materials and even petroleum based materials, or combination thereof.

[0020] 0019 In certain embodiments, the machine learning algorithms are trained to account for solubility parameters and properties based on the established standards for compostability and / or sustainability metrics measurements. In certain embodiments, the algorithms account for the chemical structure of the materials for making the output predictions. In certain embodiments, the grades of materials may be accounted for during model training. In certain embodiments, the properties such as density, molecular weight, glass transition temperature, degree of crystallization may be used to train the model accounting for material grades. In certain embodiments, the experimental conditions corresponding to compostability and / or sustainability properties are accounted for during model training. These experimental conditions may be experiment media, testing temperature, and extent of oxygen exposure. In certain embodiments, the experimental media may be any of soil, compost, and water. In certain embodiments, the properties of the media may be accounted for during model training. In certain embodiments, these properties may be any of compost composition, carbon content, nitrogen content, water content, microorganism contents, and pH.

[0021] 0020 In various embodiments, the invention encompasses data extraction and modeling methods comprising an Al-powered platform employing data extraction and modeling methodologies for effective prediction of compostability, sustainability and / or economic factors and generation of a bio-based formulations comprising various materials especially for sustainable polymers in order to empower all components of the applicable supply chain, such as, for example, researchers, purchasers, sellers, and engineers to manufacture more informed decisions in material development, ultimately driving innovation and progress in numerous fields.

[0022] 0021 In certain embodiments, the data is structured into one or more parameters comprising material input parameters, process input parameters, and output parameters.

[0023] 0022 In various embodiments, the invention comprises a platform that performs two essential tasks based on user inputs: prediction of compostability and / or sustainability properties of biobased composites and optimization. In certain embodiments, the prediction module predicts the compostability and / or sustainability properties based on specific input parameters. In other embodiments, the optimization module adjusts parameters iteratively to achieve desired compostability and / or sustainability properties.

[0024] 0023 In various embodiments, the compostable polymeric materials of the invention are defined by their chemical composition, however more is needed as the processing steps applied to the material, results in formulations, which also determine physical and non-physical properties of the material.

[0025] 0024 In various embodiments, the physical and n on-physical parameters are therefore used to define the compositions of the invention. In various embodiments, generating or defining the compostability and / or sustainability properties of the bio-based formulations, which incorporate the polymeric materials comprises identifying one or polymers and where appropriate identifying the specific physical and non-physical parameters of interest (e.g. compostability, sustainability properties of the one or more polymers, supply chain issues, cost of material, cost production, etc.) that can be applied to the bio-based composition to obtain the desired or at least the predicted or computed compostability, sustainability, production, etc. properties of the formulation or composition comprising such polymers.

[0026] 0025 In one embodiment, the invention encompasses a method of computation of the compostability and / or sustainability properties of one or more biobased polymer materials or combination of polymer materials including certain optimal properties for a compostable, polymeric formulation comprising identifying a defined a class of physical or compostability and / or sustainability properties related the one or more polymers, in particular a class of compostability and / or sustainability properties.

[0027] 0026 In another embodiment, the invention encompasses one or more polymeric materials and methods of obtaining such materials, wherein the polymeric materials satisfy the requirements based on the compostability and / or sustainability performance of the polymeric material. The invention further provides methods for generating novel polymeric structures comprising polymeric materials, which may together be suitable for generating formulations or compositions that are compostable.

[0028] 0027 The invention encompasses an Al-powered method for predicting properties (e.g. compostability and / or sustainability of bio-based polymers and identifying formulations of compostable materials. In various embodiments, the invention encompasses a plurality of compostability and / or sustainability properties of various kinds. Accordingly, the optimization methods are multi-property optimization methods. It is known that a property can either become part of the function used or may appear as a constraint to be satisfied by the solution of the method instead or even both. In other embodiments, a multi-property optimization can be performed by combining properties for instance in a weighted sum formula. The invention may choose stratified sampling techniques to find as many solutions that satisfy the requirements. These sampling methods are used to ensure that the whole range of sampling spaces are searched appropriately to find the solutions.

[0029] 0028 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for computing compostability and / or sustainability properties of a biobased polymer based on information about the polymer chemical composition, by (i) loading the polymer chemical composition and (ii) computing the compostability and / or sustainability properties of the polymer chemical composition. The algorithm may or may not structure information depending on the availability of data to calculate the chemical structure information of the ingredients to do these calculations.

[0030] 0029 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for identifying one or more polymers with desired parameters or properties, by (i) loading a set of selected parameters or properties selected from, but not limited too, the compostability, sustainability, associated supply chain, material costs, production costs, etc. of the polymers and (ii) computing a polymeric chemical composition possessing such desired parameters or properties.

[0031] 0030 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for generating one or more formulations, by defining their (finally selected) polymeric chemical composition, being based on an multi -objective optimization method, the method comprising: (i) the computation of the first aspect and second aspect and (ii) performing a multiobjective optimization related to compostability and / or sustainability preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms or by systematic search algorithm over plausible materials concentrations.

[0032] 0031 In another embodiment, the invention encompasses an Al-based method for the identification of a compostable properties of a bio-based polymeric formulation based on an intrinsic physical property of the polymeric material, which is computed related to compostability and / or sustainability. In another embodiment, the invention encompasses an Al-based method for the identification of a compostable properties of a bio-based polymeric formulation based on a non-intrinsic non-physical property of the polymeric material, which is computed related to compostability and / or sustainability.

[0033] 0032 In another embodiment, the invention encompasses a method carried out by a computer, for computing one or more compostability and / or sustainability properties of a bio-based formulation, based on information about the chemical composition of the polymer or other inorganic components of the formulation, the method comprising: (i) loading information about the chemical composition relevant to the structure; and (ii) computing one or more compostability and / or sustainability properties of a structure based on said loaded information.

[0034] 0033 In certain embodiments, the machine learning algorithms are trained to account for solubility parameters and properties based on the established standards for compostability and / or sustainability metrics measurements. In certain embodiments, the algorithms account for the chemical structure of the materials for making the output predictions. In certain embodiments, the grades of materials may be accounted for during model training. In certain embodiments, the properties such as density, molecular weight, glass transition temperature, degree of crystallization may be used to train the model accounting for material grades. In certain embodiments, the experimental conditions corresponding to compostability and / or sustainability properties are accounted for during model training. These experimental conditions may be experiment media, testing temperature, and extent of oxygen exposure. In certain embodiments, the experimental media may be any of soil, compost, and water. In certain embodiments, the properties of the media may be accounted for during model training. In certain embodiments, these properties may be any of compost composition, carbon content, nitrogen content, water content, microorganism contents, and pH.

[0035] 0034 In a particular embodiment, the method comprises: (i) computing information of a compostable polymeric material; and (ii) computing said compostability and / or sustainability properties of a formulation from said information by use of a trained model.

[0036] 0035 In a particular embodiment, said step of computing information of a formulation comprises the step of generating a formulation, based on information about the physical and non-physical parameters associated with the formulation, which result is being further enriched by information about the compostability and / or sustainability of the polymeric composition.

[0037] 0036 In another embodiment, the invention encompasses a method, carried out by a computer, of generating one or more compostable formulations by defining their polymeric and optionally inorganic chemical composition being based on a multi-objective optimization method or a systematic search algorithm, the method comprising: (i) computing a plurality of compostability and / or sustainability properties and (ii) performing a multi-objective optimization method including those computed compostability and / or sustainability properties as objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms, or using a systematic search algorithm over plausible materials concentrations.

[0038] 0037 In various embodiments, the invention encompasses a method, carried out by a computer, of generating one or more formulations, by defining their (finally selected) polymeric composition being based on an multi-objective optimization method, the method comprising: (i) computing a plurality of objectives, with at least one compostability and / or sustainability property, preferably at least two compostability and / or sustainability properties and optionally at least one structural properties; and performing an multi-objective optimization method based on said computed objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms or based on using a systematic search algorithm over plausible materials concentrations.

[0039] 0038 In certain embodiments, the methods may preferably be supported by training methods, in particular a method, carried out by a computer, of training a model suitable for use in computing for a given formulation based on the compostability and / or sustainability properties of the polymer components, said training of said model being based on (i) loading information of a plurality of given compostability and / or sustainability properties and related structure properties and (ii) training the model based on said loaded information, wherein said model preferably being one or more (convolutional) neural networks and / or a method, carried out by a computer, of training a model suitable for use in computing for a given chemical composition geometry generating relevant information, said training of said model being based on (i) loading information of a plurality of given polymers and their compostability and / or sustainability properties and (ii) training said model based on said loaded information, wherein said model preferably being one or more neural networks or other machine learning algorithms such as XGboost, random forest or support vector machines or linear regression models.

[0040] 0039 In other embodiments, the methods may also output the computed compostability and / or sustainability properties, which may be useful for further post processing of the materials (when made) and / or feeding other simulation methods used further in the process as part of post processing.

[0041] 0040 In another embodiment, the invention encompasses a search method, carried out by a computer, for determining one or more polymeric materials, by outputting their finally selected polymeric formulation and required material (industrial) processing steps such as melting, casting, extrusion, molding, powder sintering, heat treatment, shaping / forming. Solidification, and mechanical processing, these being selected by an optimization method, the method comprising: (i) computing one or more compostability and / or sustainability properties, selected from a predefined group of and (ii) performing an optimization method including those computed compostability and / or sustainability properties as objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms or a systematic search algorithm, wherein said compute step (i) being based on a method, for computing said one or more compostability and / or sustainability properties of a material, based on information about the polymeric composition, in particular those listed herein, the method comprising: (i) loading information about the polymeric composition and compostability and / or sustainability properties; and (ii) computing one or more compostability and / or sustainability properties of a material based on said loaded information.

[0042] 0041 Further the invention describes a method, carried out by a computer, of training a model for computing for a polymeric formulation (of a material) and applied processing steps, generating information (of said formulation), said model, inputted with one or more polymeric components and one or more inorganic components and applied material processing steps and outputting geometry generating information, said training of said model being based on (i) loading a plurality of chemical compositions and applied material processing steps and their related geometry generating information and (ii) training, by adapting the model parameters, the model, based on said loaded information by comparing the output provided by the model with the loaded information (in that the difference between the output and the loaded information needs to be minimized). One may perform this method a first time for geometry generating information directly useful for generating the image and a second time for enrichment information.

[0043] 0042 In various embodiments, the methods of the invention comprise a search method, carried out by a computer, for determining one or more polymeric materials, by outputting their selected chemical composition selected by an optimization method, the method comprising: (1) performing an optimization method, comprising (i) computing one or more compostability and / or sustainability properties, and (ii) performing an optimization method including those computed compostability and / or sustainability properties as objectives, preferably the method being based on evolutionary computation, a systematic search algorithm or optimization algorithms such as genetic algorithms, wherein said compute step comprise (i) computing one or more compostability and / or sustainability properties of a bio-based polymer, based on and computing compostability and / or sustainability properties of at least one polymer from said information by use of a first trained model (2), determining the polymer composition by use of a second trained model (by use of an inversion of a predetermined model).

[0044] 0043 In another embodiment, the invention encompasses a method, carried out by a computer, for determining one or more compostable formulations by outputting a final formulation based on their finally selected chemical composition, being selected by an optimization method, the method comprising: performing an optimization method including a computed compostability and / or sustainability, the method being based on evolutionary computation, systematic search algorithms or optimization algorithms such as genetic algorithms, wherein said compute step (i) being based on a method, for computing at least one compostability and / or sustainability property of a material, based on information about the formulation, the method comprising: (i) loading information about the formulation; and (ii) computing at least one compostability and / or sustainability property of a bio-based material related to compostability and / or sustainability based on said loaded information. The invention provides an embodiment wherein at least one or more compostability and / or sustainability properties are computed (irrespectively whether one of those compostability and / or sustainability properties may be part of the other); and said optimization method is a multiobjective optimization method or a systematic search algorithm, based on all said computed objectives (to thereby be able to emphasize their relative importance within the optimization method).

[0045] 0044 The invention further provides a method, carried out by a computer, of training a model for computing for a given bio-based formulation, said model, inputted with chemical composition, said training of said model being based on (i) loading information of a plurality of given compositions and their related compostability and / or sustainability, and (ii) training by adapting the model parameters, the model based on said loaded information by comparing the output provided by the model with the loaded information (in that the difference between the output and the loaded information needs to be minimized).

[0046] 0045 Accordingly, the invention encompasses a system and platform designed for improving the formulation design process focusing on compostability and / or sustainability properties of polymer composites, wherein a variety of properties are predicted based on the formulation components including polymer, inorganic, and organic components provided to the framework as input. In various embodiments, the formulation components can be specified by the platform users, wherein they can select among materials with a variety of chemical properties and practical functions. In embodiments, the system and platform use one or multiple machine learning algorithms, including, but not limited to, decision-tree based, ensembling, deep neural network, support vector machine or linear models, to make predictions. In certain embodiments, non-machine learning algorithms may be combined with the machine learning algorithms to improve the prediction capabilities of the embodiments of the present invention. In various embodiments of the invention disclosed herein, the materials that comprise the components of the bio-based compositions and formulations encompassed by the invention include, but are not limited to, bio-based components, inorganic components, and / or petroleum components. In various embodiments, these materials and components include, but are not limited to, polymers, additives, plasticizers, fillers, and / or compatibilizers. In certain embodiments, the compostability and / or sustainability properties include, but are not limited to, disintegration, biodegradation, biobased content and climate change impact metrics. In certain embodiments, these predictions may be reported based on the established standards. In certain embodiments, the models are trained based on the historical data. In certain embodiments, the validation of the predictions based on the model can be implemented using conventional methods or methods that are devised or based on the type of the historical data. 0046 In certain embodiments, the inputs of the embodiments of the present invention may be features for describing the shape of the composites being tested. In certain embodiments, the embodiments of the present invention may support information about the composites when they are tested in different shapes. In certain embodiments, the predictions may be obtained for any of film-shaped, granular-shaped, or spherical-shaped materials which may be in powder form. In certain embodiments, the data can be obtained by systematic interpolation of output values. These outputs could be any of disintegration, biodegradation metrics, or biobased content values. In certain embodiments, these interpolations can be conducted based on the chemical structure of the composites or based on similarities in formulation recipes. In certain embodiments, the materials in formulation recipe can be confirmed by the algorithm utilized by the system and platform to evaluate the compatibility of selected materials. In certain embodiments, these evaluations may be based on machine learning algorithms or based on the established solubility rules in polymer chemistry. In certain embodiments, the machine learning algorithms are trained to account for solubility parameters and properties based on the established standards for compostability and / or sustainability metrics measurements. In certain embodiments, the algorithms account for the chemical structure of the materials for making the output predictions. In certain embodiments, the grades of materials may be accounted for during model training. In certain embodiments, the properties such as density, molecular weight, glass transition temperature, degree of crystallization may be used to train the model accounting for material grades. In certain embodiments, the experimental conditions corresponding to compostability and / or sustainability properties are accounted for during model training. These experimental conditions may be experiment media, testing temperature, and extent of oxygen exposure. In certain embodiments, the experimental media may be any of soil, compost, and water. In certain embodiments, the properties of the media may be accounted for during model training. In certain embodiments, these properties may be any of compost composition, carbon content, nitrogen content, water content , microorganism contents, and pH. In certain embodiments, the embodiments of the present invention users may add information for new materials to combine the historical data in the embodiments of the present invention with the new information they have from their experimentations. In certain embodiments, the user can override the information for the available materials using their data. 0047 The invention represents a significant advancement in the field, building upon previous developments by introducing a comprehensive approach to identifying, predicting, generating and customizing sustainable polymer materials for a wide range of applications.

[0047] 0048 The invention comprises methods, systems, apparatus and platforms for identifying distinct sustainable, biodegradable, and / or eco-friendly polymer materials, predicting their properties (e.g. mechanical and functional properties), customizing these approaches for sustainable and / or biodegradable formulations, and devising or assessing materials for diverse applications, encompassing both rigid and flexible products. Moreover, the present invention encompasses techniques for producing or recognizing polymeric materials by considering various characteristics, qualities or features, such as their mechanical properties, chemical structure, and other individual characteristics (such as polymer grade, molecular weight, and poly dispersity) regarding their biodegradability, compostability, and / or sustainability, employing computational methods.

[0048] 0049 In specific embodiments, the present invention involves a platform facilitating the creation of sustainable solutions achieving up to approximately 100% biobased content. This is achieved by utilizing inorganic materials or, alternatively, petroleum-based inputs to produce a biodegradable, compostable, and sustainable formulation, optionally featuring a low Life Cycle Assessment (LCA) and a high Biobased Carbon Content (BCC).

[0049] 0050 In multiple embodiments, the invention comprises an Al-driven platform capable of predicting physical (e.g. mechanical, chemical, thermal, and barrier) and non-physical (e.g. supply chain, material costs, production costs and other economic considerations) properties while formulating sustainable, compostable and / or biodegradable materials. Through the integration of sophisticated algorithms and data-driven techniques, the present invention accelerates the advancement of sustainable materials, enhancing their properties and functionalities.

[0050] 0051 In various embodiments, the invention involves data extraction, model selection and modeling techniques utilizing an Al-driven platform. This platform employs methodologies for extracting data and modeling to accurately predict mechanical properties and formulate biodegradable, sustainable, and / or compostable materials, particularly focusing on sustainable polymers in various use cases. The aim is to equip researchers and engineers with comprehensive insights, enabling them to make informed decisions in material development. Ultimately, this drives innovation and advancement across various fields.

