Method for determining target formulations containing target biodegradability

Abstract

The present invention relates to a method for determining a formulation specification including a target biodegradation rate. A target biodegradation rate indicating the biodegradation characteristics of a formulation is provided. A digital representation of a potential target formulation specification is provided. A habitat is provided indicating habitat descriptor values ​​for habitat descriptors. A model is provided based on the habitat, adapted to determine the biodegradation rate of the formulation in the habitat. The biodegradation rate of the potential target formulation is determined based on the provided model and digital representation. The determined biodegradation rate is then compared to the target biodegradation rate, and either i) the potential target formulation is determined as the target formulation, or ii) a new potential target formulation specification for the potential target formulation is provided, and the determination of the biodegradation rate is repeated using the new potential target formulation specification.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present invention relates to a method, apparatus, and computer program product for determining a target formulation specification that indicates a target formulation having a target biodegradation rate. The present invention also relates to a training method, training apparatus, and training computer program for training a data-driven biodegradation model that can be utilized by the method, apparatus, and computer program product for determining a target formulation specification. The present invention also relates to a method and apparatus for providing an interface for providing a target formulation specification. [Background technology]

[0002] Background of the Invention In general, formulations, i.e., products containing at least two chemical components, are widely used in industrial and / or daily necessities due to their wide range of application characteristics. The use of formulations includes, among others, coatings, personal care products, cleaning detergents, lubricants, packaging, films, and foams. However, this widespread application also results in a large amount of waste containing used formulations. Non-degradable waste is problematic when disposed of in undesignated environments. In particular, the accumulation of chemicals resulting from chemicals that do not undergo a change in chemical structure to return to the circulation is undesirable. Thus, if non-biodegradable formulations are not properly recovered in the intended waste stream, this can lead to increased chemical pollution in the environment. Therefore, not only are degradable formulations necessary, but knowledge of the formulation's biodegradability must also be taken into account early in the product design process. In particular, it would be advantageous to be able to predict formulations that provide specific biodegradability and are suitable for the intended application already during the manufacturing design process. Therefore, it would be advantageous to provide the ability to accurately and computationally inexpensively predict formulations containing biodegradability suitable for an application. Summary of the Invention [Problem to be solved by the invention]

[0003] Summary of the Invention It is an object of the present invention to provide a method, an apparatus, and a computer program product that allows for accurate determination and allows for the determination of formulation specifications that indicate a target formulation with a target biodegradation degree at low computational cost. It is also an object of the present invention to provide a training method, a training apparatus, and a computer program product that can be used in the method, the apparatus, and the computer program product and that allows for the provision of a biodegradation model that can be trained to provide good determination accuracy by utilizing fewer computational resources. [Means for solving the problem]

[0004] In a first aspect of the present invention, a computer-implemented method for determining a target formulation specification indicative of a target formulation including a target biodegradation degree is provided, the method comprising: a) providing a target biodegradation degree, the biodegradation degree indicative of a biodegradation characteristic of a potential target formulation; b) providing a digital representation of a potential target formulation for the potential target formulation; c) providing biodegradation habitats, the biodegradation habitats indicative of habitat descriptor values ​​for habitat descriptors that affect the biodegradation of the formulation in each habitat, the habitat descriptors indicative of environmental characteristics of the habitat; and d) providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model indicative of a biodegradation characteristic of the formulation in each biodegradation habitat. a digital representation of the formulation; and a method for determining a biodegradation rate of the potential target formulation, the method comprising: a) providing a digital representation of the formulation based on the digital representation of the formulation; b) determining a biodegradation rate of the potential target formulation based on the provided biodegradation model and the digital representation; c) determining a biodegradation rate of the potential target formulation based on the provided biodegradation model and the digital representation; and e) comparing the determined biodegradation rate of the potential target formulation with the target biodegradation rate, and based on the comparison, either i) determining the potential target formulation as the target formulation and determining the potential target formulation specification as the target formulation specification, or ii) providing a new potential target formulation specification for the potential target formulation and repeating the determination of the biodegradation rate using the new potential target formulation specification for the potential target formulation.

[0005] Because the biodegradation model is specifically adapted to determine the biodegradation rate of a potential target formulation for a specific biodegradation habitat characterized by respective habitat descriptor values ​​that affect the biodegradation rate of the formulation in each habitat, the biodegradation rate of the formulation, particularly the potential target formulation, for each habitat can be determined with great accuracy. Furthermore, because the biodegradation model is specifically trained for one or more specific biodegradation habitats, less training data is required for training, and the biodegradation model is more flexible in determining the biodegradation rate of new formulations that are not part of the training data set. Thus, accurate determination of the biodegradation rate of potential target formulations is provided with low computational cost. And because the determination of the target formulation is based on accurate and computationally inexpensive biodegradation rate determinations, the method also enables accurate and computationally inexpensive determination of target formulation specifications that indicate target formulations containing respective target biodegradation rates. Furthermore, whereas currently utilized testing methods for testing the biodegradation rate of formulations are very time-consuming and can take months or years to obtain results, the above-described method allows for essentially immediate results, particularly for potentially suitable formulations. This not only reduces the technical requirements for determining the degree of biodegradability, but also significantly shortens the time required to design new biodegradable products. Furthermore, by providing an easy possibility to take into account the accurate determination of the degree of biodegradability already during the product design process, it makes it possible to design products in such a way that plastic waste, especially in the form of microplastics, can be avoided. In particular, it can be ensured that the compounds used in the products will biodegrade in the respective expected environment, for example, the marine habitat.

[0006] The development of new chemical products tailored to application requirements is a major challenge in the modern chemical industry. In recent years, additional requirements have arisen regarding the environmental impact of chemical products during their life cycle. One important aspect of environmental impact is the prevention of chemical accumulation. This is a growing problem that can be avoided if formulation materials are biodegradable. A series of standardized tests are currently used to evaluate biodegradability. Various tests exist for biodegradability using specific conditions (e.g., ISO 13432, ISO 14852, ISO 14855, ISO 17556, and OECD 301). Standardized tests often balance time-efficient testing (as short as 14 days and as long as 24 months) with realistic conditions. In fact, to speed up testing, temperatures higher than those in real-world conditions are often used. Companies developing new plastics must invest significant resources in self-assessment and certification of their product sustainability. The entire biodegradation evaluation process, including laboratory space and equipment, can be costly and time-consuming. Therefore, there is a need to identify the biodegradability of new materials early in the development process. The proposed method for determining biodegradability disclosed herein allows for faster and more efficient development of new materials. The biodegradability can be determined at an early stage, even before the formulation is prepared. This makes it possible to determine whether a formulation is suitable for market entry, thereby shortening the time to market. This also allows for reduced waste generation, as the formulation does not need to be synthesized to determine its biodegradability. The proposed method provides a digital twin of the measurement of the biodegradability of the formulation.

[0007] Furthermore, standard measurements and tests of biodegradability are often time-consuming, involving waiting times of up to several months or even years. Particularly when developing new formulations for each application, these time-consuming tests can severely limit the development process. In this context, the present invention makes it possible to provide results for new formulations immediately, significantly reducing the time after which results are available.

[0008] Furthermore, because the number of potentially suitable formulations for a particular application is enormous and, in many cases, many of these formulations have not been thoroughly researched, technical product engineers today are faced with the technical challenge of finding formulations that are not only suitable for a particular application but also satisfy the respective target properties, particularly the target biodegradability. To find each formulation that may be suitable for the application, they must prepare and test a vast number of possible formulations or examine vast data sets and libraries in which potential formulations are stored. Even when using sophisticated experimental design, it is still necessary to prepare and experimentally test a very large number of possible formulations. In this regard, the above-described method can assist users, such as technical product engineers, in automatically finding potentially suitable formulations much more quickly. In particular, by using the above-described method, users only need to prepare and test potentially suitable formulations that are determined to be highly likely to satisfy the respective target properties, particularly the target biodegradability. Therefore, unnecessary preparation and testing of formulations can be avoided. Therefore, the method allows users to perform the technical task of finding suitable formulations for technical applications more quickly and efficiently.