[0051] 0052 In specific embodiments, the data is organized into several parameters, including material input parameters, process input parameters, and output parameters. Material parameters encompass the behavior of individual materials, such as polymers, plasticizers, elastomers, and fillers. Additionally, specific material properties such as polymer grade, molecular weight, and polymer dispersity are taken into account. Process parameters include factors related to production processes and product types, which are crucial considerations. Furthermore, output parameters are tailored to different standards based on application requirements (e.g. rigid and / or flexible materials).

[0052] 0053 In another embodiment, the invention involves one or more machine learning models integrated into an Al-driven platform. These models include support vector machines (SVM), artificial neural networks (ANN), linear regression, random forest, and k-nearest neighbors (KNN) and XGBoost, utilized for predictive modeling purposes. A careful selection process ensures that each model is well-suited for specific user inputs. Across various embodiments, accuracy scores are pivotal in determining the appropriate model for selection.

[0053] 0054 In various embodiments, preferred embodiment of the invention involves a platform designed to execute three fundamental functions tailored to user inputs: material discovery, material development, and an import / export module. Within certain embodiments, the discovery module anticipates physical and non-physical properties by analyzing distinct input parameters, including material composition, grades, costs, and sample type. Alternatively, in other embodiments, the development module iteratively fine-tunes parameters to attain the desired mechanical, chemical, thermal, barrier and economi c / producti on properties, aligning with the intended end-of-life characteristics. Additionally, the import / export module facilitates the acceptance of user-collected or explored data, providing functionality akin to the other two modules but tailored to user data. Moreover, this module aids users in backing up and logging data derived from the Al platform.

[0054] 0055 In various embodiments, preferred embodiments of the invention involve a platform designed to support users (e.g. users, buyers and sellers of materials and composites) with search, ranking, and recommendation systems based on user input. Other desired use cases include films flexible, barrier, etc, various types of flexible and rigid packaging, food contact materials, etc.

[0055] 0056 In another embodiment, the invention encompasses one or more polymeric materials and methods of obtaining such materials, wherein the polymeric materials satisfy the requirements based on the mechanical performance of the polymeric material related to biodegradation, compostability, or sustainability and translated into certain mechanical properties for certain novel polymers, therefore the invention provides methods for generating novel polymeric structures comprising polymeric materials, which may together be suitable for generating formulations or compositions that are biodegradable, compostable, or sustainable in various use cases of flexible and rigid materials.

[0056] 0057 The invention encompasses an Al-powered method for predicting mechanical properties of sustainable polymers and identifying formulations of sustainable (e.g. biodegradable, compostable, etc.) materials based on their application. In various embodiments, the invention encompasses a plurality of mechanical properties of various kinds. Accordingly, the optimization methods are multi-property optimization methods. It is known that a property can either become part of the function used or may appear as a constraint to be satisfied by the solution of the method instead or even both. In other embodiments, a multi-property optimization can be performed by combining properties for instance in a weighted sum formula.

[0057] 0058 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for computing mechanical properties of a polymer based on information about the material application, polymer characteristics (e.g. grade, molecular weight and poly dispersity), polymer chemical composition and polymer chemical structure, by (i) loading the polymer chemical composition along with their grades and (ii) product application / type (either rigid or flexible) and (iii) computing the mechanical properties of the polymer chemical composition.

[0058] 0059 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for identifying one or more polymers with certain mechanical properties, by (i) loading the product application and (ii) loading the mechanical properties and (iii) loading the potential materials (iv) computing a polymeric chemical composition possessing such mechanical properties in various . 0060 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for identifying one or more polymers with certain biodegradation, compostability, or sustainability properties, based on information about the mechanical properties, by (i) loading the product application and (ii) loading the mechanical properties and (iii) loading the potential materials (iv) computing a polymeric chemical composition possessing such mechanical properties related to biodegradation, compostability, and / or sustainability.

[0059] 0061 In another embodiment, the invention encompasses an Al-based method, carried out by a computer, for generating one or more formulations, by defining their (finally selected) polymeric chemical composition, being based on an multi -objective optimization method, the method comprising: (i) the computation of the first aspect and second aspect and (ii) performing a multiobjective optimization related to biodegradation, compostability, or sustainability, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms.

[0060] 0062 In a preferred embodiment, the present invention offers an expanded scope of mechanical property exploration beyond what was previously possible. Users can now delve into a plethora of mechanical characteristics tailored to their selected product or sample type. These encompass a wide array of properties, including but not limited to Tensile Strength-MD (MPa), Tensile Strength-TD (MPa), Young’s Modulus-MD (MPa), and Young’s Modulus-TD (MPa), Elongation at Break - MD (%), Elongation at Break - TD (%) each vital in various applications and directional contexts. The MD (Machine Direction) and TD (Transparency Direction) provide nuanced insights, enabling precise calculations and informed decision-making processes.

[0061] 0063 In a preferred embodiment, the computing of said one or more physical or non-physical property or parameters of a material is based on information about the composition such as one or more of the following: (i) sub-grains / domains, second phases, molecular aggregates, intermetallic particles, precipitates particles, dispersoids particles, inclusions, and possible contaminants; and (ii) economic factors such as associated supply chains, material costs, production costs and other economic considerations.

[0062] 0064 In a particular embodiment, the method comprises: (i) computing information of said biodegradable, compostable and / or sustainable polymeric material; and (ii) computing said mechanical properties of a structure from said information by use of a trained model based on chemical structure models including but not limited to SMILES method.

[0063] 0065 In a particular embodiment of this aspect, said step of computing information of a formulation comprises the step of generating a formulation, based on information about the chemical composition, material application and different materials’ grades of said formulation, which result is being further enriched by information about the sustainable polymeric composition, relevant for the mechanical properties of said formulation.

[0064] 0066 In another embodiment, the invention encompasses a method, carried out by a computer, of generating one or more biodegradable, compostable and / or sustainable formulations by defining their polymeric chemical composition being based on a multi-objective optimization method, the method comprising: (i) computing a plurality of mechanical properties and (ii) performing a multiobjective optimization method including those computed mechanical properties as objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms.

[0065] 0067 In a preferred embodiment, the methods may preferably be supported by training methods, in particular a method, carried out by a computer, of training a model suitable for use in computing for a given formulation based on the mechanical properties of the polymer components, said training of said model being based on (i) loading information of a plurality of parameters (e.g. given mechanical geometries and related structure properties) and (ii) training the model based on said loaded information, wherein said model preferably being one or more (convolutional) neural networks and / or a method, carried out by a computer, of training a model suitable for use in computing for a given chemical composition geometry generating relevant information, said training of said model being based on (i) loading information of a plurality of given polymers and their mechanical properties and (ii) training said model based on said loaded information, wherein said model preferably being one or more (convolutional) neural networks.

[0066] 0068 In other embodiments, the methods may also output the computed mechanical properties, which may be useful for further post processing of the materials (when made) and / or feeding other simulation methods used further in the process as part of post processing. 0069 In another embodiment, the invention encompasses a search method, carried out by a computer, for determining one or more polymeric materials, by outputting their finally selected polymeric formulation and required material (industrial) processing steps such as cost of production, material costs, melting, casting, extrusion, molding, powder sintering, heat treatment, shaping / forming. Solidification, and mechanical processing, these being selected by an optimization method, the method comprising: (i) computing one or more mechanical properties, selected from a predefined group of and (ii) performing an optimization method including those computed mechanical properties as objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms, wherein said compute step (i) being based on a method, for computing said one or more mechanical properties of a material, based on information about the polymeric composition, in particular those listed herein, the method comprising: (i) loading information about the polymeric composition and mechanical properties; and (ii) computing one or more mechanical properties of a material based on said loaded information.

[0067] 0070 Further the invention describes a method, carried out by a computer, of training a model for computing for a polymeric formulation (of a material) and applied processing steps, generating information (of said formulation), said model, inputted with one or more polymeric compositions and applied material processing steps and outputting geometry generating information, said training of said model being based on (i) loading a plurality of chemical compositions and applied material processing steps and their related geometry generating information and (ii) training, by adapting the model parameters, the model, based on said loaded information by comparing the output provided by the model with the loaded information (in that the difference between the output and the loaded information needs to be minimized). One may perform this method a first time for geometry generating information directly useful for generating the image and a second time for enrichment information.

[0068] 0071 In various embodiments, the methods of the invention comprise a search method, carried out by a computer, for determining one or more polymeric materials, by outputting their selected chemical composition selected by an optimization method, the method comprising: (1) performing an optimization method, comprising (i) computing one or more mechanical properties, and (ii) performing an optimization method including those computed mechanical properties as objectives, preferably the method being based on evolutionary computation or optimization algorithms such as genetic algorithms, wherein said compute step comprise (i) computing one or more mechanical properties of a biodegradable, compostable and / or sustainable polymer, based on and computing mechanical properties of a polymer from said information by use of a first trained model (2), determining the polymer composition by use of a second trained model (by use of an inversion of a predetermined model).

[0069] 0072 In another embodiment, the invention encompasses a search method, carried out by a computer, for determining one or more biodegradable, compostable and / or sustainable formulations by outputting a final formulation based on their finally selected chemical composition, being selected by an optimization method, the method comprising: performing an optimization method including a computed sustainability (e.g. including biodegradation and / or compostability, etc.) the method being based on evolutionary computation or optimization algorithms such as genetic algorithms, wherein said compute step (i) being based on a method, for computing at least one radiation shielding property of a material, based on information about the formulation, the method comprising: (i) loading information about the formulation; and (ii) computing mechanical property of a material related to biodegradation, compostability and / or sustainability, based on said loaded information. The invention provides an embodiment wherein at least two radiation shielding properties are computed (irrespectively whether one of those radiation shielding properties may be part of the other); and said optimization method is a multiobjective optimization method, based on all said computed objectives (to thereby be able to emphasize their relative importance within the optimization method).

[0070] 0073 The embodiments of the present invention provide a method, carried out by a computer, of training a model for computing for a given sustainable formulation, said model, inputted with chemical composition, said training of said model being based on (i) loading information of a plurality of given compositions and their related biodegradation, compostability, and / or sustainability, and (ii) training by adapting the model parameters, the model based on said loaded information by comparing the output provided by the model with the loaded information (in that the difference between the output and the loaded information needs to be minimized). 0074 By integrating the Tg prediction algorithm as a feature within the Al-powered platform of the present invention for sustainability and / or compostability, materials lacking compostabilitydatasets (i.e. Disintegration and Biodegradation), can utilize Tg within the embodiments of the present invention as a feature, in addition to chemical structure features, to predict the sustainability and / or compostability of said material, for new or unexplored characteristics, qualities, properties or uses.

[0071] 0075 Embodiments of the present invention encompass an innovative machine learning algorithm tailored for Al-powered platforms, specifically designed to predict end-of-life and sustainability properties for renewable biomaterials. Utilizing machine learning techniques and material property structure properties, the embodiments of the present invention predicts the thermal properties of renewable biomaterials, neat and blends, crucial for deploying in a compostability predictive algorithm based on material properties. Given the significant challenges in developing compostable biomaterials, particularly in understanding their complex chemistry and engineering, this invention addresses these hurdles by facilitating predictive capabilities. By leveraging machine learning techniques, the embodiments of the present invention may predict and generate key thermal properties essential for influencing material compostability. Aspects of the invention may involve identifying critical properties such as the chemical makeup and molecular structure of polymers, incorporating organic and inorganic additives, and assessing their impact on essential thermal characteristics, including but not limited to glass transition temperature and molecular weight. Embodiments of the present invention introduces a method to incorporate supporting features into the compostability algorithm, enhancing its predictive capabilities based on structural, molecular, and thermal properties. More preferably, embodiments of the present invention focus on developing a novel algorithm to predict glass transition temperature (Tg), which will be integral to the compostability algorithms as noted herein. Therefore, the Tg prediction algorithm represents an advancement in the algorithms, as it directly supports the accurate assessment of predicting compostability for renewable biomaterials.

[0072] 0076 The invention encompasses a machine-learning (ML) powered algorithm, that streamlines and addresses existing challenges in developing renewable biomaterials that are compostable, preferably with commercially feasible and environmentally compatible compostability properties. The inventive ML-algorithm’s ability to predict the glass transition temperature (Tg) of these materials, a property that may influence compostability. This inventive and predictive capability, centered around the Tg properties, streamlines the process of assessing compostability, by leveraging molecular proteins and features inherent in these materials.

[0073] 0077 The inventive ML-powered algorithm addresses several challenges within the industry by streamlining the design and development of compostable materials that require extensive research and development (R&D) focused on the chemistry and material properties of each input. One of the significant challenges in developing materials that have compostable properties, lies in understanding the complex chemistry and material and thermal properties. To address this, the ML-algorithm identifies key molecular descriptors (e.g. Functional groups), that represent the “chemical” and “molecular” make-up of polymers, as well as the influence of organic and inorganic additives on the essential thermal properties. Thermal properties, like melting temperature (Tm) and glass transition temperature (Tg) can influence and play a role in the mechanisms behind compostability (e.g. Disintegration and Biodegradation) and / or sustainability.

[0074] 0078 Furthermore, the inventive ML-algorithms in material science and engineering (MS&E), particularly in renewable and biomaterials that are compostable and sustainable, for the process of material design and development that have desirable compostability and sustainability properties.

[0075] 0079 The inventive ML-powered algorithm provides molecular feature components for the polymers, blends, and composites. In preferred embodiments, the inventive ML-algorithm is developed using historical data. In various embodiments, a combination of regression models including machine learning algorithms were used to build a predictive model.

[0076] 0080 In preferred embodiment, the present invention may predict the glass transition temperature (Tg) of polymers, blends, additives, and composite. Using this embodiment, the development, prediction, and generation of compostable and sustainable materials is supported by the prediction of Tg, using molecular features, to predict compostables. In other embodiments, the inventive algorithm reduces the likelihood of data scarcity for predictions across compostable materials.

[0077] 0081 In various embodiments, the inventive ML-algorithm encompasses a machine learning regression model tailored for biomaterials, particularly polymers and blends, aimed at applying predictions of glass transition temperature (Tg) based on molecular features for predicting one or more compostability properties. This model serves as a critical tool for assessing compostability attributes of renewable and biomaterials, leveraging molecular descriptors and chemical features to accurately predict Tg values. Moreover, in other embodiments, users can obtain Tg properties directly from the algorithm for sustainability and / or compostability predictions, which includes disintegration and biodegradation for selected materials, including composites with additives. This versatile approach enables the model to accommodate a wide range of biomaterial compositions, including those with additives and composites, providing valuable insights into their compostability potential.

[0078] 0082 In various embodiments, the system and ML-algorithm apply feature-engineering techniques that include molecular properties and features crucial for predicting thermal properties, particularly the glass transition temperature (Tg). These techniques encompass a comprehensive array of molecular characteristics, including but not limited to functional groups (such as Esters, etc.), melting temperature (Tm), and the ratio of glass transition temperature to melting temperature (Tg / Tm ratio). The predicted Tg values play a pivotal role in the compostability algorithm, serving as key parameters for assessing the compostability of materials. By integrating molecular features like functional groups and thermal properties, the algorithm enhances its predictive capabilities, facilitating more accurate assessments of material compostability based on their chemical composition and thermal behavior.

[0079] 0083 In preferred embodiments, the system and ML-algorithm apply machine learning ensemble models that include, but not limited to, XGBoost, Random Forest, Gradient Boosting, or other regression-ensemble techniques.

[0080] 0084 In various embodiments, the polymeric materials of the invention are defined by their chemical composition and properties, and processing steps are applied to determine the thermal glass transition temperature (Tg) of the materials and / or inputs. The historical data is structured into one or more input parameters comprising material features, including but not limited to functional groups, thermal properties, weight percentage (wt%), thermal ratios, material grades, and thermal outputs.

[0081] 0085 In preferred embodiments, the system and platform include machine learning models combined with historical data to predict thermal properties (e.g. Tg) of polymer, blends, additives, and composite materials. In preferred embodiments, the model user may choose the molecular features and functional groups of the material inputs which the Tg property can be obtained. In other embodiments, the model user may build a material using only molecular features and functional groups to predict and generate an output for Tg.

[0082] 0086 In preferred embodiments, the ML-algorithm encompasses mathematical equations to calculate and predict the Tg of blend(s) composed of biobased and / or petroleum derived polymer units. In preferred embodiments, the melting temperature (Tm) paired with mathematical equations is applied to address gaps in historical data for materials.

[0083] 0087 In preferred embodiments, the inventive ML-algorithm encompasses methods for identifying certain compostable materials, predicting the compostability properties of the materials, that are 100% biobased, or in other embodiments even petroleum based, or combinations thereof, by using molecular features and properties to predict Tg as a feature of compostability.

[0084] 0088 In preferred embodiments, the inventive ML-algorithm utilizes machine learning regression models to predict the thermal properties of materials that do not have historical thermal and / or composting (e.g. disintegration and biodegradation).

[0085] 0089 In preferred embodiments, the ML-powered algorithm makes predictions on thermal properties using molecular properties and functional groups, to predict Tg. In preferred embodiments, the inventive Al-powered compostability algorithm can predict the composability by using Tg and / or various other features to make predictions on unseen or novel polymers, blends, additives, and / or composites, in terms of materials compostability properties.

[0086] 0090 In preferred embodiments, the inventive ML-algorithm carries out two core functions based on user inputs: firstly, it facilitates the prediction of compostability properties of renewable biomaterials by forecasting the Tg properties that influence compostability; or alternatively, it predicts both the Tg and compostability by leveraging molecular properties and chemical features inherent in input parameters of novel or unseen materials.