[0009] The present method refers to a computer-implemented method and can therefore be performed by a general or specialized computer adapted to execute the method, for example, by executing the respective computer program. The present method is adapted to determine, particularly predict, a target formulation specification that indicates a target formulation having a target biodegradability. Generally, the formulation specification includes instructions on how to produce a specific related formulation. For example, the formulation specification, if applicable, can refer to the ingredients, starting products, and / or manufacturing conditions that result in the preparation of the formulation in a manufacturing process. Thus, the formulation specification is always associated with the formulation produced when the formulation specification is implemented, for example, using suitable laboratory or industrial equipment. In particular, the formulation specification can also be considered as a recipe for how to produce the related formulation. Since the target formulation specification and the target formulation correspond to each other, i.e., the target formulation, when executed accordingly, produces the target formulation, both terms can be used simultaneously hereinafter, for example, when the target formulation specification is determined, the target formulation is also determined, and vice versa.

[0010] A biodegradable formulation refers to a formulation that can be broken down by biological processes. In particular, a biodegradable formulation can refer to a formulation that can be assimilated by bacteria and / or fungi to produce environmentally friendly products, i.e., decomposed into non-polluting residues, for example, by generating inorganic carbon and / or biomass. Generally, biodegradability indicates the biodegradation characteristics of a formulation. In particular, biodegradability refers to a measure of the degradation, or decomposition, of a formulation caused by biological processes, i.e., processes involving biological materials, particularly microorganisms, involved in the degradation process. Thus, biodegradability does not refer to a purely chemical degradation process that does not involve microbial activity. Biodegradability is an intrinsic property of a formulation. In this context, the intrinsic property of a formulation refers to a property of a formulation that arises from and therefore reflects the properties of the formulation relative to a specific situation, i.e., structure, composition, etc. In particular, biodegradability reflects the properties of a formulation when present in a specific biologically active environment. Target biodegradability can refer to any quantification of the biodegradability of a formulation. For example, the target biodegradation can refer to only one value, e.g., the half-life of the formulation in a respective habitat, or it can refer to more than one value, e.g., the degradation function of the formulation over time in a particular habitat. Preferably, the target biodegradation of a formulation refers to any one of the formulation's mineralization properties, biotransformation properties, and / or degradation half-life. Preferably, the target biodegradation is provided in the form of a percentage value of biodegradation after a predetermined time frame.

[0011] Biodegradable formulations can be designed to break down through biological action upon disposal. Biodegradability can relate to the environmental fate and / or behavior of a formulation. Biodegradability can relate to the extent to which a formulation can be degraded by microorganisms such as bacteria, fungi, or algae. Biodegradability can depend on the component composition of the formulation's chemical structure, molecular weight, physical factors (such as crosslink density, branching, crystallinity, or solubility), and exposure conditions (such as the habitat, such as soil, compost, or aquatic systems). With respect to exposure conditions, substrate properties such as the microorganism, microbial population, nutrient concentration, temperature, pH, pO2, ionic conditions, or toxicity affect biodegradability. Biodegradability can be measured based on measured mass loss (mg / hour), dissolved organic carbon (DOC, organic carbon concentration / hour), oxygen consumption (e.g., by manometry, e.g., Pa / hour), or carbon dioxide evolution over time (e.g., by manometry, e.g., Pa / hour).

[0012] Many measurement standards have been developed to quantify biodegradability in terms of measured properties of a compound. Various measurement methods have been defined to determine biodegradability under specific laboratory conditions. For example, for wastewater, OECD Test No. 301: "Ready Biodegradability" (July 17, 1992) describes six methods for determining biodegradability. Furthermore, ASTM D5988-18, "Standard Test Method for Determining Aerobic Biodegradation of Plastic Materials in Soil," describes the measurement of carbon dioxide evolved by microorganisms as a function of exposure time, thus determining the degree of biodegradation compared to a reference material. Furthermore, ISO 17556:2019, "Plastics—Determination of the Ultimate Aerobic Biodegradability of Plastic Materials in Soil by Monitoring the Oxygen Demand in a Respirator or the Amount of Carbon Dioxide Evolved," describes the optimal rate of biodegradation of plastic materials in test soil by controlling the oxygen consumption or carbon dioxide evolution.Further, for example, ISO 14855-1:2012 "Determination of the ultimate aerobic biodegradability of plastic materials under controlled composting conditions—method by analysis of evolved carbon dioxide—Part 1: General method" and ASTM D5338-15 "Standard test method for determining aerobic biodegradation of plastic materials under controlled composting conditions, incorporating thermophilic temperatures" determine the ultimate aerobic biodegradability of plastics (the ability of microorganisms to completely consume chemicals or organic materials in the presence of oxygen) based on organic compounds under controlled composting conditions by measuring the percentage of carbon to carbon dioxide conversion and the degree of disintegration of the plastic at the end of the test. ASTM D6400-21 "Standard specification for labeling of plastics designed to be aerobically composted in municipal or industrial facilities" additionally includes ASTM elemental analysis, plant germination (phytotoxicity), and mesh filtration of the resulting particles. ISO 17088:2021 "Plastics - Organic recycling - Specifications for compostable plastics" includes an assessment of the adverse effects on the composting process and equipment, as well as on the quality of the resulting compost, including the presence of high levels of restricted metals and other harmful elements.

[0013] Regarding aerobic biodegradation, the following standards have been developed: ISO 18830:2016 "Plastics - Determination of aerobic biodegradation of non-floating plastic materials in a seawater / sandy sediment interface - Method by measuring the oxygen demand in a closed respirometer" and ISO 19679:2020 "Plastics - Determination of aerobic biodegradation of non-floating plastic materials in a seawater / sediment interface - Method by analysis of evolved carbon dioxide." Biodegradation assessment is measured by oxygen demand or CO2 evolution. Further standards include, for example, ISO 14853:2016 "Plastics - Determination of the ultimate anaerobic biodegradation of plastic materials in an aqueous system - Method by measurement of biogas production", ISO 23977-1:2020 "Plastics - Determination of the aerobic biodegradation of plastic materials exposed to seawater - Part 1: Method by analysis of evolved carbon dioxide", and ISO 23977-2:2020 "Plastics - Determination of the aerobic biodegradation of plastic materials exposed to seawater - Part 2: Method by measuring the oxygen demand in a closed respirometer".

[0014] The quantified biodegradation properties for a formulation may depend on the measurement method and conditions used, the measurement environment, and measurements related to the degradation process (such as mass loss, DOC, oxygen consumption, or carbon dioxide evolution over time). The measurement method and measured properties may be provided as metadata for each measurement point related to biodegradation.

[0015] Generally, the formulation can be any formulation. The formulation is composed of at least two components, which can refer to any chemical substance. For example, the components can be small molecules, polymers, etc. However, the components can also be more complex chemical products themselves. In a preferred example, the formulation defines the packaging of the product.

[0016] In a first step, the method includes providing a target biodegradability indicating the biodegradation characteristics of the formulation. In particular, providing may refer to receiving the target biodegradability from a user's input, for example, using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the target biodegradability is already stored. Furthermore, providing may also include receiving the target biodegradability from another source, for example, via a network connection, and providing the received biodegradability. Generally, the target biodegradability may refer to a single target value, for example, the target half-life of the formulation in a particular habitat, or may refer to a range of values ​​that the formulation should meet in a particular habitat. Furthermore, the target biodegradability may also refer to any type of target function, for example, a time sequence of biodegradation. For example, the target biodegradability may indicate that the target formulation should have a first range of biodegradability values ​​during a first time range, followed by a second range of target biodegradability values ​​during a subsequent time range. Furthermore, the target degradation function may take into account the specific interactions of the biodegradability of the components of the target formulation. For example, the target function may determine that the outer components of the packaging should biodegrade over a first time range, and the inner components of the packaging should biodegrade over a later second time range. Such a more complex target biodegradability may be advantageous when a formulation is desired to not biodegrade in a particular habitat for some time, e.g., during the average use time of the formulation, but then rapidly biodegrade in the same or another habitat. Furthermore, the target biodegradability may also refer to the target biodegradability of each component of the formulation or the target biodegradability of a combination of components of the formulation. It may also be useful to determine the specific biodegradability of each component, particularly when the components of the formulation fulfill different technical functions in the intended application. For example, in the case of food packaging, a component in contact with the food should not biodegrade in the habitat provided by the food, e.g., the milk environment, but should later biodegrade, e.g., in a compost habitat, along with the other components of the formulation.