[0087] 0091 The inventive algorithm, carried out by a computer, of a training model for computing for a given polymer, blend(s), or composite, said model, imputed with chemical composition and material properties, will predict the glass transition temperature (Tg) by said training model being based on (i) utilizing historical training data that includes material properties and functional groups, (ii) compositions including various wt% of materials and / or inputs, to predict glass transition temperature using features and applying mathematical functions to deliver a Tg output. The inventive algorithm is then deployed as a feature to the compostability algorithm, to support and deliver accurate and reliable predictions for sustainability and / or compostability of materials, specifically, disintegration and biodegradation properties of renewable biomaterials.

[0088] 0092 Furthermore, in preferred embodiments, the ability to predict the glass transition temperature (Tg) of unseen or unexplored materials based on their molecular properties and features hold significant implications for the development and prediction of emerging sustainable and compostable materials. By leveraging this innovative machine learning algorithm to predict Tg values, for specific function and use to support the prediction of compostability properties, users can streamline the process of material design, assessment, and prediction, in combination with Al-powered compostability algorithms; expediting the discovery of novel materials.

[0089] 0093 Furthermore, the integration of the inventive ML-algorithm for predicting Tg values into the compostability algorithm of the present invention, enhances its predictive capabilities, enabling more accurate predictions for thermal and compostability properties. This advancement supports the inventive Al-powered algorithms for compostable and sustainable alternatives, as an inventive feature, that addresses ongoing environmental challenges, but also promotes the identification and exploration of novel and emerging materials.

[0090] BRIEF DESCRIPTION OF THE FIGURES

[0091] 0094 The embodiments of the present invention shall be more clearly understood with reference to the following detailed description of the embodiments of the invention taken in conjunction with the accompanying drawings, in which:

[0092] 0095 FIG. 1 depicts a preferred embodiment of a predictive system for identifying, predicting, generating and customizing bio-based polymer materials for a wide range of applications;

[0093] 0096 FIG. 2 depicts a preferred embodiment of a generative system for identifying, predicting, generating and customizing bio-based polymer materials for a wide range of applications; 0097 FIG. 3 depicts a further preferred embodiment of the predictive system for both mechanical and sustainability properties so as to identifying, predicting, and customizing bio-based polymer materials for a wide range of applications;

[0094] 0098 FIG. 4 depicts a further preferred embodiment of the generative system for both mechanical and sustainability as well as identifying, generating and customizing bio-based polymer materials for a wide range of applications;

[0095] 0099 FIGS. 5A & 5B depict further preferred embodiments of the predictive and generative systems for identifying, predicting, generating and customizing bio-based polymer materials directed to defined compostable characteristics;

[0096] 00100 FIGS. 6A & 6B depict further preferred embodiments of the predictive and generative systems for identifying, predicting, generating and customizing bio-based polymer materials directed to defined mechanical characteristics;

[0097] 00101 FIG. 7 depicts a further preferred embodiment of the predictive and generative systems for identifying, predicting, generating and customizing sustainable polymer materials based on glass transition temperature;

[0098] 00102 FIG. 8 depicts an exemplary schematic demonstrating a model receives, for example, the weight percentage of the materials and certain parameters and physical properties as inputs and returns the end-of-life (e.g. compostability, biobased carbon content) outputs;

[0099] 00103 FIG. 9 depicts an exemplary schematic demonstrating a model receives, for example, the desired end-of-life properties (i.e., compostability, biobased carbon content) as inputs and returns formulation composition values as output;

[0100] 00104 FIG. 10 depicts an exemplary schematic demonstrating the synergies amongst the algorithms that allow users to deliver novel formulations, while on the other hand, capable of predicting a wide array of properties with high accuracy;

[0101] 00105 FIG. 11 depicts an exemplary schematic demonstrating a model receives, for example, the weight percentage of the materials and certain parameters and physical properties as inputs and returns the end-of-life (i.e., compostability, biobased carbon content, etc.) outputs. The chemical structure of the materials is accounted for to do part of the calculations for some composites; and

[0102] 00106 FIG. 12 illustrates a schematic representation of the model based on the chemical structures of the materials.

[0103] DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

[0104] 00107 The description, which follows, and the embodiments described therein are provided by way of illustration of an example, or examples of particular embodiments of principles and aspects of the present invention. These examples are provided for the purposes of explanation and not of limitation, of those principles of the invention. In the description that follows, like parts are marked throughout the specification and the drawings with the same respective reference numerals.

[0105] 00108 It should also be appreciated that the present invention can be implemented in numerous ways, including as a process, method, an apparatus, a system, a device or a method. In this specification, these implementations, or any other form that the invention may take, may be referred to as processes. In general, the order of the steps of the disclosed processes may be altered within the scope of the invention. The description that follows, and the embodiments described therein, is provided by way of illustration of an example, or examples, of particular embodiments of the principles and aspects of the present invention. These examples are provided for the purposes of explanation, and not of limitation, of those principles and of the invention.

[0106] 00109 It will be understood by a person skilled in the relevant art that in different geographical regions and jurisdictions these terms and definitions used herein may be given different names but relate to the same respective systems.

[0107] 00110 Although the present specification describes components and functions implemented in the embodiments with reference to standards and protocols known to a person skilled in the art, the present disclosure as well as the embodiments of the present invention are not limited to any specific standard or protocol. Each of the standards for Internet and other forms of computer network transmission (e.g., TCP / IP, UDP / IP, HTML, HTTP, SSL and SFTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same functions are considered equivalents.

[0108] 00111 Preferred embodiments of the present invention can be implemented in numerous configurations depending on implementation choices based upon the principles described herein. Various specific aspects are disclosed, which are illustrative embodiments not to be construed as limiting the scope of the disclosure. Although the present specification describes components and functions implemented in the embodiments with reference to standards and protocols known to a person skilled in the art, the present disclosures as well as the embodiments of the present invention are not limited to any specific standard or protocol.

[0109] 00112 Embodiments of the present invention are directed to system, methods and apparatus for predicting, generating and customizing materials for a wide range of applications, including data processing software modules, predictive modeling software modules, formulation recommendation / generation software modules and import / export software modules.

[0110] 00113 A person skilled in the art will understand that the present description will reference terminology from the field of artificial intelligence, including machine learning, and may be known to such a person skilled in the relevant art. A person skilled in the relevant art will also understand that artificial neural networks generally refer to computing or computer systems that are design to mimic biological neural networks (e.g. animal brains). Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other images. A person skilled in the relevant art will understand that a convolutional neural network is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. A digital image is a binary representation of visual data. It contains a series of pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be. A recurrent neural network (RNN) will be understood to refer to artificial neural networks where connections between nodes form a directed graph along a temporal sequence. 00114 Machine learning techniques will generally be understood as being used to identify and classify specific reviewed data. Machine learning approaches first tend to involve what is known in the art as a training phase. In the context of classifying functions, a training “corpus” is first constructed. This corpus typically comprises a set of known applications (as noted herein). Furthermore, it is preferable for the corpus to be representative of the real world scenarios in which the machine learning techniques will ultimately be applied. This is followed by a “training phase” in which the applications together with the labels associated with the data, files, etc. themselves, are fed into an algorithm that implements the “training phase”. The goal of this phase is to automatically derive a “model”. A model effectively encodes a mathematical function whose input is the application and whose output may be a target. Specific machine learning algorithms in the art include the Naive Bayes Algorithm, Artificial Neural Networks, Decision Trees, Support Vector Machines, Logistic Regression, Nearest Neighbors, etc. A person skilled in the relevant art will understand that the term classifier is also used to describe a model. For example, one may refer to a Support Vector Machine classifier. Once the model / classifier is established, it can be used to evaluate new criteria that are presented.

[0111] 00115 In a preferred embodiment of the present invention, the Al training process involves leveraging machine learning techniques to analyze and learn from a comprehensive dataset of polymer formulations as well as various other properties (e.g. mechanical, chemical, functional, and / or sustainability properties). By training a model on specific data, the embodiments of the present invention can provide valuable predictions and insights for the discovery and development of sustainable polymer materials tailored to specific properties, characteristics, applications, etc. A person skilled in the art of training such models will understand that depending on the data trained, the models of the present invention can be directed to specific outcomes (see, for example, Engines 1 and 2 in FIGS. 3 and 4). A person skilled in the relevant art will also understand that multiple engines (e.g. Engines 3, 4, 5, etc. (not shown)) can be used in the preferred embodiments of the present invention. Embodiments of the present invention can involve training predictive and generative modules or engines based on selected properties (e.g. sustainability, physical, mechanical or chemical properties) to drive specific desired outcomes. These trained engines, in preferred embodiments of the present invention, may operate concurrently or sequentially and may interact with each other to assist in predicting and / or generating the desired outcome (e.g. see FIGS. 3 and 4). 00116 In a further preferred embodiment, the steps of the training process accomplished in the context of the embodiments of the present invention involve: (1) Data Collection: The first step is to gather a diverse and comprehensive dataset related to specific characteristics, such as, but not limited to, sustainable polymers, their formulations, and mechanical / chemical / functional / sustainability properties. These datasets may encompass a wide range of polymer compositions, processing conditions, and application requirements to ensure that the Al model can learn effectively from various scenarios. Data are primarily sourced from two main resources, including internal datasets and open literature, ensuring a robust foundation for the Al training process; (2) Data Labeling: Each data point in the dataset needs to be labeled with relevant information. This may include, but is not limited to, details about the polymer composition, processing parameters, mechanical properties (such as tensile strength, Elongation at Break, etc.), sustainability properties and intended application (e.g. rigid or flexible); (3) Feature Extraction: Before feeding the data into the machine learning algorithm, relevant features need to be extracted from the raw data. In the case of the preferred embodiments of the present invention, these features may include, but not limited to, sustainability properties, and mechanical properties (including materials compositions, polymers’ grade, materials’ chemical structure, molecular weight distribution, monomer ratios, processing temperature, curing time, etc.) These features serve as inputs to the Al model; (4) Model Training: Using a suitable machine learning algorithm (in the context of the preferred embodiments of the present invention that may include, but is not limited to, algorithms such as XGBoost, Naive Bayes, Neural Networks, Decision Trees, etc.), the labeled dataset is then used to train the Al model. During the training phase, the algorithm learns the underlying patterns and relationships between the input features (e.g. polymer formulations, chemical structure and process parameters) and the output labels (e.g. mechanical properties or sustainability properties); (5) Model Evaluation and Optimization: Once the initial model is trained, it needs to be evaluated to ensure its performance and accuracy. This involves testing the model on a separate validation dataset to assess its ability to generalize to new instances accurately. If necessary, the model is fine-tuned or optimized to improve its performance; and (6) Deployment and Integration: After the model has been successfully trained and validated, it can be deployed within the Al-powered platform as provided herein.

[0112] 00117 A person skilled in the relevant art will understand that supervised learning refers to machine learning in which the classification of the observed data is inferred from a sample of the data supplied by an outside source. This is in contrast to unsupervised learning where there is only input data (X) and no corresponding output variables. A goal for unsupervised learning may be to model the underlying structure or distribution in the data in order to learn more about the data.

[0113] 00118 A person skilled in the relevant art will understand that the term “deep learning” refers to a type of machine learning based on artificial neural networks. Deep learning is a class of machine learning algorithms (e.g. a set of instructions, typically to solve a class of problems or perform a computation) that use multiple layers to progressively extract higher level features from raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify human-meaningful items such as digits or letters or faces.

[0114] 00119 The term “biodegradable polyester elastomer” is herein defined to refer to an elastomer synthesized from alcohols and a dicarboxylic organic acid(s) that could further include other additives that are biodegradable.

[0115] 00120 The term "bio-based" or “biobased” refers to compounds, compositions, etc. that are derived fully or partially from regenerative organic materials instead of being derived fully from petroleum. Sustainable biomaterials are materials that contribute to environmental improvement in some way, such as having a low life cycle assessment (LCA), being biobased, recyclable, or compostable. These characteristics can be mutually exclusive, meaning a material could fall into the sustainable category without necessarily having all of these traits. Biodegradable materials, on the other hand, specifically break down into CO2, water, and biomass at the end of their life cycle. While "biodegradable" is an academically accepted term that can be used interchangeably with "compostable," the two are not always considered synonymous in industry. Compostable materials are, by definition, biodegradable (they break down into CO2) and also undergo physical disintegration (degradation) within a specified timeframe, up to 90%. Embodiments of the present invention are directed to supporting the development of sustainable materials, with an initial focus on compostable materials as defined by the industry.

[0116] 00121 As used herein, a person skilled in the relevant art will understand that the term “plasticizer” could refer to any additive used to be added to polymers or materials to improve their flexibility, workability, or processability. As used herein, a person skilled in the relevant art will also understand that the term “plasticizer” could refer to any additive used to be added to polymers to improve the compatibility between different polymers or between a polymer and another material, such as a filler or reinforcement.

[0117] 00122 The prefix “bio” as used herein refers to a material that has been derived from a renewable resource.

[0118] 00123 As used herein, a person skilled in the relevant art would understand the term “user” to refer to any person or entity involved in the research, development, testing, manufacture, purchase and sale, application, regulation, and / or approval of biomaterials. Users of biomaterials would include brands, manufacturers, compounders, purchasers / buyers and vendors / sellers of biomaterials

[0119] 00124 As used herein, a person skilled in the relevant art will understand the term “biomaterial” to refer to any substance that is derived from biobased materials. A person skilled in the relevant art will understand that biomaterials may be, but are not required, to be compositable, biodegradable, or sustainable.

[0120] 00125 A person skilled in the relevant art will understand that the terms “blend” and “resin” as used herein interchangeably, refer to a homogeneous mixture of two or more different polymers and / or elastomers along with other additives. A person skilled in the relevant art will understand that blends may be binary, tertiary, quaternary, and quinary depending on the desired properties and specifications of the final product. Additionally, blends can encompass a wide range of ratios and combinations of polymers and elastomers, as well as various additives such as plasticizers, fillers, stabilizers, and pigments, to achieve specific performance characteristics. Blends can involve different types and classes of polymeric materials with various compositions, which may include varying weight percentages (wt%). For instance, a blend could consist of a combination of polylactic acid (PLA) and polybutylene adipate terephthalate (PBAT) in a 50-50 ratio by weight.

[0121] 00126 The term “biodegradable” refers to compositions of the invention that can biodegrade within 12 months in a compost environment in a non-toxic, environmentally compatible manner with no heavy metal nor PTFE content, and remaining soil-safe (i.e., lack of eco-toxins). The compositions of the invention biodegrade within 12 months. Compostable plastic is biodegradable, but not every plastic that is biodegradable is compostable. The compositions of the invention are both biodegradable and compostable. As used herein, “biodegradable” compositions are engineered to biodegrade in compost, soil, or water. In particular, biodegradable plastics are plastics with innovative molecular structures that can be decomposed by bacteria at the end of their life under certain environmental conditions. Biodegradable and / or compostable compositions include polymers and / or composites, biocomposites made using polymers (biobased or not), additives and / or fillers. Some (bio)composite formulations can include only polymer blends, while others may have additives or fillers or both.

[0122] 00127 The term “bioplastics" or “biopolymer” is used to refer to plastics that are bio-based, biodegradable, or fit both criteria. Bio-based plastics of the invention are fully or partly made from renewable feedstock derived from biomass. Commonly used raw materials to produce these renewable feedstocks for plastic production include, but are not limited to, com starch, com stalks, sugarcane stems, cellulose, and various oils and fats from renewable sources.

[0123] 00128 As used herein, “sustainable polymeric formulation” include biodegradable, and / or compostable, and / or sustainable formulations.

[0124] 00129 As used herein, “composites or formulations” encompass material blends developed using the material engineering / science and economic considerations described herein and applicable for the types of materials observed in biomaterials, such as bioplastics and / or compostable / biodegradable materials. Polymeric materials would be under this umbrella.

[0125] 00130 As used herein, “compostable” compositions refer to biodegradation and disintegration into soil conditioning material (i.e., compost). For a plastic to be labeled as industrial "compostable", be broken down by biological treatment at an industrial composting facility in 180 days or less. Composting utilizes microorganisms, agitation, heat, and humidity to yield carbon dioxide, water, inorganic compounds, and biomass that is similar in characteristic to the rest of the finished compost product. Decomposition of the composition should occur at a rate similar to the other elements of the material being composted (e.g., within 6 months) and leave no toxic residue that would adversely impact the ability of the finished compost to support plant growth. ASTM Standard D6400 outlines the specifications that must be met to label a plastic as industrial "compostable". 00131 A person skilled in the relevant art will understand that the term “sustainable” or “sustainability” refers to an integrated approach that takes into consideration environmental concerns along with economic development. In 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” A person skilled in the relevant art will understand that sustainability encompasses compostability (e.g. disintergration and biodegradation capabilities of composites), biodegradability and / or other desirable outcomes (e.g. biobased carbon content (BCC), LCA / GWP (Global Warming Potential), cost, etc.).

[0126] 00132 The term “disintegration” refers to a plastic product that leaves no more than 10% of its original dry weight after twelve weeks (about 90 days) in a controlled thermophilic composting test and sieved through a 2.0-mm mesh.