[0017] The method includes providing a digital representation of a potential target formulation specification for a potential target formulation. In particular, providing may refer to receiving the digital representation from a user input, e.g., using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the digital representation is already stored. The digital representation of the potential target formulation specification may be any representation that provides information that allows defining and preparing the potential target formulation. Furthermore, it is preferred that the digital representation includes and / or allows deriving respective characterization parameters, e.g., physicochemical properties of the components of the potential target formulation. Preferably, the digital representation includes indicators for the characterization parameters that refer to the chemical structures of at least two components of the potential target formulation and measures of the quantities of the at least two components of the potential target formulation to define the formulation. The measures of quantity may be mass, volume, etc. Furthermore, the digital representation may include, as the characterization parameters, derivatives of the chemical structures of the at least two components (e.g., quantitative ratios of the at least two components). More preferably, the digital representation may indicate, as the characterization parameters, the components, the quantities, and the forms of the components of the potential target formulation. The morphology of a formulation may be suitable for describing a mixture of at least two components. The morphology may be described by a morphological descriptor. Furthermore, the morphology may include a geometric measure describing the result of mixing at least two components of the formulation. The result of mixing at least two components may be indicated by at least one phase present in the formulation and the relationship of at least two phases, if at least two phases are present. The relationship of at least two phases may indicate the type of phase present in the formulation and the arrangement of the at least two phases relative to each other. Furthermore, the digital representation may indicate the arrangement of the two components in the formulation as a characterization parameter. The arrangement may indicate the shape of at least one phase at least partially embedded in another phase, the size of the embedded component / structure, e.g., capsule size, the frequency / density of the embedded component / structure, etc. In one example, a potential target formulation may include two phases, i.e., a hydrophilic phase due to component A and a hydrophobic phase due to component B. The phases may only be partially mixed, for example, with the aid of a mixing agent or by stirring / shaking the formulation.A portion of component A may be incorporated into the component B phase as spherical droplets. Thus, the relationship between at least two phases may indicate the size of the droplets in the component B phase and the frequency of droplet appearance. The morphology may be described using descriptors. The morphology may be described through at least one phase present in the formulation. At least one phase present in the formulation may be described by its dimensions, a shape associated with the phase, an aggregate phase, the components present in the phase, a measure of the amount associated with the components present in the phase, etc.

[0018] Additionally or alternatively, the digital representation can indicate the ingredients, amounts of ingredients, and processing conditions as characterization parameters. The processing conditions can be described by mixing instructions. The mixing instructions can be described by mixing descriptors. The mixing instructions can be indicated by the order of adding at least two ingredients and the mixing conditions. The mixing conditions can include details regarding stirring, temperature, pressure, atmosphere, etc. The mixing instructions significantly affect the physicochemical properties of the formulation, and therefore the biodegradation. For example, adding component C to components A and B with stirring can be different from adding component B to components A and C with stirring, because components A and B may establish different intermolecular interactions with components A and C. Different intermolecular interactions can result in different arrangements of the ingredients and therefore different formulations.

[0019] Additionally or alternatively, the digital representation may further indicate storage conditions as characterization parameters, which may refer to the temperature, pressure, atmosphere, and time interval associated with storage of the formulation.

[0020] In the example of using a solution containing capsules, the surface of the capsules may be degraded first, followed by the interior of the capsules when reached by microorganisms, so the treatment form or treatment conditions are advantageous in determining the formulation specifications. This is because, in such a situation, the form resulting from the treatment conditions is important in determining the portion of the formulation that is degraded. As a result, a target biodegradation rate can be provided based on the order of access to at least two components in the formulation. Thus, the digital representation can indicate the order of access to at least two components in the formulation. Also, the relationship between at least two phases can indicate the order of access to at least two components in the formulation.