[0127] 00133 The term “polyesters” refers to polymers of the invention that are obtained, for example, by aliphatic diols, aliphatic dicarboxylic acids, and aromatic dicarboxylic acids / esters. The term polyesters also includes aliphatic and aliphatic-aromatic polyesters. The biodegradable thermoplastic polyesters of the current invention include but are not limited to: polylactic acid (PL A) or poly(lactic acid) (PL A);poly caprolactone (PCL); poly(butylene succinate) (PBS) or polybutylene succinate (PBS); poly(butylene succinate adipate) (PBSA), polybutylene succinate adipate (PBSA), poly(butylene succinate-co-adipate) (PBSA), polybutylene succinate-co-adipate (PBSA), poly(butylene succinate-co-butylene adipate) (PBSA) or polybutylene succinate-co- butylene adipate (PBSA); poly(butylene succinate terephthalate) (PBST), polybutylene succinate terephthalate (PBST), poly(butylene succinate-co-terephthalate) (PBST), polybutylene succinate- co-ter ephthalate (PBST), poly(butylene succinate-co-butylene terephthalate) (PBST) or polybutylene succinate-co-butylene terephthalate (PBST); poly(butylene adipate terephthalate) (PBAT), polybutylene adipate terephthalate (PBAT), poly(butylene adipate-co-terephthalate) (PBAT), polybutylene adipate-co-terephthalate (PBAT), poly(butylene adipate-co-butylene terephthalate) (PBAT) or polybutylene adipate-co-butylene terephthalate (PBAT); and polyhydroxy alkanoates (PHAs). 00134 As used herein, the term “additive” could refer to any material used to enhance a targeted property or function of material and / or composition, which could be in any form such as solid, liquid, powder, fiber, or crystal.

[0128] 00135 The term “polyhydroxyalkanoates (PHAs)” refers to a family of bio-based thermoplastic polyesters synthesized by various microorganisms, particularly through bacterial fermentation. The PHA family encompasses over 150 different monomers, allowing for the production of materials with a wide range of properties. Notably, these plastics are biodegradable and include, but are not limited to, poly-3 -hydroxybutyrate (PHB), polyhydroxybutyrate-co-hydroxy valerate (PHBV), poly-4-hydroxybutyrate (P4HB), polyhydroxybutyrate-co-hydroxyhexanoate (PHBH), polyhydroxyvalerate (PHV), polyhydroxyhexanoate (PHH), polyhydroxyoctanoate (PHO), polyhydroxy decanoate (PHD), and polyhydroxydodecanoate (PHDD).

[0129] 00136 As used herein, a person skilled in the relevant art will understand that the term “elastomer” could refer to any polymer with viscoelasticity (both viscosity and elasticity) and can undergo substantial elastic deformation under stress and return to its original shape when the stress is removed. Elastomers are commonly found in rubber materials and are characterized by their ability to stretch and recoil, making them ideal for applications requiring flexibility and resilience.

[0130] 00137 A person skilled in the art will understand that “non-physical” factors may include: (a) infrastructure requirements - composting and recycling infrastructure vary city to city sometimes, resulting in potential mismatch between materials and waste management systems, impacting uptake; (b) regulations - different geographies have different policies surrounding which materials are defined as compostable, and which ones are acceptable; (c) scalability and volume - due to reduced investment in the space, and its nascency, some emerging solutions do not. yet have the ability to scale to commercial volumes required to service the industry alone; (d) equipment compatibility - emerging technologies often require custom machinery and equipment to develop the material, resulting in a mismatch between the novel material, and industry infrastructure; (f) cost / cost to own: aforementioned obstacles, coupled with unoptimized inputs can lead to material costs that can sometimes be 3 - 5x the cost of traditional plastics; and (g) economic factors.

[0131] 00138 As used herein, a person skilled in the relevant art will understand that the term “economic factors” refers to non-physical factors that are involved in the production, development, marketing and sales of polymeric material. Such economic (e.g. non-physical) factors include, but are not limited to, (a) Raw Material Costs (e.g. prices of monomers and additives required for polymer synthesis); (b) Energy Costs (e.g. expenses related to the energy required for production processes such as polymerization, extrusion, and molding); (c) Labor Costs (e.g. wages and benefits for the workforce involved in production, quality control, and maintenance); (d) Capital Costs (e.g. investment in machinery, equipment, and infrastructure necessary for production facilities); (e) Research and Development (R&D) (e.g. expenditures on developing new polymers, improving existing ones, and optimizing production processes); (f) Regulatory Compliance (e.g. costs associated with meeting environmental, safety, and quality standards set by regulatory bodies); (g) Supply Chain Logistics (e.g. costs related to the procurement, transportation, and storage of raw materials and finished products); (h) Market Demand (e.g. economic viability influenced by the demand for polymeric materials in various industries such as automotive, packaging, and construction); (i) Competitive Landscape (e.g. pricing strategies, market share, and competitive pressures from other manufacturers); and (j) Technological Advancements (e.g. investment in new technologies that can improve efficiency, reduce costs, or enhance the properties of polymeric materials.)

[0132] 00139 As used herein, a person skilled in the relevant art will understand that the term “polymeric materials” refers to formulations, composite materials, polymeric materials, or materials that can contain one or all of the following: polymer, polymer blends, copolymers, additives, fillers, and / or composites, plasticizers, elastomers, compatibilizer.

[0133] 00140 As used herein, “wt.%,” “parts by mass (w / w)” or “parts by mass % (w / w)” refer to the percentage weight of an ingredient with respect to the total weight of a composition.

[0134] 00141 A person skilled in the relevant art will understand that the term “sample type” refers to the product applications that can be used in industry which can be either rigid or flexible materials.

[0135] 00142 A person skilled in the art will understand that the embodiments of the present invention may employ a number of “parameters”. The parameters upon which the predictions may be made include, but are not limited to, the following parameters: (a) economic factors influencing financial viability, such as cost, demand, margins, and profitability; (b) regulatory agencies regulating food, drugs, and consumer products for safety and efficacy, such as FDA, FCN, (e.g. a binary evaluation that measures whether a material or blend complies with the Food Contact Notification); (c) MFI (measure of a polymer's flowability when melted, indicating viscosity); (d) Tensile Strain @ Break (e.g. the strain a material undergoes before breaking under tensile stress); (e) Elongation @ Break (e.g. the percentage a material stretches before breaking); (f) additional material characteristics such as, but not limited to hardness, impact resistance, strength, etc.; (g) Cobb (e.g. test measuring the water absorption rate of paper or cardboard); (h) WVTR (e.g. rate at which water vapor passes through a material); (i) Oil & Grease Resistance (e.g. a material's ability to resist absorption or degradation from oils and grease); (j) Cast Extrusion (e.g. ; Process where heated material is formed into a sheet or film by passing through a flat die); (k) Home Compostability TUV / DIN (e.g. certification for materials that break down in home composting conditions); (1) Industrial Compostability TUV / DIN (e.g. certification for materials that break down in industrial composting systems); (m) Recyclability (e.g. ability of a material to be reused or processed after its initial use); (n) volume; (o) thickness; (p) Regional Policy & EPR Impact (e.g. effects of extended producer responsibility and environmental policies on waste management in different regions, such as North America, South America, MENA, Asia, EU, etc.); (o) LCA (e.g. evaluation of a product's environmental impact throughout its entire lifecycle); (v) Cast Extrusion Processing Guidelines (e.g. Recommendations for optimizing the cast extrusion process for desired product properties).

[0136] 00143 The term “glass transition temperature (Tg)” is herein referred to as the temperature at which the gradual transition in amorphous materials from a “hard” and “glassy state” to a “rubbery” state. The glass transition temperature (Tg) can represent the change in a materials molecular mobility and is often associated with thermal, mechanical and at times, compostability properties.

[0137] 00144 The term “melting temperature (Tm)”, also known as the melting point, is defined as the temperature at which a material changes from a solid to a liquid state. It is often associated with Tg properties, particularly in mechanical and thermal properties.

[0138] 00145 The term “molecular properties” refers herein to the physical characteristics or attributes of molecules that describe their structure, composition, or behavior. Specifically, within the scope of this invention, it pertains to properties such as molecular weight and thermal characteristics of materials.

[0139] 00146 In the context of this invention, the terms "functional groups" or "functional features" pertain to the specific arrangements of atoms within a molecule that dictate its chemical properties, particularly those influencing the glass transition temperature of materials. For instance, the innovative ML-algorithm developed for predicting the glass transition temperature of compostable materials takes into account various functional groups. These include, but are not limited to, methyl groups (-CH3), ethyl groups (-CH2), alkyl groups, esters (R-O-R), carboxylic acids (-C=O), hydroxyl groups (-OH), oxygen atoms (-O-), hydrogen atoms (-H), aromatic rings, aldehydes, ketals, esters, ethers, and others.

[0140] 00147 The term “feature engineering” is the process of selecting, transforming, or creating new features derived from raw data to enhance the predictive performance of machine-learning algorithms. It entails identifying pertinent information and converting it into a format that the algorithm can efficiently utilize for making predictions or classifications. In the context of this inventive ML-algorithm, feature engineering involves assessing molecular and functional group properties to predict the glass transition temperature accurately.

[0141] 00148 The preferred embodiments of the present invention incorporate a machine-learning (ML) powered algorithm aimed at supporting the efficient creation, development and production of biomaterials. By leveraging such ML techniques, the algorithm of the present invention addresses critical challenges within the biomaterials industry, particularly in streamlining the design and assessment of biomaterials by predicting various desired physical and non-physical properties. The further preferred embodiments of the present invention incorporate a machine-learning (ML) powered algorithm aimed at supporting the development of renewable biomaterials with compostable properties.

[0142] 00149 Embodiments of the present invention focus on the reliable and accurate prediction of the glass transition temperature (Tg), a feature and property when evaluating the likelihood of compostability, of polymers, blends, and composites with additives. In preferred embodiments, the prediction model of the present invention utilizes key features and properties to facilitate the prediction of Tg as an output, which is then applied to support the prediction and therefore, generation of compostable materials.

[0143] 00150 The ML-algorithm of the present invention utilizes molecular properties and functional groups as key features in building a regression model for the prediction of glass transition temperature. The embodiments of the present invention involve the application of a prediction algorithm for Tg, applied in the embodiments of the present invention (e.g. a compostability algorithm powered by an Al-platform for existing and emerging biomaterials) for the development of sustainable alternatives.

[0144] 00151 In preferred embodiments, by combining regression models and innovative feature engineering, the present invention identifies molecular descriptors and chemical features essential for predicting Tg and assessing compostability attributes. Furthermore, the adaptability of the present invention allows it to accommodate diverse biomaterial compositions, enhancing its versatility across various industries and offering a streamlined approach to sustainable material design and development.

[0145] 00152 In preferred embodiments, the core component of the invention is the machine-learning (ML) algorithm tailored for predicting the glass transition temperature (Tg) for compostable materials that are biobased and / or renewable. This algorithm serves as the foundation for streamlining the design and development of renewable biomaterials with compostability properties. It utilizes advanced regression models and ensemble learning techniques to analyze molecular features and chemical properties, enabling accurate predictions of Tg values.

[0146] 00153 In preferred embodiments, the ML-algorithm of the present invention utilizes features engineering for the predictive algorithms’ capabilities. In preferred embodiments, the algorithm selects, transforms, and creates new features derived from raw historical data, that is used to train and enhance the performance of predicting Tg.

[0147] 00154 In preferred embodiments, the feature engineering process in the inventive ML-model identifies molecular properties and functional groups that significantly correlate with the Tg properties. The processes utilize attributes such as, but not limited to, number of hydroxyl groups, ester groups, Tg / Tm ratio, amongst other features. 00155 In preferred embodiments, the inventive algorithm leverages historical data that encompasses material properties, functional groups, chemical compositions to train the regression machine learning models. In certain cases, these input parameters provide insights into the molecular structure and characteristics of materials that are composed of but not limited to polymers, organic and inorganic additives, filler materials, for neat, blends, and / or composite materials.

[0148] 00156 In preferred embodiments, the inventive algorithm utilizes the historical data along with the input parameters, which are, the molecular features and chemical properties, and composition (e.g. wt%) that structure in the regression model that can effectively evaluate, analyze, and predict Tg values based on the aforementioned input parameters.

[0149] 00157 In preferred embodiments, the ML-algorithm undergoes data preparation and processing, where historical data on material properties, chemical compositions, and functional groups is collected and structured into input parameters, with corresponding Tg / Tm as output.

[0150] 00158 In preferred embodiments, the data processing includes feature extraction techniques and data normalization processes to ensure accurate and consistent input data for predictive modeling.

[0151] 00159 In preferred embodiments, the ML-algorithm utilizes historical data on neat polymers that are biobased and or petroleum based, and corresponding material properties, chemical compositions, and functional groups as input parameters for Tg as an output.

[0152] 00160 In preferred embodiments, the ML-algorithm utilizes historical data on polymeric blends, that may include two or more polymeric materials, that are biobased and or petroleum derived, with corresponding material properties, chemical compositions, and functional groups as input parameters alongside weight percentage (wt%) for Tg as an output.

[0153] 00161 In preferred embodiments, the ML-algorithm of the present invention utilizes historical data on polymeric and composite materials, that may include two or more polymeric materials with additional additives (e.g. Organic, Inorganic Compounds) that are biobased, renewable, and or petroleum derived, with corresponding material properties, chemical compositions, and functional groups as input parameters alongside weight percentage (wt%) for Tg as an output. 00162 In preferred embodiments, the inventive ML-algorithm of the present invention leverages various ensemble regression models and employed within the algorithm of the present invention, that include, but not limited to XGBoost, Random Forest, Gradient Boosting, and other ensemble techniques

[0154] 00163 In preferred embodiments, these input parameters are structured into training and testing sets to train ensemble regression models such as XGBoost, Random Forest, and / or Gradient Boosting to predict output values of Tg, using trained input parameters.

[0155] 00164 In preferred embodiments, the following regression models in the inventive ML-algorithm of the present invention utilizes feature engineering techniques to extract molecular features of each input to enhance predictive performance of the models.

[0156] 00165 In preferred embodiments, the regression model utilizes training with molecular features such as functional groups, which are, but not limited to methyl groups (-CH3), ethyl groups (-CH2), alkyl groups, esters (R-O-R), carboxylic acids (-C=O), hydroxyl groups (-OH), oxygen atoms (- O-), hydrogen atoms (-H), aromatic rings, aldehydes, ketals, esters, ethers, and others.

[0157] 00166 In preferred embodiments, the regression model utilizes the molecular features and composition of materials, which can be, polymers, organic compounds, additive materials, inorganic compounds, and / or filler materials by terms of weight percentage (wt%).

[0158] 00167 In preferred embodiments, the regression model in the ML-algorithm of the present invention utilizes molecular properties such as molecular weight and or melting temperature (Tm) as input parameters for Tg prediction output.

[0159] 00168 In preferred embodiments, the ML-powered algorithm of the present invention predicts Tg using molecular properties and functional groups of materials for neat and blended polymeric materials using rule of mixtures of materials weight percentages (i.e. wt%) or commonly known methods but not limited to Goron-Taylor equation and / or Fox Equation.

[0160] 00169 In preferred embodiments, the ML-powered algorithm of the present invention predicts the Tg of neat polymeric materials by utilizing historical data sets used to train the models, in combination of ensemble regression models with individual material and functional group properties.

[0161] 00170 In preferred embodiments, the ML-powered algorithm of the present invention predicts the Tg of polymeric blends by utilizing historical data sets used to train the models, in combination of ensemble regression models with individual and material blends with corresponding functional group properties by wt%.

[0162] 00171 In preferred embodiments, the ML-powered algorithm of the present invention leverages the rule of mixtures for polymeric blend materials, utilizing individual material properties and functional groups with corresponding wt%.

[0163] 00172 In preferred embodiments, the Gordon-Taylor and or Fox Equation, which is used to estimate Tg of polymeric blends is applied within the ML-algorithm of the present invention to predict the Tg of polymeric blend materials. In preferred embodiments, in addition to the feature engineering within the ML-algorithm of the present invention, the wt% of each individual material (e.g.) in addition to the estimated or historical data for Tg output.

[0164] 00173 In preferred embodiments, the ML-algorithm of the present invention utilizes the chosen regression model of best performance to predict the Tg value of a neat, blend, or composite material. In preferred embodiments, the ML-algorithm of the present invention utilizes the chosen regression model, of best performance, following the chemical feature implementation, to predict the Tg value of a neat, blend, or composite material.

[0165] 00174 In preferred embodiments, the ML-algorithm of the present invention implements a material dictionary to consolidate material classification and or weight percentage (wt%) in order to extract features related to each individual input within the material dictionary.

[0166] 00175 In preferred embodiments, the ML-algorithm of the present invention implements the material dictionary in addition to input chemical features input parameters to collect and deliver Tg outputs.

[0167] 00176 In preferred embodiments, the ML-algorithm of the present invention deploys the regression models with feature implementation as a function that is applied in external models. 00177 In preferred embodiments, the ML-algorithm of the present invention deploys the regression models with feature implementation in addition to the material dictionary as a function that is applied in external models.

[0168] 00178 In preferred embodiments, the ML-algorithm of the present invention serves a function and features within the compostability model in the Al-platform for biobased, renewable, and petroleum based materials for compostability properties.

[0169] 00179 In preferred embodiments, the ML-algorithm of the present invention is built using historical data in addition to molecular properties (e.g. grades, molecular weight, and / or Tm) and functional groups (e.g. methyl, ethyl, esters, carboxylic acids, etc.) features to predict Tg across material types (e.g. Polymeric, blends, composites, etc) and can generate a Tg output for an individual and / or combination of materials by manually implementation of each corresponding features.

[0170] 00180 In preferred embodiments, the ML-algorithm of the present invention is versatile and applicable with material properties and features for new and unseen materials of various types. In preferred embodiments, the ML-algorithm of the present invention can predict Tg of new and unseen materials leveraging material properties and chemical features in combination with known materials from historical data.

[0171] 00181 In preferred embodiments, the Tg prediction model can be utilized to predict the Tg of materials that are biobased and / or compostable. In preferred embodiments, in the compostability model in the Al-platform, the Tg model is implemented as a feature to support the prediction and materials to predict compostability.

[0172] 00182 In preferred embodiments, the Tg prediction model utilizes the molecular properties (e.g. grades, molecular weight, and / or Tm) and functional groups (e.g. methyl, ethyl, esters, carboxylic acids, etc.) features to predict Tg across material types (e.g. Polymeric, blends, composites, etc, that are biobased and / or compostable. In preferred embodiments, in the compostability model in the Al-platform, the Tg model is implemented as a feature to support the prediction and materials to predict compostability, by using the molecular properties and functional groups. 00183 In preferred embodiments, the models are trained across historical datasets, composed of, but not limited to biobased and / or petroleum derived polymers, organic and inorganic additives, blends, and composites to train and learn the complex relationships and correlations between molecular features and Tg values. Therefore, in preferred embodiments, the algorithm of the present invention enhances the predictive nature of the compostability model, and the robustness, ensuring reliable assessments of Tg for compostable materials.

[0173] 00184 The machine-learning (ML) algorithm of the present invention empowers the prediction of a valuable thermal property, glass transition temperature (Tg), with a full suite of input parameters, modeling each molecular and chemical correlation with Tg as an output. This ML-algorithm of the present invention provides the ability to be deployed in the compostability algorithm of the present invention that possesses predictive and degenerative Al capabilities.

[0174] 00185 By leveraging this ML-algorithm of the present invention, users can meet the demanding industry requirements for renewable and biobased solutions that are compostable for existing and / or emerging materials. The compostability of materials are impacted by their inherent chemistry, which includes valuable properties such as glass transition temperature (Tg).

[0175] 00186 The predicting ML-algorithm of the present invention provides the ability to predict and / or generate Tg values of new and / or existing materials by using a growing database of materials (e.g. 85) that include but not limited to polymers, polymeric blends, polymer composites with additives, and composites containing polymers, organic, and / or inorganic additives, that is collected by open- source research articles and / or databases. Furthermore, the ML-algorithm of the present invention is capable of predicting Tg of new and / or existing materials by solely relying on material and chemical structure properties.

[0176] 00187 For example, the ML-algorithm of the present invention predicts the Tg of known materials (e.g. Polymeric blends, composites, etc.) using input values from dictionaries containing material classification(s) and weight percentages (wt%). Historical data on material properties and compositions are utilized to train the algorithm of the present invention. The ML-algorithm of the present invention access material dictionaries containing the aforementioned classifications and wt% of known materials. The input parameters are structured into the algorithm of the present invention which then utilizes an ensemble regression model to predict Tg values. 00188 Additionally, the ML-algorithm of the present invention seamlessly integrated into the compostability model, and delivering accurately predicted Tg values of the materials from the dictionary. This enables the users to predict the compostability of existing materials that are renewable, biobased, and / or petroleum derived. By combining Tg predictions with compostability assessments, stakeholders can make informed decisions regarding material selection for sustainable manufacturing practices.

[0177] 00189 For example, the ML-algorithm of the present invention predicts the glass transition temperature (Tg) for new or unseen materials by leveraging input values from dictionaries containing material classifications, weight percentages (wt%), and molecular structure features. Historical data on material properties and compositions are utilized to train the algorithm of the present invention, ensuring its ability to accurately predict Tg values for unfamiliar materials. By accessing material dictionaries with classifications, wt% information, and molecular structure features, the algorithm of the present invention incorporates these parameters into its prediction process. Utilizing an ensemble regression model, the algorithm of the present invention generates Tg predictions for novel materials based on their unique characteristics and properties.

[0178] 00190 Furthermore, the ML-algorithm of the present invention seamlessly integrates with a compostability Al-model, delivering the accurately predicted Tg values of the new or unseen materials from the dictionary. This integration enables users to assess the compostability of emerging materials that may be renewable, biobased, and / or petroleum derived. By combining Tg predictions with compostability assessments, stakeholders can confidently evaluate the suitability of these novel materials for sustainable manufacturing practices.

[0179] 00191 The embodiments of the present invention may be built on robust machine learning (ML) algorithms that empower generative Al capabilities, with a full suite of features, modeling each essential component in the development stage of a product / material life cycle. The preferred embodiments may provide features for users to meet demanding industry requirements, ranging from global warming potential (GWP) to product compostability, and / or material performance by evaluating compostability properties and / or biobased carbon content (BCC). Preferred embodiments include, but are not limited to, the capability to make predictions based on the chemical structure of materials and polymers in composite. Preferred embodiments include, but are not limited to, predicting the sustainability features of a polymer associated with a biobased product / material life cycle.

[0180] 00192 Leveraging generative Al & ML-prediction algorithms, the embodiments of the present invention provide users the solutions for predicting and / or designing and generating cutting-edge formulations, composites, and / or polymeric materials with desired physical and non-physical parameters, including but not limited to compostability / sustainability properties. The embodiments of the present invention may continuously grow and improve, with an existing and growing material database of a plurality of materials (e.g., 85) including bio-derived materials, biopolymers, additives, and fillers, with each material containing datasets for model training and testing, collected by open-source research articles and / or databases. One of the most important features in the embodiments of the present invention is the use of chemical structures as a model feature allowing the preferred embodiments to grow the number of materials as inputs. The chemical structures are accounted for as some functional groups in structure of polymers and additives influence the disintegration and biodegradation properties of the polymers more than other functional groups. As noted herein, the preferred embodiments work based on the chemical structures. Based on the selected materials (i.e., PLA, cellulose and glycerol as shown in FIG. 14) the numerical information are extracted from the structure of each component and are used to make model predictions accordingly. This model is used to make predictions for specific formulations and also to generate formulations based on a set of desired compostability and / or sustainability values for the composites.

[0181] 00193 The compostability and / or sustainability properties algorithm provides the ability to predict and / or generate relevant processing and performance properties for a wide range of biobased materials.

[0182] 00194 In a preferred embodiment of the present invention, there is provided: (a) matching ML algorithms to support supplier / buyer matchmaking based on polymer compatibility to input parameters; (b) one or more databases of ingredients available to support and refine polymer search and matching; (c) comparison modules to understand material performance in comparison to competitive legacy ingredients; (d) sustainability insights; (e) LLM Co-Pilot to support search refinement and allow users to sift through ingredient libraries for more ingredients, or produce ingredient insights; and (f) ML models to support ingredient blending and formulation optimization.

[0183] 00195 In a preferred embodiment of the present invention, there is provided: (a) ML predictors for key physical and non-physical properties of materials to a level suitable for online active learning (i.e. model improvement over time and increased user interaction); (b) leveraged historical laboratory data, automated web scrapers, cheminformatic models and active laboratory testing to combat data sparsity; (c) multiple commercialization pathways, including R&D acceleration and supply chain applications; (d) support users (e.g. buyers, sellers, manufacturers and developers) of sustainable materials with search, ranking, and recommendation systems; (e) a cloud architecture which allows for cloud based hosting and to facilitate the movement and transformation of data.

[0184] 00196 The preferred embodiment of the present invention uses various user-inputs to predict relevant or desired end product parameters or properties. In addition to these features, the innovative component of this algorithm is the development of generative Al functionality, which can leverage existing internal and / or external physical and non-physical parameter data to generate formulations that meet predetermined criteria.

[0185] 00197 This model is used to generate the production and end-of-life properties for any formulation containing materials of a dataset with various materials. The generated values are used to decide whether or not the formulation has the required production or end-of-life properties. For example, for a formulation containing PLA, glycerol and calcium carbonate, the model can predict the disintegration and biodegradation values to be 95% and 93% after 90 days and 180 days, respectively. These predicted values could be used to evaluate whether this composite would meet the industrial standards criteria associated with disintegration and biodegradation values. The algorithm is also able to suggest formulations that corresponds to a certain end-of-life properties. Important functional groups in different polymers, plasticizers, and other additives may be used in chemical structure modeling.

[0186] 00198 The use of this new chemical structure modeling approach streamlines the exploration in the realm of unknown materials in terms of compostability and / or sustainability by just having their structural information. This can also help us to navigate new copolymers, polymers, and additives including plasticizers, elastomers, plasticizers and reinforcing materials.

[0187] 00199 In certain embodiments, data processing modules within the system are designed to efficiently gather, store and organize extensive datasets related to biomaterials. This includes data on material composition, manufacturing process, environmental factors, economic factors and relevant compostability and / or sustainability properties.

[0188] 00200 In certain embodiments, predictive modeling components harness advanced algorithms and artificial intelligence techniques to analyze the collected data. Through machine learning, these components predict and optimize the compostability and / or sustainability and chemical properties of sustainable and unsustainable materials including, but not limited to, biodegradation, disintegration, biobased carbon content and global warming potential metrics based on the associated standards.

[0189] 00201 In certain embodiments, the formulation recommendation / generation modules leverage the insights generated by the predictive modeling components and various optimization techniques to provide tailored recommendations for material formulations and compositions.

[0190] 00202 In certain embodiments, the recommendations aid in the development of biomaterials with improved / desired compostability and / or sustainability properties, contributing to enhanced sustainability.

[0191] 00203 In certain embodiments, the system has various applications for the biomaterials. In certain embodiments, the specific application of this system is for enhancing sustainable materials. Compostable materials, designed for a reduced environmental footprint and responsible resource management, play a pivotal role in sustainable practices (e.g. sustainability) across a variety of industries. In certain embodiments, the system capabilities are particularly suited for the development of materials that promote eco-friendly alternatives and contribute to a more environmentally conscious future.

[0192] 00204 In certain embodiments, the system is versatile and applicable to a wide range of biomaterials including, but not limited to, plastics, textiles, and packaging materials. In certain embodiments, the invention accommodates various material compositions, allowing for a comprehensive enhancement of compostability and / or sustainability features across different industries.

[0193] 00205 In certain embodiments, the primary focus of the system is on compostable materials, aligning with the growing demand for environmentally friendly alternatives. In certain embodiments, compostable materials are pivotal in addressing the issues of waste management and reducing the environmental impact associated with conventional materials.

[0194] 00206 In certain embodiments, the data processing modules include feature extraction techniques and data normalization processes to ensure accurate and consistent input data for predictive modeling.

[0195] 00207 In certain embodiments, the data processing modules are equipped with feature extraction techniques. These techniques intelligently identify and extract pertinent information from raw data. In certain embodiments, spatial, temporal, and frequency domain features are considered, providing a holistic representation of the underlying patterns in the data. State-of-the-art algorithms are employed to selectively filter and highlight key features, enhancing the efficiency of predictive modeling.

[0196] 00208 In certain embodiments, the system data processing modules go beyond basic standardization and encompass advanced data normalization processes. Data normalization ensures that inputs are consistently represented across different scales, units, and formats. Variations in the original dataset are addressed, minimizing discrepancies and facilitating the accurate interpretation of data. The normalization processes contribute to the stability and reliability of predictive modeling outcomes.

[0197] 00209 In certain embodiments, the system’s data processing modules go beyond basic standardization and encompass advanced data normalization processes. Data normalization ensures that inputs are represented across different scales, units, and formats. Variations in the original dataset are addressed, minimizing discrepancies and facilitating the accurate interpretation of data. In certain embodiments, the normalization processes contribute to the stability and reliability of predictive modeling outcomes. 00210 In certain embodiments, the data processing modules feature adaptive algorithms that dynamically adjust to the characteristics of the input data. These algorithms autonomously adapt their parameters based on identified features during the preprocessing phase. The adaptability ensures the system's efficacy in handling diverse datasets and evolving data patterns over time.

[0198] 00211 In certain embodiments, the predictive modeling components may utilize machine learning models, including Support Vector Machines, Artificial Neural Networks, Linear Regression, Random Forest Regression and XGBoost, to predict properties. Each model brings unique strengths, collectively enhancing the system's ability to predict the properties of materials across a range of complexities and scenarios.

[0199] 00212 In certain embodiments, the system integrates XGBoost, an optimized gradient boosting algorithm, to enhance the efficiency and accuracy of predictive modeling. XGBoost' s iterative approach improves predictive performance, making it particularly effective in optimizing the system's ability to predict and optimize diverse compostability and / or sustainability properties in bio-based materials.

[0200] 00213 In certain embodiments, the formulation recommendation modules are designed to offer customizable guidance for sustainable material compositions. This involves a refined approach with specific emphasis on following key aspects.

[0201] 00214 In certain embodiments, the formulation recommendation modules are adept at interpreting user-defined input parameters, providing a personalized aspect to the recommendation process. The nature of these input parameters remains flexible, accommodating various user preferences and requirements in a manner that aligns with the user's distinct needs.

[0202] 00215 In certain embodiments, the modules tailor recommendations based on the user's articulated desired properties for compostable materials. The specifics of these properties are left intentionally vague, allowing users to define and refine their requirements according to the unique goals and considerations of their applications.

[0203] 00216 In certain embodiments, customizable recommendations encompass a broad range of compositional possibilities for compostable materials, providing users with ample room for exploration and adaptation. 00217 In certain embodiments, the level of customization remains adaptable, enabling users to navigate and refine the recommendations based on evolving project requirements or unforeseen variables.

[0204] 00218 In certain embodiments, the formulation recommendation modules exhibit flexibility in proposing material compositions, ensuring that the recommendations align with the dynamic and evolving landscape of sustainable material development. The system's adaptability enables users to explore a spectrum of possibilities, fostering an environment of continuous improvement and innovation in compostable material formulations.

[0205] 00219 In certain embodiments, a method is disclosed for predicting compostability and / or sustainability properties of materials, providing a flexible framework. The method involves receiving user-defined input parameters related to material properties and processes, processing data, retrieving relevant information from a database, and utilizing machine learning models for prediction.

[0206] 00220 In certain embodiments, a method is disclosed for formulation recommendation or generation, which involves receiving user-defined desired compostability and / or sustainability properties and identifying parameters for optimization, processing and retrieving information from the database, and employing optimization algorithms / search algorithms to adjust parameters and provide the recommended / generated formulation.

[0207] 00221 In various embodiments, the invention encompasses a computer implemented or computer assisted method of generating of formulations comprising one or more components including, but not limited to, one or more sustainable and or compostable polymers including one or more computation steps based on, for example, at least one compostability and / or sustainability property computation or prediction or as part of an optimization method employed on a computer environment or system using for example artificial intelligence or machine learning algorithms. In certain embodiments, optimization methods use typically iterative processes and hence the compute steps are executed iteratively. Computation can be performed by computing a given deterministic or by compostability and / or sustainability property inspired formula, selected to be sufficiently accurate while still being tractable or by use of a training model, representative for a well selected data set using techniques from machine learning for those situations where suitable formulae do not exist.

[0208] 00222 In various embodiments, the invention is useful in the design of compostable, and / or sustainable formulations, including one or more components that can together act as a compostable, and / or sustainable material.

[0209] 00223 In a first embodiment, the invention encompasses a computation of one or more compostability and / or sustainability properties using a well selected approach. In various embodiments, the invention encompasses one or more compostability and / or sustainability properties, in particular compostability and / or sustainability properties related to biodegradation, disintegration, biobased carbon content and global warming potential metrics based on the associated standards, and for example a class of compostability and / or sustainability properties. Moreover in an embodiment of the invention, it is emphasized that for the class of compostability and / or sustainability properties (and for each of such property separate) a polymer based formula is an appropriate approach and in other embodiments for the class of compostability and / or sustainability properties a training model based approach (based on existing data) is the preferred approach.

[0210] 00224 In another embodiment, the invention encompasses a computer implemented approach for identification of one or more formulations or materials satisfying the requirements on the performance of the formulation or material in its use as a compostable and / or sustainable material. Therefore, the invention encompasses methods for generating or defining formulations or structures, which may together be suitable for the task designed for.

[0211] 00225 In another embodiment, the invention encompasses methods to optimize the compostability properties of the formulation for (1) compostability and / or sustainability, and (2) sustainability taking into the amount of time and environmental conditions to optimize each considering the optimal components with compostability and / or sustainability properties that allow the formulation to achieve one or more of these properties.

[0212] 00226 In various embodiments, the invention encompasses methods that take into account a plurality of compostability and / or sustainability properties of various kinds, hence the optimization methods are multi-objective optimization methods. It is known that a compostability and / or sustainability property or objective can either become part of the objective function used or may appear as a constraint to be satisfied by the solution of the method or even both.

[0213] 00227 In certain embodiments, the invention encompasses methods for identifying and optimizing a formulation specifically properties of the formulation including compostability and / or sustainability, and sustainability properties using the Al and machine learning applications described herein. In certain embodiments, the method can be described as a method for computing optimized compostability and / or sustainability properties of a compostable, and sustainable material, comprising the steps of inputting, or loading (in the computer system storage module and memory module) the information on the formulation or compostability and / or sustainability properties thereof and, computing said compostability and / or sustainability properties of a material based on said inputted composition. As such methods are adapted for use in global optimization methods applied for generating or defining materials, their use is to explore (all) the chemical composition combinatorial possibilities, denoted chemical space, and an appropriate modelling format and a computational discovery process to facilitate this. The same consideration can be made for the optimization methods themselves that are properly selected, fine-tuned or redesigned for the purpose of identifying materials using the inventive computational discovery process. Preferably, in a large combinatorial search space, the method is implemented in a way that the user can restrict the search space by selecting subsets of materials based on criteria such as the compostability and / or sustainability properties optimal for a compostable, and sustainable material.

[0214] 00228 In relation to one embodiment of the invention, an appropriate modelling format for computational materials discovery is related to evaluating physical and compostability and / or sustainability properties (for instance modelled as an alphanumeric string of characters as input data, based on compostability and / or sustainability properties) that differs from the appropriate modelling format for evaluating compostability and / or sustainability properties (which are a geometric type of input).