[0021] Additionally or alternatively, the digital representation of the formulation may show, as a characterization parameter, a cooperative effect associated with at least two components. The cooperative effect may be synergistic or antagonistic. Comparing the biodegradation rate of a single component with the biodegradation rate of a mixture of components reveals the cooperative effect. In situations where there are three or more components, comparing the biodegradation rate associated with at least two components selected from the three or more components with the biodegradation rate associated with the three or more components in combination further reveals the cooperative effect. In doing so, a more accurate and realistic description of the formulation is achieved.

[0022] Preferably, the digital representation includes as characterization parameters physicochemical properties of the potential target formulation and / or at least one component of the potential target formulation. In particular, the physicochemical properties of the formulation can be quantified by the physicochemical parameters. Preferably, the digital representation directly includes the physicochemical parameters, preferably with reference to a descriptor. In particular, the physicochemical parameters refer to parameters that quantify the physicochemical properties of the formulation and / or its components. In this context, the term "physicochemical properties" refers to the physical and / or chemical properties of the formulation and / or its components. However, the digital representation can also be provided to enable deriving the physicochemical properties, for example, in the form of a formulation descriptor, for example, by providing a representation of a potential target formulation specification, where the respective physicochemical properties are already stored or can be determined, for example, by calculation of the respective descriptor. Preferably, the digital representation refers to at least one of the formulation's recipe, structural formula, brand name, IUPAC name, chemical identifier, and CAS number.

[0023] The potential target formulation specification may also be considered as a starting formulation specification that indicates for which formulations or in which region of the potential formulation space the biodegradability should first be determined in the search for formulation specifications that will result in formulations with the target biodegradability. Generally, the digital representation of the potential target formulation specification can be provided by a user or automatically, for example, according to predetermined rules, or arbitrarily. For example, the user can select a promising potential target formulation specification as a starting point. However, any target formulation specification may be used, or a set of rules may be used, to provide potential target formulation specifications without user intervention. Preferably, the potential target formulation specification is provided based on rules that take into account constraints on the potential target formulation specification space, i.e., the target formulation space.

[0024] Preferably, the physicochemical parameters refer to at least one of composition descriptors, count descriptors, structural fragment lists, fingerprints, graph invariants, 3D descriptors, and / or high-dimensional descriptors representing parameters quantifying the physicochemical properties of the formulation and / or its components. In a preferred embodiment, the descriptors refer to 3D descriptors, in particular quantum chemical descriptors. Furthermore, the inventors have found that molar mass in particular very accurately describes the biodegradability of formulations and / or formulation components. Therefore, it is particularly preferred that the physicochemical parameters include the molar mass of the formulation and / or formulation components. Possible physicochemical parameters are defined in more detail below.

[0025] The compositional descriptor may refer to any of the potential, average molecular weight, polydispersity, charge, spin, boiling point, melting point, enthalpy of fusion, dissociation constant, Hansen parameters, protic, polar and dispersive contributions, Abraham parameters, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, inhibitor constant, mutagenicity, LD50, bioconcentration, toxicity, biodegradation profile, and viscosity.

[0026] The count descriptor may refer to any of the following: sum of atomic electronegativities, sum of atomic polarizabilities, amount of components, ratio of amount of components, number of atoms and non-H atoms, number of H, B, C, N, O, P, S, Hal and heavy atoms, number of H donor and H acceptor atoms, number of bonds, number of non-H or multiple bonds, number of double, triple and aromatic bonds, number of functional groups, ratio of functional groups, sum of bond orders, aromatic ratio, number of rings or cycles, number of unpaired electrons, number of rotatable bonds, rotatable bond fraction, and number of conformers.

[0027] The physicochemical parameters referring to the list of structural fragment descriptors may refer to at least one of a list of molecular fractions, a list of functional groups, a list of bonds, and a list of atoms. The fingerprint descriptors preferably include at least one of a MACCS key, preferably in bit format or total amount format, a Morgan fingerprint and other circular fingerprints, preferably in bit format or total amount format, a topological twist, an atom pair, an infrared spectrum and related spectra, a fingerprint number, a PubChem fingerprint, a substructure fingerprint, and a Klekota-Roth fingerprint. The graph invariance / topology index descriptors preferably include at least one of a topostructural index and a topochemical index.