[0215] 00229 In another embodiment of the invention, the method comprises the steps of: (1) inputting initial data which comprise a set of chemical elements under chemical system constraints (such as further defined in the text); (2) initialization, for example, declaring variables of the data set and computer system, being the storage and the memory module of the used computer system.

[0216] 00230 In another embodiment, when one wants to identify a new compostable, and sustainable material, the user can assist in selecting in the composition, chemical or search space to point the computer computational power thereto. For instance, the user can select groups / clusters of chemical elements based on the field of interest or the technological relevance and restrict the number of elements to a sizeable set.

[0217] 00231 As an exemplary embodiment of a suitable modelling format, a chemical descriptor can be computed based on the chemical element most fundamental variables of interest, such as compostability and / or sustainability properties including, but are not limited to, biodegradation, disintegration, biobased carbon content and global warming potential metrics based on the associated standards.

[0218] 00232 Preferably, the chemical descriptor is computed in order to be reduced to a string of alphanumeric characters. The reduction of the range of variables into one string of character is computed by the mean of the most appropriate method for the case of interest. The case of interest may be of the form of digitally encoded variables, suitable for algorithms such as components used in a compostable and / or sustainable material model.

[0219] 00233 Here it is to be noted that the descriptor value assigned to a chemical component of a compostable and / or sustainable material does not entail any preferred computational method as it may apply to evolutionary computing and machine learning.

[0220] 00234 In an embodiment of the invention, in relation to compostability and / or sustainability properties and the underlying space to be explored, the method for computing one or more compostability and / or sustainability properties of a material, based on information about the components of a formulation, also taking into account the inputted chemical information, is possible in the same way or similar way as described above, but now on the level of a microstructure. It is worth emphasizing that while the invention acknowledges that compostability and / or sustainability properties can be determined by geometric information, the starting method input, for instance via the chemical descriptor, belongs to the chemical domain and does not entail or require any preferred geometric domain or information.

[0221] 00235 For the purpose of computing said one or more structural properties of a material, based on information about the chemical composition the following microscopic building blocks are of primary importance: including one or more of chemical features, molecular features, number, weight percentage and or types of functional groups, and or atomic ratios.

[0222] 00236 In another embodiment, the invention encompasses a method of predicting the compostability and / or sustainability properties of a compostable, and / or sustainable material, the method comprises the steps of: (1) inputting initial data which comprises a set of chemical compositions; (2) initialization of the data set and computer system being the storage module and the memory module of the used computer system; (3) computing compostability and / or sustainability properties based on chemical information.

[0223] 00237 In another, embodiment, the invention encompasses a method to predict the compostability and / or sustainability features of a composite based on the chemical structure of the ingredients.

[0224] 00238 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify and / or rank physical and non-physical parameters, including but not limited to compostability / sustainability properties of a polymer or other chemical substance or combination thereof that provide a compostable, and sustainable material to a statistically significant degree, comprising an active method step of: training a first machine learning model with a plurality of computer-implemented models that model the targeted compostability and / or sustainability properties of the compostable, and sustainable material using user defined parameters, and which define prior probabilities in the models' parameters and models' marginal likelihood. The examples of training such a machine learning model can be found in the Examples section presented below. In a particular embodiment, the parameters of the plurality of computer-implemented models have user defined prior probabilities and marginal likelihoods. The computer-implemented models may be mathematical models, models that predict polymer or chemical structures, or some combination thereof. 00239 In a particular embodiment, computer-implemented models comprise models that predict compostability and / or sustainability properties of a biobased material.

[0225] 00240 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify and / or predict the compostability and / or sustainability properties of a material particularly a bio-based material to a statistically significant degree, which comprises an active method step of: training a second machine learning model to estimate the mutual information between observed data and computer-implemented models' parameters, to design experiments to optimally identify bio-based materials.

[0226] 00241 Mutual information (MI) is a ubiquitous measure of dependency between a pair of random variables and is one of the comer stones of information theory. Experiments are designed to identify materials and their compostability and / or sustainability properties that are identified as being most probable to be a compostable, and sustainable material based upon the output of the machine learning model.

[0227] 00242 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify compostability and / or sustainability properties of a material and / or rank materials that function as a compostable, and sustainable material to a statistically significant degree, which comprises an active method step of: combining materials identified using a machine learning model to generate measurable or observable compostability and / or sustainability properties related to a biodegradable, compostable, and sustainable material designed from the plurality of computer- implemented models' prior probabilities of a material or combination thereof to act as compostable, and sustainable material model.

[0228] 00243 Another embodiment encompasses a method that utilizes computer-implemented models and data from experiments in machine learning models to identify and / or rank one or more compostable, and sustainable materials to a statistically significant degree, which comprises an active method step of: retraining a machine learning model using the measured or observed compostability and / or sustainability property including, but not limited to, a typical set of compostability and / or sustainability properties include, but are not limited to, biodegradation, disintegration, biobased carbon content and global warming potential metrics based on the associated standards and performing one or more iterations of the machine learning model until a polymeric formulation is identified with the desired properties.

[0229] 00244 In a particular embodiment, the methods of the disclosure can identify and / or rank materials that possess compostability and / or sustainability properties of a material to a statistically significant degree. The material could be known chemical entities or novel chemical entities. With regards to the former, the methods of disclosure can identify known chemical entities that can be used as a biodegradable material or be combined with other chemical entities to have an improved effect, and / or be used at amounts that are not normally utilized. The methods of the disclosure can also be used to identify new chemical entities based upon the machine learning modeling data and permutations made thereof.

[0230] 00245 It will be understood that the present system includes processor interconnecting a memory and a communications interface. The processor can include a central-processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a microprocessor, a processing core, a field- programmable gate array (FPGA), or similar. In some embodiments, the processor can include multiple cooperating processors. The processor can cooperate with non-transitory computer readable medium, such as the memory to execute instructions to realize the functionality discussed herein.

[0231] 00246 For the methods disclosed herein any of the steps that require computation (e.g., machine learning steps), these steps can be performed using the CPU and / or GPU of a computer or server or performed using an Al accelerator of a server. In a particular embodiment, the machine learning steps are carried out using a GPU of a computer. In another embodiment, the machine learning steps are carried out using an Al accelerator from a cloud-based server or web service.

[0232] 00247 Memory can include a combination of volatile memory (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. non-volatile random-access memory, read only memory or ROM, Electrically Erasable Programmable Read Only memory or EEPROM, flash memory). All or some of the memory can be integrated with the processer. Memory stores computer reasonable instructions for execution by the processor. 00248 It will now be apparent that each element of memory can be carried out by the processor executing operations. In other words, functionality described below as being carried out by a module of memory or a module of the generative or predictive engine can be based on any known server environment.

[0233] 00249 In some embodiments, memory stores a plurality of computer-readable data and programming instructions, accessible by processor, in the form of software objects, such as various applications, queries or types of data for use during the execution of those applications. In particular, the execution of the instructions in memory by processor queries or requests data from property databases and receives said data via communications interface. In addition, the execution of the instructions in memory also determines if any data points are missing or incomplete from the retrieved data, compensates for the missing or incomplete data and analyzes the data. Furthermore, the execution of the instructions in memory by processor provides the resulting analyzed results in a graphical user interface for a user to view and make decisions upon. The person skilled in the art will now recognize the various forms of computer-readable programming instructions stored in memory can be executed by processor as applications or queries.

[0234] 00250 In a preferred embodiment, data processing modules within the system are designed to efficiently gather, store and organize extensive datasets related to sustainable materials. This includes data on material composition, grades, molecular weights, chemical structure, application, manufacturing process, environmental factors and relevant mechanical properties.

[0235] 00251 In a preferred embodiment, predictive modeling components harness advanced algorithms and artificial intelligence techniques to analyze the collected data. Through machine learning, these components predict and optimize the mechanical and chemical properties of sustainable and unsustainable materials including, but not limited to, tensile strength, elongation at break, Young’s modulus for various applications.

[0236] 00252 In a preferred embodiment, the formulation recommendation / generation modules leverage the insights generated by the predictive modeling components and various optimization techniques to provide tailored recommendations for material formulations and compositions. 00253 In a preferred embodiment, the recommendations aid in the development of compostable materials with improved / desired mechanical properties, contributing to enhanced sustainability.

[0237] 00254 In a preferred embodiment, the system has various applications for all the materials. In a preferred embodiment, the specific application of this system is for enhancing sustainable materials. Compostable materials, designed for a reduced environmental footprint and responsible resource management, play a pivotal role in sustainable practices across a variety of industries. In a more preferred embodiments, the system capabilities are particularly suited for the development of materials that promote eco-friendly alternatives and contribute to a more environmentally conscious future.

[0238] 00255 In a preferred embodiment, the system is versatile and applicable to a wide range of materials including, but not limited to, plastics, textiles, and packaging materials with use cases of rigid and flexible. In a preferred embodiment, the invention accommodates various material compositions, allowing for a comprehensive enhancement of sustainability features across different industries.

[0239] 00256 In a preferred embodiment, the primary focus of the system is on compostable materials, aligning with the growing demand for environmentally friendly alternatives. In a preferred embodiment, compostable materials are pivotal in addressing the issues of waste management and reducing the environmental impact associated with conventional materials.

[0240] 00257 In a preferred embodiment, the data processing modules are equipped with feature extraction techniques. These techniques intelligently identify and extract pertinent information from raw data. In a preferred embodiment, spatial, temporal, and frequency domain features are considered, providing a holistic representation of the underlying patterns in the data. State-of-the- art algorithms are employed to selectively filter and highlight key features, enhancing the efficiency of predictive modeling.

[0241] 00258 In a preferred embodiment, the system data processing modules go beyond basic standardization and encompass advanced data normalization processes. Data normalization ensures that inputs are consistently represented across different scales, units, and formats. Variations in the original dataset are addressed, minimizing discrepancies and facilitating the accurate interpretation of data. The normalization processes contribute to the stability and reliability of predictive modeling outcomes.

[0242] 00259 In a preferred embodiment, the system’s data processing modules go beyond basic standardization and encompass advanced data normalization processes. Data normalization ensures that inputs are represented across different scales, units, and formats. Variations in the original dataset are addressed, minimizing discrepancies and facilitating the accurate interpretation of data. In a preferred embodiment, the normalization processes contribute to the stability and reliability of predictive modeling outcomes.

[0243] 00260 In a preferred embodiment, the data processing modules feature adaptive algorithms that dynamically adjust to the characteristics of the input data. These algorithms autonomously adapt their parameters based on identified features during the preprocessing phase. The adaptability ensures the system’s efficacy in handling diverse datasets and evolving data patterns over time.

[0244] 00261 In a preferred embodiment, the predictive modeling components utilize machine learning models, including Support Vector Machines, Artificial Neural Networks, Linear Regression, k- nearest neighbors (KNN), Random Forest Regression and XGBoost, to predict properties. Each model brings unique strengths, collectively enhancing the system’s ability to predict the properties of characteristics, properties, qualities, etc. across a range of complexities and scenarios including but not limited to rigid and flexible products.

[0245] 00262 In a preferred embodiment, SVM models are integrated into the predictive modeling components to effectively classify and analyze data related to compostable materials’ mechanical properties. The utilization of SVM enhances the system’s ability to discern intricate patterns and relationships within diverse datasets, contributing to precise predictions and optimizations.

[0246] 00263 In a preferred embodiment, models based ensemble modules may also be applied. A person skilled in the relevant art will understand that ensemble learning may be applied in the present invention. Ensemble modules or methods employ a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models. 00264 In a preferred embodiment, the system harnesses the power of ANN models, mimicking the human brain’s learning processes for accurate predictions and optimizations. ANN models excel in capturing complex patterns, making them particularly suited for understanding and optimizing the multifaceted mechanical properties of compostable materials.

[0247] 00265 In a preferred embodiment, linear regression models are strategically employed to interpret linear relationships within the data pertaining to compostable materials. While foundational, the inclusion of linear regression ensures the system’s versatility in addressing scenarios where linear correlations play a significant role in predicting and optimizing mechanical properties.

[0248] 00266 In a preferred embodiment, system incorporates Random Forest Regression models, leveraging ensemble learning to handle diverse and large datasets effectively. Random Forest models excel in capturing complex interactions within the data, ensuring robust predictions and optimizations for the mechanical properties of compostable materials.

[0249] 00267 In a preferred embodiment, the system integrates XGBoost, an optimized gradient boosting algorithm, to enhance the efficiency and accuracy of predictive modeling. XGBoost’ s iterative approach improves predictive performance, making it particularly effective in optimizing the system’s ability to predict and optimize diverse mechanical properties in compostable materials.

[0250] 00268 In specific configurations, the system integrates k-nearest neighbors (KNN) to enhance both the efficiency and accuracy of predictive modeling. Leveraging its iterative nature, k-nearest neighbors (KNN) significantly improves predictive performance, making it particularly effective in optimizing the system’s ability to predict and fine-tune various mechanical properties in compostable materials.

[0251] 00269 In a preferred embodiment, the formulation recommendation modules are designed to offer customizable guidance for sustainable material compositions that meet the desired mechanical properties based on the material applications (e.g. rigid and flexible). This involves a refined approach with specific emphasis on following key aspects.

[0252] 00270 In a preferred embodiment, the formulation recommendation modules are adept at interpreting user-defined input parameters, providing a personalized aspect to the recommendation process. The nature of these input parameters remains flexible, accommodating various user preferences and requirements in a manner that aligns with the user’s distinct needs.

[0253] 00271 In a preferred embodiment, the modules tailor recommendations based on the user’s articulated desired properties for compostable materials. The specifics of these properties are left intentionally vague, allowing users to define and refine their requirements according to the unique goals and considerations of their applications.

[0254] 00272 In a preferred embodiment, customizable recommendations encompass a broad range of compositional possibilities for compostable materials, providing users with ample room for exploration and adaptation.

[0255] 00273 In a preferred embodiment, the level of customization remains adaptable, enabling users to navigate and refine the recommendations based on evolving project requirements or unforeseen variables.

[0256] 00274 In a preferred embodiment, the formulation recommendation modules exhibit flexibility in proposing material compositions, ensuring that the recommendations align with the dynamic and evolving landscape of sustainable material development. The system’s adaptability enables users to explore a spectrum of possibilities, fostering an environment of continuous improvement and innovation in compostable material formulations.

[0257] 00275 In a preferred embodiment, a method is disclosed for formulation recommendation or generation, which involves receiving user-defined desired mechanical properties and identifying parameters for optimization, processing and retrieving information from the database, and employing optimization algorithms to adjust parameters and provide the recommended / generated formulation.

[0258] 00276 In various embodiments, the invention encompasses a computer implemented or computer assisted method of generating of formulations comprising one or more components including, but not limited to, one or more biodegradable, compostable, and / or sustainable polymers including one or more computation steps based on, for example, at least one mechanical property computation or prediction or as part of an optimization method employed on a computer environment or system using for example artificial intelligence or machine learning algorithms. In a preferred embodiment, optimization methods use typically iterative processes and hence the compute steps are executed iteratively. Computation can be performed by computing a given deterministic or by mechanical property inspired formula, selected to be sufficiently accurate while still being tractable or by use of a training model, representative for a well selected data set using techniques from machine learning for those situations where suitable formulae do not exist.

[0259] 00277 In various embodiments, the invention is useful in the design of biodegradable, compostable, and / or sustainable formulations, including one or more components that can together act as a biodegradable, compostable, and / or sustainable material.

[0260] 00278 In a first embodiment, the invention encompasses a computation of one or more mechanical properties using a well selected approach. In various embodiments, the invention encompasses one or more mechanical properties, in particular mechanical properties related to biodegradable, compostable, and / or sustainable materials, and for example a class of mechanical properties. Moreover in an embodiment of the invention, it is emphasized that for the class of mechanical properties (and for each of such property separate) a polymer based formula is an appropriate approach and in other embodiments for the class of mechanical properties a training model based approach (based on existing data) is the preferred approach.

[0261] 00279 In another embodiment, the invention encompasses a computer implemented approach for identification of one or more formulations or materials satisfying the requirements on the performance of the formulation or material in its use as a biodegradable, compostable and / or sustainable material. Therefore, the invention encompasses methods for generating or defining formulations or structures, which may together be suitable for the task designed for.

[0262] 00280 In another embodiment, the invention encompasses methods to optimize the mechanical properties of the formulation for (1) biodegradation, (2) compostability, and (3) sustainability taking into the amount of time and environmental conditions to optimize each considering the optimal components with mechanical properties that allow the formulation to achieve one or more of these properties.

[0263] 00281 In various embodiments, the invention encompasses methods that take into account a plurality of mechanical properties of various kinds, hence the optimization methods are multi- objective optimization methods. It is known that a mechanical property or objective can either become part of the objective function used or may appear as a constraint to be satisfied by the solution of the method or even both.

[0264] 00282 In a preferred embodiment, the invention encompasses methods for identifying and optimizing a formulation specifically properties of the formulation including, but not limited to, biodegradation, compostability, and / or sustainability properties using the Al and machine learning applications described herein. In a preferred embodiment, the method can be described as a method for computing optimized properties of a biomaterial, and sustainable material, comprising the steps of inputting, or loading (in the computer system storage module and memory module) the information on the formulation or mechanical properties thereof and, computing said mechanical properties of a material based on said inputted composition. As such methods are adapted for use in global optimization methods applied for generating or defining materials, their use is to explore (all) the chemical composition combinatorial possibilities, denoted chemical space, and an appropriate modelling format and a computational discovery process to facilitate this. The same consideration can be made for the optimization methods themselves that are properly selected, fine-tuned or redesigned for the purpose of identifying materials using the inventive computational discovery process. Preferably, in a large combinatorial search space, the method is implemented in a way that the user can restrict the search space by selecting subsets of materials based on criteria such as the mechanical properties optimal for a biodegradable, compostable, and sustainable material.

[0265] 00283 In relation to one embodiment of the invention, an appropriate modelling format for computational materials discovery is related to evaluating physical and mechanical properties (for instance modelled as an alphanumeric string of characters as input data, based on mechanical properties) that differs from the appropriate modelling format for evaluating mechanical properties (which are a geometric type of input).

[0266] 00284 In another embodiment of the invention, the method comprises the steps of: (1) inputting initial data which comprise a set of chemical elements under chemical system constraints (such as further defined in the text); (2) initialization, for example, declaring variables of the data set and computer system, being the storage and the memory module of the used computer system. 00285 In another embodiment, when one wants to identify a new biodegradable, compostable, and sustainable material, the user can assist in selecting in the composition, chemical or search space to point the computer computational power thereto. For instance, the user can select groups / clusters of chemical elements based on the field of interest or the technological relevance and restrict the number of elements to a sizeable set.

[0267] 00286 As an exemplary embodiment of a suitable modelling format, a chemical descriptor can be computed based on the chemical element most fundamental variables of interest, such as mechanical properties including, but are not limited to, strength, Young’s modulus (MPa), tensile strength (MPa), ultimate tensile strength, specific strength, Yield strength, dynamic strength, creep strength, torsion strength, fatigue strength, maximum flexural stress (MPa), stress, strain, stressstrain, stress at yield (MPa), specific modulus (MPa), flexural modulus (MPa), 1% secant flexural modulus (MPa), elongation at yield (MPa), elongation at break (%), impact energy (J / m), impact Strength (kJ7m2), hardness at 25°C (Shore D), tear strength (kN / m), tear work (kJ / m2),MFI @ 190 oC (g / 10 min), glass transition temperature (oC), melting temperature (oC), crystallization temperature (oC), and HDT (oC).

[0268] 00287 Preferably, the chemical descriptor is computed in order to be reduced to a string of alphanumeric characters. The reduction of the range of variables into one string of character is computed by the mean of the most appropriate method for the case of interest. The case of interest may be of the form of digitally encoded variables, suitable for algorithms such as components used in a biodegradable, compostable, and sustainable material model.

[0269] 00288 Here it is to be noted that the descriptor value assigned to a chemical component of a biodegradable, compostable, and sustainable material does not entail any preferred computational method as it may apply to evolutionary computing and machine learning.

[0270] 00289 In an embodiment of the invention, in relation to mechanical properties and the underlying space to be explored, the method for computing one or more mechanical properties of a material, based on information about the components of a formulation, also taking into account the inputted chemical information, is possible in the same way or similar way as described above, but now on the level of a microstructure. It is worth emphasizing that while the invention acknowledges that mechanical properties can be determined by geometric information, the starting method input, for instance via the chemical descriptor, belongs to the chemical domain and does not entail or require any preferred geometric domain or information.

[0271] 00290 For the purpose of computing said one or more structural properties of a material, based on information about the chemical composition the following microscopic building blocks are of primary importance: including one or more of chemical features, molecular features, number and or types of functional groups, and or atomic ratios.

[0272] 00291 In another embodiment, the invention encompasses a method of predicting the mechanical properties of a biodegradable compostable, and / or sustainable material, the method comprises the steps of: (1) inputting initial data which comprises a set of chemical compositions; (2) initialization of the data set and computer system being the storage module and the memory module of the used computer system; (3) computing mechanical properties based on chemical information.

[0273] 00292 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify and / or rank mechanical properties of a polymer or other chemical substance or combination thereof that provide a biodegradable, compostable, and / or sustainable material to a statistically significant degree, comprising an active method step of: training a first machine learning model with a plurality of computer-implemented models that model the targeted mechanical properties of the biodegradable, compostable, and sustainable material using user defined parameters, and which define prior probabilities in the models’ parameters and models’ marginal likelihood. The examples of training such a machine learning model can be found in the Examples section presented below. In a particular embodiment, the parameters of the plurality of computer- implemented models have user defined prior probabilities and marginal likelihoods. The computer- implemented models may be mathematical models, models that predict polymer or chemical structures, or some combination thereof.

[0274] 00293 In a particular embodiment, computer-implemented models comprise models that predict mechanical properties of a biodegradable, compostable, and sustainable material.

[0275] 00294 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify and / or predict the mechanical properties of a material particularly a biodegradable, compostable, and sustainable material to a statistically significant degree, which comprises an active method step of: training a second machine learning model to estimate the mutual information between observed data and computer-implemented models’ parameters, to design experiments to optimally identify biodegradable, compostable, and sustainable materials.

[0276] 00295 Mutual information (MI) is a ubiquitous measure of dependency between a pair of random variables and is one of the comer stones of information theory. Experiments are designed to identify materials and their mechanical properties that are identified as being most probable to be a biodegradable, compostable, and sustainable material based upon the output of the machine learning model.

[0277] 00296 In another embodiment, the invention encompasses a method that utilizes computer- implemented models and data from experiments in machine learning models to identify mechanical properties of a material and / or rank materials that function as a biodegradable, compostable, and sustainable material to a statistically significant degree, which comprises an active method step of: combining materials identified using a machine learning model to generate measurable or observable mechanical properties related to a biodegradable, compostable, and sustainable material designed from the plurality of computer-implemented models’ prior probabilities of a material or combination thereof to act as biodegradable, compostable, and sustainable material model.

[0278] 00297 Another embodiment encompasses a method that utilizes computer-implemented models and data from experiments in machine learning models to identify and / or rank one or more biodegradable, compostable, and sustainable materials to a statistically significant degree, which comprises an active method step of: retraining a machine learning model using the measured or observed mechanical property including, but not limited to, a typical set of mechanical properties include, but are not limited to, strength, Young’s modulus (MPa), tensile strength (MPa), ultimate tensile strength, specific strength, Yield strength, dynamic strength, creep strength, torsion strength, fatigue strength, maximum flexural stress (MPa), stress, strain, stress-strain, stress at yield (MPa), specific modulus (MPa), flexural modulus (MPa), 1% secant flexural modulus (MPa), elongation at yield (MPa), elongation at break (%), impact energy (J / m), impact Strength (kJ / m2), hardness at 25°C (Shore D), tear strength (kN / m), tear work (kJ / m2), MFI @ 190 oC (g / 10 min), glass transition temperature (oC), melting temperature (oC), crystallization temperature (oC), and HDT (oC) and performing one or more iterations of the machine learning model until a polymeric formulation is identified with the desired properties.

[0279] 00298 In a particular embodiment, the methods of the disclosure can identify and / or rank materials that possess mechanical properties of a biodegradable, compostable, and sustainable material to a statistically significant degree. The biodegradable, compostable, and sustainable material could be known chemical entities or novel chemical entities. With regards to the former, the methods of disclosure can identify known chemical entities that can be used as a biodegradable, compostable, and sustainable material or be combined with other chemical entities to have an improved effects, and / or be used at amounts that are not normally utilized. The methods of the disclosure can also be used to identify new chemical entities based upon the machine learning modeling data and permutations made thereof.

[0280] 00299 It will be understood that the present system includes processor interconnecting a memory and a communications interface. The processor can include a central-processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a microprocessor, a processing core, a field- programmable gate array (FPGA), or similar. In some embodiments, the processor can include multiple cooperating processors. The processor can cooperate with non-transitory computer readable medium, such as the memory to execute instructions to realize the functionality discussed herein.

[0281] 00300 Memory can include a combination of volatile memory (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. non-volatile random-access memory, read only memory or ROM, Electrically Erasable Programmable Read Only memory or EEPROM, flash memory). All or some of the memory can be integrated with the processer. Memory stores computer reasonable instructions for execution by the processor.

[0282] 00301 It will now be apparent that each element of memory can be carried out by the processor executing operations. In other words, functionality described below as being carried out by a module of memory or a module of the generative or predictive engine can be based on any known server environment. 00302 In some embodiments, memory stores a plurality of computer-readable data and programming instructions, accessible by processor, in the form of software objects, such as various applications, queries or types of data for use during the execution of those applications. In particular, the execution of the instructions in memory by processor queries or requests data from property databases and receives said data via communications interface. In addition, the execution of the instructions in memory also determines if any data points are missing or incomplete from the retrieved data, compensates for the missing or incomplete data and analyzes the data. Furthermore, the execution of the instructions in memory by processor provides the resulting analyzed results in a graphical user interface for a user to view and make decisions upon. The person skilled in the art will now recognize the various forms of computer-readable programming instructions stored in memory can be executed by processor as applications or queries.

[0283] 00303 In a further preferred embodiment, the present invention provides a platform designed to bridge the gap between uses, producers, manufacturers and developers of biomaterials. In a preferred embodiment, users (e.g. buyers) seeking sustainable alternatives to fossil fuel-based applications and the suppliers, converters, and compounders who can support their transition can interact through the embodiments of the present invention. For example, buyers can upload their product application requirements, enabling biomaterial suppliers to assess feasibility and optimize their materials accordingly. Through the preferred systems of the present invention, users (e.g. suppliers) may receive tailored optimization guidelines to ensure their biomaterials align with buyer specifications while also gaining visibility into potential converters and compounders who can aid in the development and scaling of these solutions. By facilitating direct interactions between users and other users (e.g. key industry players), the platform of the present invention assists with streamlining a fragmented supply chain, fostering collaboration, innovation, and the widespread adoption of biomaterials. In yet a further embodiment, transactions may take place on the platform. However, the platform can serve as a dynamic networking and development hub, accelerating the shift toward biomaterial solutions

[0284] 00304 A preferred embodiment provides a platform that uses Al to optimize matchmaking, recommendations, pricing, and decision-making processes for the user (e.g. buyer persona). For buyers, the Al may analyze input parameters, and historical data to provide personalized material recommendations. For sellers, Al helps sellers by providing insights into market trends, customer preferences, customer insights, and blend insights, enabling them to optimize listings accordingly. Overall: The Al system enhances the efficiency, relevance, and experience of the marketplace, leading to smarter transactions for both parties.

[0285] 00305 In a preferred embodiment, there is provided a computer-implemented method of providing biomaterial material recommendations, the method comprising: (a) providing a set of precursor data, the precursor data comprising physical and nonphysical data on precursor materials; (b) receiving a first set of input data from a first user and outputting the first set of input data to a predictive modeling module, the predictive modeling module harnessing an artificial intelligence engine to analyze the first set of input data and the precursor data through machine learning to create a first potential formulation of the biomaterial; (c) receiving a second set of input data from a second user and outputting the second set of input data to a predictive modeling module, the predictive modeling module harnessing an artificial intelligence engine to analyze the second set of input data and the precursor data through machine learning to create a second potential formulation of biomaterial; (d) outputting the first and second a second potential formulation of biomaterial to a formulation modeling module, the formulation modeling module harnessing an artificial intelligence engine to analyze the first and second formulation through machine learning to create formulation data optimizing matchmaking, recommendations, pricing, and decision-making processes for the first and second user; and (f) providing the formulation data to the first and second user to provide tailored recommendations for a formulation of the biomaterial. In a further preferred embodiment, there is provided a buyer persona, in which the user is a buyer, and the buyer may input a set of parameters, which yields a list of biopolymers (generated using Al to enhance ingredient matches to user needs). The user can select a biopolymer from the ingredient list to then generate and optimize formulations using the biopolymer as the base material, leveraging the AI / ML of the present invention to ensure the formulation matches user's needs and satisfies the input parameters.

[0286] 00306 A preferred embodiment of the present invention is provided in FIGS. 1 and 2. There is provided predictive system 100 and generative system 200 each having a module or engine for prediction (110) or generation (210). Systems 100, 200 may be implemented with computer systems or mobile devices which are well known in the art. Generally speaking, computers and mobile devices include a central processor, system memory, and a system bus that couples various system components (typically provided on cards), including the system memory, to the central processor. A system bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The structure of a system memory may be well known to those skilled in the art and may include a basic input / output system (BIOS) stored in a read only memory (ROM) and one or more program modules such as operating systems, application programs and program data stored in random access memory (RAM). Computers and mobile devices may also include a variety of interface units and drives for reading and writing data. A user can interact with the computer or mobile device with a variety of input devices, all of which are known to a person skilled in the relevant art. Computers or mobile devices can operate in a networked environment using local connections to one or more remote computers or other devices, such as a server, a router, a network personal computer, a peer device or other common network node, a wireless telephone or wireless personal digital assistant.

[0287] 00307 Engines 110 and 210 are configured for communication with databases 120, 220 via a communication network. More specifically, engine 110, 210 includes communications interfaces, processors and memories wherein the engines 110, 210 query and receive data from databases 120, 220, and user inputs. In other embodiments, communications interfaces, processors and memories may be connected to a network, the internet or other databases for information, allowing processors to communicate with other computer devices, or other databases for information. This will be further discussed below. Specific components of communications interfaces, processors and memories are selected based on the type of network or other links that the embodiments of the present invention are required to communicate over.

[0288] 00308 Data and inputs received therefrom may then be modified and analyzed by engines 110, 210 and then displayed on a graphical user interface on a client terminal (see FIGS. 8 to 13). The analyzed data on the graphical user interface may then be used by the user in decision making with respect to the development of polymer materials, including compostable material, for a wide range of user defined applications. Components of systems 100, 200 will discussed further in detail below. 00309 As shown in FIG. 1, engine 110 contains a data cleaning or processing module 140 and a machine learning engine 150, along with user input module 130. In a preferred embodiment, the data processing modules include data collecting, data cleaning, feature extraction techniques and data normalization processes to ensure accurate and consistent input data from database 120 for predictive modeling. User input module 130 can provide the user with selections of prepopulated inputs or direct user input values. A person skilled in the relevant art would understand that any number of specific inputs could be used including but not limited to, Suitable inputs may include data on material composition, sample type, grades, molecular weights, chemical structure, application, manufacturing process, environmental factors and relevant mechanical properties.

[0289] 00310 As shown in FIG. 1, once the material inputs and additional data is further processed, the inputs 140a and 130a are feed into the machine learning engine 150, which in turn produces the mechanical outputs 160, which can be presented to a user via the GUI.

[0290] 00311 As shown in FIG. 2, engine 210 contains a data cleaning or processing module 240 and a machine learning engine 250, along with user input module 230. In addition, there is also provided a sampling module 250. The sampling module 250 takes input 240a and 230a from the processed data of module 240 and the user’s desired mechanical properties inputted via module 230 to prepare a “sample” of possible materials, compositions, etc. to input into machine learning engine 260. Sampling provides for extrapolating new data points from the plausible values from the model inputs and allows the model to generate new data. More specifically, in the context of the embodiments of the present invention, "sampling" encompasses the systematic generation of data points from various material compositions and properties to ensure a comprehensive exploration of the sampling space. Specifically, the embodiments of the present invention employ stratified sampling techniques, which are structured methods designed to cover the entire range of possible solutions effectively. These techniques can aim to ensure that the sampling process adequately explores diverse combinations of material compositions and properties, seeking solutions that fulfill specified requirements. Stratified sampling methods such as Latin hypercube sampling, Halton-sequence sampling, and Sobol-sequence sampling are implemented as part of a systematic search algorithm within the embodiments of the present invention. By utilizing these stratified sampling methods, the system can methodically search through the space of different materials concentrations, thereby identifying a wide array of potential composites that meet the desired criteria. This approach enhances the robustness and efficiency of the sampling process, enabling the machine learning engine to make more accurate recommendations for formulations that meet the user’s desired mechanical properties. In a preferred embodiment, the data processing module 240 includes data collecting, data cleaning, feature extraction techniques and data normalization processes to ensure accurate and consistent input data from database 220 for generative modeling. User input module 230 can provide the user with selections of prepopulated inputs or direct user input values. A person skilled in the relevant art would understand that any number of specific inputs could be used including but not limited to, specific material properties or ranges of properties. Suitable inputs may include data on material composition, sample type, grades, molecular weights, chemical structure, application, manufacturing process, environmental factors and relevant mechanical properties. As shown in FIG. 2, once the material inputs and additional data is sampled via engine 250, the inputs 250a are feed into the machine learning engine 260, which in turn produces the mechanical outputs 270, which can be presented to a user via the GUI.

[0291] 00312 It will be understood by a person skilled in the relevant art that, in a preferred embodiment, databases 120, 220 for each associated system may include, but are not limited to, a list of materials and concentrations (i.e. ingredients and weight percent), sustainability data (i.e. biobased carbon content & LCA), and material costs. It will also be understood by a person skilled in the relevant art that inputs 140a, 230a may include, but are not limited to, compostability and / or sustainability criteria, such as, time, thickness, chemical structures, molecular properties, Tg, etc. and mechanical properties, such as material / sample type (flexible vs. rigid), processing parameters, chemical structures, Tg, etc.). It will also be understood by a person skilled in the relevant art that inputs 140a, 230a may include, but are not limited to, mechanical properties (e.g. Tensile Strength), compostability, sustainability, disintegration (% / time) and biodegradation (% / time), biobased carbon content (BCC), LCA / GWP (Global Warming Potential, Cost, etc.

[0292] 00313 The machine learning engines 150, 260 provide for the machine learning algorithms and models utilized in the present invention and as described herein.