[0028] In a preferred embodiment, the physicochemical parameters of the formulation are 3D descriptors comprising at least one of the following: total atomic volume, average volume per atom, total atomic area, average area per atom, area of ​​all atoms, average area per atom, solvent accessible surface, dispersion energy, dielectric energy, H donor, H acceptor, polar and non-polar surface area, atomically resolved H donor, H acceptor, polar and non-polar surface area, shape, sphericity, dipole and higher electric moments, polarizability, dielectric energy, proticity, polar and non-polar surface area, orbital energy and orbital gap, ionization energy, electron affinity, hardness, electronegativity, electrophilicity, excitation energy and intensity, infrared and ultraviolet absorption bands, reactivity measurements, redox potential, bond reference point, partial charge, charge surface area, atomic orbital contribution, bond order, atomic radius. In particular, the physicochemical parameters of the formulation preferably refer to 3D descriptors comprising at least one of the following: sum of volume over all atoms, average volume per atom, sum of area over all atoms, average area per atom, solvent accessible surface, dispersion energy, dielectric energy, H donor, H acceptor, polar and / or non-polar surface area, atomically resolved H donor, H acceptor, polar and / or non-polar surface area, shape, sphericity, cone angle, polarizability, dielectric energy, proticity, polar and / or non-polar surface area, excitation energy and intensity, infrared and / or UV absorption bands, reactivity measurements, particle charge and / or charge surface area. Preferably utilized high-dimensional descriptors may include at least one of conformational partition function, solubility, vapor pressure, activity coefficient, diffusion coefficient, partition coefficient, surface activity, rotational constant, moment of inertia, radius of gyration, composition drift of the formulation, density, viscosity, conformer-weighted volume and area, conformer-weighted H donor, H acceptor, protic, polar, and / or non-polar surface area, charge distribution, conformational dipole moment, and molecular refraction. Preferably utilized higher-dimensional descriptors include at least one of solubility, vapor pressure and activity coefficient, surface activity, conformer-weighted H donor, H acceptor, protic, polar, and non-polar surface area, and charge distribution.

[0029] In one embodiment, the physicochemical parameters are determined based on the components of the formulation. For example, the digital representation represents the components of the formulation, and the method further includes classifying the components of the formulation into predetermined component classes, such as solvents, surfactants, pigments, etc. These classes can be predetermined by respective expert users or learned during the training process of the biodegradation model. The physicochemical parameters can then be determined based on the component classes. In particular, the physicochemical parameters for a component class can be derived based on the values ​​of the physicochemical parameters of the components belonging to the component class. For example, a weighted average, a maximum or minimum value, a median, a total amount, etc., can be determined as the physicochemical parameters for each component class. If two or more physicochemical parameters are provided for a component within a component class, this can be performed for each component. Predetermined rules for each class can determine how each physicochemical parameter is derived from the physicochemical parameters of the components within the class. Thus, the physicochemical parameters determined for each class are the physicochemical parameters utilized in the biodegradation model.

[0030] The method further includes providing biodegradation habitats, where the biodegradation habitats indicate habitat descriptor values ​​of habitat descriptors that affect the biodegradation of the compound in the respective habitats. In particular, providing may refer to receiving the biodegradation habitats from a user's input, for example, using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the biodegradation habitats are already stored. Furthermore, providing may also refer to pre-configuring the biodegradation habitats. For example, if the method is used in a very specific situation in which only one specific biodegradation habitat is sensible, the respective biodegradation habitats can be pre-configured and do not need to be provided as specific inputs. Furthermore, providing may also include directly receiving habitat descriptor values ​​of the habitat descriptors from another source, for example, via a network connection, and providing the habitat descriptor values ​​of the received habitat descriptors as the biodegradation habitats. The provided biodegradation habitat may refer to a general habitat, for example, a marine habitat, where the respective habitat descriptor values ​​of the habitat descriptors for this habitat are already stored in the respective storage, where they can be accessed. However, the provided biodegradation habitat may also directly include the respective habitat descriptor values ​​of the biodegradation habitat to provide further specification of the biodegradation habitat. Furthermore, providing the biodegradation habitat may include providing a digital representation of the biodegradation habitat, where the digital representation may indicate the respective habitat descriptor values ​​of the habitat descriptors that affect the biodegradation rate of the formulation in each habitat. In a further preferred embodiment, the habitat is derived from the digital representation of the potential target formulation specification. For example, the biodegradation habitat may be indicated by the phases present in each potential target formulation and the aggregate phase of the formulation. Furthermore, in one embodiment, different habitats may be provided or induced for different components of the potential target formulation. For example, for a particular application, it may be expected that different components will be exposed to different habitats during the formulation's lifetime.