[0293] 00314 FIG. 3 provides for the predictive system 300 wherein there is an embodiment of the predictive system of the present invention. As shown in FIG. 3, there are a plurality of possible database, depicted as 310, 320. There is also provided user inputs 330, similar to the inputs 130, 230 of FIGS 1 and 2. There is also provided modules 240, 350 for data cleaning and preprocessing as noted herein. Engines 360a and 360b, as noted herein can provide the output 370, which can be displayed to the user (e.g. via a GUI). FIG. 3 presents the overall architecture of the entire predictive module, from a high level perspective, whereas FIGS. 5 A, 6 A and 7 represent a more detailed look into this architecture. In summary, FIG, 3 illustrates 1) The material inputs feeding into engines (machine learning black box models) 2)Simultaneously, the databases (e.g. DB1 and DB2) are undergo preprocessing and fed into the engines (e.g. Engine 1 and Engine 2) 3) All of the inputs, database, engines, are used to predict outputs, more specifically formulations with sustainability and mechanical properties.

[0294] 00315 FIG. 4 provides for the generative system 400 wherein there is an embodiment of the generative system of the present invention. As shown in FIG. 4, there are a plurality of possible databases, depicted as 410, 420. There is also provided user inputs 430, similar to the inputs 130, 230 of FIGS 1 and 2. There is also provided modules 440, 470 for data cleaning and preprocessing as noted herein. Engines 460a and 460b, as noted herein can provide the output 480, which can be displayed to the user via the GUI (see FIG. 10) FIG. 4 presents the overall architecture of the entire generative module, from a high level perspective, whereas FIGS. 5B, 6B and 7 represent the granular look into this architecture. In summary, FIG. 4 shows 1) The material inputs feeding into the sampling process, 2) Simultaneously, the databases (e.g. DB1 and DB2) are undergo preprocessing and fed into the engines (e.g. Engine 1 and Engine 2), which are the machine learning algorithms. 3) All of the inputs, database, engines, and sampling methods then yield generated outputs, more specifically formulations with sustainability and mechanical properties.

[0295] 00316 FIGS. 5A and 5B provide a preferred embodiment wherein there is a predictive and generative module, respectively, for both the prediction and generation of material having specific compostability and / or sustainability characteristics or properties. As shown in FIG. 5A, there is depicted various inputs from material compositions 501A, chemical characteristics 502A and a database of sustainability characteristics 503A. In a preferred embodiment, these feed into the predictive system set for desired sustainability characteristics to product the specific predicted sustainability properties 520A. As shown in FIG. 5B, there is depicted a generative module for arriving at target sustainability of a material. First, the target sustainability can be imputed by a user (see 50 IB), then samples are produced (in a preferred embodiment via sampling module 250 as shown in FIG. 2). The samples from 550B are then inputted into engine 510B to produce the resulting sustainability which can then be feed back into 50 IB to further refined the desired characteristics until the desired product is prepared.

[0296] 00317 FIGS. 6A and 6B provide a preferred embodiment wherein there is a predictive (FIG. 6A) and generative module (FIG. 6B), respectively, for both the prediction and generation of material having specific mechanical characteristics or properties. As shown in FIG. 6A, there is depicted various inputs from database 620A, which has been processed and / or cleaned via module 640A and material compositions 630 A. In a preferred embodiment, 640 A and 630 A are feed into the predictive system 650A set for desired mechanical characteristics to product the material with the specific predicted mechanical properties 660A. As shown in FIG. 6B, there is depicted a generative module for arriving at target mechanical properties of a material. First, the target mechanical properties or characteristics can be inputted or selected by a user (see 630B). As shown in FIG. 6 A, there is depicted database 620B, the data stored therein having been processed and / or cleaned via module 640B before combined with the target mechanical properties 630B selected by the user to produce samples 650B (in a preferred embodiment via sampling module 250 as shown in FIG. 2). The samples from 650B are then inputted into engine 660B to produce the materials having the desired mechanical properties or characteristics 670B.

[0297] 00318 FIG. 7 depicts a preferred embodiment wherein using the glass transition temperature can be used to predict or generate mechanical or sustainability characteristics. As shown in FIG. 7, there is a system 700 having a database 710 as described previously. There is also the ensemble engine 720 (having both the predictive (see 360a and 460a in FIGS. 3 and 4) and generative engines (see 360b and 460b in FIGS. 3 and 4) in accordance with the present invention and as noted herein). These engines can received input based on mechanical features and material properties. Using the glass transition temperature (“Tg”) it is possible to predict the desired sustainability.

[0298] 00319 In a preferred embodiment, there may be a graphic user interface (“GUI”) for use in association with the systems, methods and apparatus of the present invention. As shown in FIG. 1, there is generative system for generating and customizing polymer materials, including sustainable materials, for a wide range of applications. More specifically, the system includes a graphical user interface where a user can select from a desired functionality of the target material -n- and / or the desired sustainability of the target material to be generated. The user can select which “module” they wish to leverage to generate desired materials, including formulations of such target materials. The user may select the nature of characteristics of the specific formulation of the target material (e.g. binary blend, tertiary blend, etc.). Based upon the initial criteria that may be selected or inputted by the user, the embodiments of the present invention as noted herein can suggest the most relevant target material to satisfy the specified input criteria / conditions. As noted herein, the initial criteria may include mechanical properties, sustainability targets, etc. It will be understood by a person skilled in the relevant art that the degree to which the system satisfies the specific starting criteria or conditions will vary based on the degree of stringency required from by the user. This embodiment can enable users to sort the results provided by the generative module based on their priorities regarding the mechanical or sustainability properties. In a preferred embodiment, where the user wishes to select a desired functionality, the user may be presented with drop down menu where the user can select from a number of desired functionality criteria. In a preferred embodiment, there is selected, as an example, the “Elongation at Break (%)”. Should the user wish to select a desired sustainability, there may be provided a pull down menu where specific properties can be selected. There may also be a number of materials available for selection whether the user is choosing functionality or sustainability. A person skilled in the relevant art would understand that any number of materials (e.g. polymers, plasticizers / elastomers, fillers, etc.) can be selected. It will be understood that these materials may be proprietary or may be generally publicly available.

[0299] 00320 In a preferred embodiment, there may be a GUI where the user can further define the specific functionality of the materials that the user wishes the end material to have. In the case of the Elongation at Break criteria, the user can select the applicable parameters. The user may also select specific materials to be used in the development of the desired functionality.

[0300] 00321 In a preferred embodiment, a user can select from desired experimental properties, including compostability time, thickness, etc. of the target material and / or the desired formulation, including ingredients and specific concentrations of the target material. Based upon the initial criteria that may be selected or inputted by the user, the embodiments of the present invention as noted herein can predict the most relevant target material to satisfy the specified input criteria / conditions. As noted herein, the initial criteria may include physical or non-physical properties, thermal properties, etc. It will be understood by a person skilled in the relevant art that the degree to which the system satisfies the specific starting criteria or conditions will vary based on the degree of stringency required from by the user. In a preferred embodiment, the user may seek the most accurate predictions possible for the mechanical properties of their chosen formulation. However, the results also include the error level or reliability, which can provide guidance to the user regarding the accuracy of the results. In a preferred embodiment, where the user wishes to select a desired starting material, the user may be presented with a menu where the user can select from a number of desired criteria. There may also be a number of target materials available for selection that the user may choose to see how the varied target material may vary in their characteristics and such difference can be graphically displayed.

[0301] 00322 FIG. 12 depicts the predicted outputs from the models in a summary table, as an example, within the materials discovery module. In this particular scenario, a material was built and the disintegration, biodegradation, biobased carbon content, global warming potential, and elongation at break were predicted. The table presents the units (e.g. %) and the predicted values (e.g. 90%). Simply put, this is a screenshot of what the users will see as predictive outcomes from their material / formulation selection.

[0302] FIGURE 12 - Schematic representation of the model based on the chemical structures of the materials 00323 The embodiments of the present invention are built on top of previous inventions which are built on machine learning (ML) algorithms that empower generative Al capabilities, with added full suite of features for input materials’ properties and sample / product type.

[0303] 00324 Leveraging generative Al & ML-prediction algorithms, the embodiment of the present invention provides users the solutions for predicting and / or designing and generating cutting-edge formulations, composites, and / or polymeric materials with desired mechanical performance and sustainability criteria. The embodiments of the present invention is continuously growing and improving, with an existing and growing material database of a plurality of materials (e.g., 90) including bio-derived materials, biopolymers, additives, plasticizers, elastomers, compatibilizer, and fillers, with each material containing datasets for model training, testing and validating, collected by open-source research articles and / or databases and collected by internal R&D team.

[0304] 00325 While the present invention has been described with reference to a number of preferred embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention is not limited to the particular embodiments disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, the use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented system for predicting and optimizing a desired parameter of a desired biomaterial, comprising:(a) a data processing module;(b) a predictive modeling module; and(c) a formulation recommendation and generation module, wherein the data processing module is designed to gather, store and organize a plurality of datasets each comprising collected data related to a plurality of precursor biomaterials, the collected data comprising physical and non-physical characteristics of the plurality of precursor biomaterials; and wherein the predictive modeling module harnesses an artificial intelligence engine to analyze the collected data through machine learning to create prediction data to determine a potential formulation of the desired biomaterial having the desired parameter; and wherein the formulation recommendation and generation module harness an artificial intelligence engine to the prediction data to provide a formulation of the precursor biomaterials to arrive at the desired biomaterial having the desired parameter.

2. The computer-implemented system of claim 1 wherein the collected data comprises composition data, manufacturing process data, economic factor data, environmental factor data, compostability data, or sustainability data of the precursor biomaterials.

3. The computer-implemented system of claim 2 wherein the collected data comprises user derived data inputted from a user.

4. The computer-implemented system of claim 2 wherein the sustainability data comprises chemical structure of the precursor biomaterials, physical properties comprising density,molecular weight, glass transition temperature, degree of crystallization of the precursor biomaterials.

5. The computer-implemented system of any one of claims 1 to 4, wherein the desired biomaterial is a compostable materials or a sustainable material.

6. The computer implemented system of claim 1, wherein the desired biomaterial is compostable, designed for a reduced environmental footprint.

7. The computer implemented system of any one of claims 1 to 6, wherein the predictive modeling module uses chemical structure modeling to model disintegration and biodegradation properties of the desired biomaterial.

8. The computer implemented system of claim 6, wherein the desired biomaterial comprises plastics, textiles, or packaging materials.

9. The computer implemented system of claim 1, wherein the data processing modules comprise feature extraction techniques and data normalization processes to ensure accurate and consistent collective data for predictive modeling.

10. The computer implemented system of claim 9, wherein the collected data may be compiled using one or several normalization methods including Z-score normalization, mean data imputation, imputation based on the domain knowledge, or combinations thereof.

11. The computer implemented system of claim 9, wherein the data processing module features adaptive algorithms that dynamically adjust to the characteristics of the input data, wherein these adaptive algorithms autonomously adapt their parameters based on identified features during a preprocessing phase.

12. The computer implemented system of any one of claims 1 to 11, wherein the artificial intelligence engine is selected from the group consisting of Support Vector Machines (SVM), Artificial Neural Networks, Linear Regression, Random Forest Regression and XGBoost to predict compostability and / or sustainability properties.

13. The computer implemented system of claim 9, wherein the system integrates an optimized gradient boosting algorithm into the artificial intelligence engine of the predictive modeling module.

14. The computer implemented system of claim 13, wherein the optimized gradient boosting algorithm is XGBoost.

15. The computer implemented system of claim 1, wherein the formulation recommendation module offers user customizable guidance for sustainable material compositions.

16. The computer implemented system of claim 15, wherein the artificial intelligence engine of the formulation recommendation and generation module incorporates user-defined input and output parameters to the prediction data when providing a formulation of the precursor biomaterials to arrive at the desired biomaterial having the desired parameter.

17. The computer system of claim 16, wherein the artificial intelligence engine of the predictive modeling module uses a systematic search algorithm to identify precursor biomaterials.

18. The computer system of claim 17, wherein the systematic search algorithm is a multiple stratified sampling method selected from Latin hypercube sampling, Halton-sequence sampling, or Sobol-sequence sampling.

19. The computer system of claim 16, wherein formulations can be generated based on a user’ s preference for precursor biomaterials based on number of polymers, plasticizers, natural and inorganic fibers, or any number of chain extenders.

20. The computer system of claim 16, wherein user-defined input and output parameters are selected from biodegradation, disintegration, Biobased Carbon Content, and LCAs.

21. The computer implemented system of any one of claim 1 to 20, wherein the collected data is selected from the group consisting of Young’s modulus (MPa), Young’s modulus-MD (MPa), Young’s modulus-TD (MPa), tensile strength (MPa), tensile strength-MD (MPa), tensile strength- TD (MPa), ultimate tensile strength, specific strength, Yield strength, dynamic strength, creep strength, torsion strength, fatigue strength, maximumflexural stress (MPa), stress, strain, stress-strain, stress at yield (MPa), specific modulus (MPa), flexural modulus (MPa), 1% secant flexural modulus (MPa), elongation at yield (MPa), elongation at break (%), elongation at break-MD (%), elongation at break-TD (%), impact energy (J / m), impact Strength (kJ / m2), hardness at 25 °C (Shore D), tear strength (kN / m), tear work (kJ / m2), MFI @ 190 oC (g / 10 min), glass transition temperature (oC), melting temperature (oC), crystallization temperature (oC), and HDT (oC) or combinations thereof.

22. The computer implemented system of claim 1, wherein the artificial intelligence engine of the data processing module comprises feature extraction techniques and data normalization processes.

23. The computer implemented system of claim 22, wherein the data is compiled using Z-score normalization, mean data imputation, k-nearest neighbors (KNN) imputation, or combinations thereof.

24. The computer implemented system of claim 22, wherein the data normalization processes ensures that inputs are represented across different scales, units, and formats, and wherein variations in the original dataset are addressed, minimizing discrepancies and facilitating the accurate interpretation of data, and wherein normalization processes contribute to the stability and reliability of predictive modeling module.

25. The computer implemented system of claim 1, wherein the artificial intelligence engine of the data processing module feature adaptive algorithms that dynamically adjust to the characteristics of the collected data, wherein these algorithms autonomously adapt their parameters based on identified features during the preprocessing phase to ensure the system efficacy in handling diverse datasets and evolving data patterns over time.

26. A method for predicting the development and production of a desired biomaterial having a desired parameter, the method comprising:(a) inputting, storing and organizing a plurality of datasets comprising collected data related to a plurality of precursor biomaterials, the collected data comprisingphysical and non-physical characteristics of the plurality of precursor biomaterials in a data processing module;(b) outputting the collected data to a predictive modeling module, the predictive modeling module harnesses an artificial intelligence engine to analyze the collected data through machine learning to create prediction data determining a potential formulation of the desired biomaterial having the desired parameter; and(c) outputting the prediction data to a formulation recommendation and generation module, wherein the formulation recommendation and generation module applies the prediction data to provide tailored recommendations for a formulation of the precursor biomaterial to arrive at the desired biomaterial having the desired parameter.

27. The method of claim 26 wherein the collected data comprises composition data, manufacturing process data, economic factor data, environmental factor data, compostability data, or sustainability data of the precursor biomaterials.

28. The method of claim 27 wherein the sustainability data comprises chemical structure of the precursor biomaterials data, physical properties of the precursor biomaterials data, the physical properties comprising density, molecular weight, glass transition temperature, degree of crystallization of the precursor biomaterials.

29. The method of any one of claims 26 to 28, wherein the desired biomaterial is a compostable material or a sustainable material.

30. The method of claim 26, wherein the desired biomaterial is compostable.

31. The method of any one of claims 26 to 30, wherein the predictive modeling module uses chemical structure modeling to model disintegration and biodegradation properties of the desired biomaterial.

32. The method of claim 31, wherein the desired biomaterial comprises plastics, textiles, or packaging material.

33. The method of claim 26, wherein the data processing module employs feature extraction techniques and data normalization processes to the collected data for predictive modeling.

34. The method of claim 33, wherein the collected data is compiled using a normalization method selected from the group consisting of Z-score normalization, mean data imputation, imputation based on the domain knowledge, or combinations thereof.

35. The method of claim 34, wherein the data processing module employs adaptive algorithms that dynamically adjust to the characteristics of the input data, wherein these adaptive algorithms autonomously adapt their parameters based on identified features during the preprocessing phase to ensure the system efficacy in handling diverse datasets and evolving data patterns over time.

36. The method of any one of claims 26 to 35, wherein the predictive modeling modules uses machine learning model selected from the group consisting of Support Vector Machines (SVM), Artificial Neural Networks, Linear Regression, Random Forest Regression and XGBoost to predict compostability and / or sustainability properties.

37. The method of any one of claims 26 to 35 wherein the data related to a plurality of precursor biomaterials further comprises a user input and the artificial intelligence engine of the predictive modeling module analyzes user inputs, and compares the user input against historical data to provide recommendations and pricing information along with an optimization guidelines on how to build optimized blends of precursor biomaterials based on the collected data.

38. A computer-implemented method of providing biomaterial material recommendations, the method comprising:(a) providing a set of precursor data, the precursor data comprising physical and nonphysical data on precursor materials;(b) receiving a first set of input data from a first user and outputting the first set of input data to a predictive modeling module, the predictive modeling module harnessing an artificial intelligence engine to analyze the first set of input data and theprecursor data through machine learning to create a first potential formulation of the biomaterial;(c) receiving a second set of input data from a second user and outputting the second set of input data to a predictive modeling module, the predictive modeling module harnessing an artificial intelligence engine to analyze the second set of input data and the precursor data through machine learning to create a second potential formulation of biomaterial;(d) outputting the first and second a second potential formulation of biomaterial to a formulation modeling module, the formulation modeling module harnessing an artificial intelligence engine to analyze the first and second formulation through machine learning to create formulation data optimizing matchmaking, recommendations, pricing, and decision-making processes for the first and second user; and(e) providing the formulation data to the first and second user to provide tailored recommendations for a formulation of the biomaterial.

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