[0031] In general, habitat descriptors indicate the environmental characteristics of a habitat. In particular, the environmental characteristics of a biodegradation habitat can affect biological activity in the respective habitat, for example, the presence, growth, or absence of certain bacteria. Therefore, the environmental characteristics defined by the habitat descriptors also indirectly affect the biodegradation of the formulation in the respective habitat. For example, if a formulation is biodegradable by certain bacteria that require a certain salt concentration, the formulation and / or formulation components will biodegrade quickly in a habitat that provides such a salt concentration, such as a marine habitat, but much more slowly in a habitat that does not have the appropriate salt concentration, such as wastewater.

[0032] Preferably, the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a freshwater lake habitat, a compost habitat, or a soil habitat. In a preferred embodiment, the biodegradation habitat refers to a marine habitat, and the habitat descriptor refers to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to a freshwater lake habitat, and the habitat descriptor refers to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to wastewater, and the habitat descriptor refers to at least one of water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test period, and enzyme environment. In a more preferred embodiment, the biodegradation habitat refers to soil, and the habitat descriptor refers to at least one of temperature, sand content, pH value, water content, nutrient concentration, microbial community, and enzyme environment. In a more preferred embodiment, the biodegradation habitat refers to compost, and the habitat descriptor refers to at least one of temperature, compost activity, pH value, water content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, and enzyme environment. Generally, the habitat can also refer to the habitat of a standard test used to determine the biodegradability of a formulation. For example, standard tests such as those defined by ISO 13432, ISO 14852, ISO 14855, ISO 17556, and OECD 301 also define specific habitats in which biodegradation occurs. Therefore, providing a biodegradation habitat can also include providing one of the standard tests, for example, selecting it via user input, and in this case, the habitat descriptor refers to the test, i.e., the test environment, and thus to specific characteristics of the test habitat. Additionally, a habitat may also be defined by the biodegradability of a reference formulation or other reference chemical, in which case the habitat may be provided by providing a reference and its biodegradability, in which case the reference and its biodegradability represent habitat descriptors.

[0033] The method further includes providing a biodegradation model based on the provided biodegradation habitat. In particular, providing a biodegradation model preferably refers to selecting a biodegradation model based on the provided biodegradation habitat. For example, multiple biodegradation models may be stored in the biodegradation storage, with each biodegradation model trained for one or more different biodegradation habitats. Preferably, each biodegradation model is trained specifically for a different value or range of values ​​of the habitat descriptor values ​​of the biodegradation habitat. Based on the provided biodegradation habitats exhibiting habitat descriptor values, a respective appropriate biodegradation model may then be selected from the multiple biodegradation models. For example, a biodegradation model is appropriate if the exhibited habitat descriptor value is within the range of habitat descriptor values ​​for which the biodegradation model was trained. For example, a respective lookup table may be provided that allows easy comparison between the exhibited habitat descriptor value and the range of descriptor values ​​for which the biodegradation model stored in the storage was trained, so that an appropriate biodegradation model can be directly selected. However, in another embodiment, providing a biodegradation model based on the provided biodegradation habitat may also refer to user selection of a biodegradation model. For example, the user may be provided with a preselection of biodegradation models that refer to the provided biodegradation habitats, and then be allowed to select the respective biodegradation model to be used. Generally, possible stored biodegradation models refer to biodegradation models that have already been parameterized based on respective training data sets for one or more habitats. Since the training data sets used to parameterize the biodegradation models are historical data, as will be described in more detail below, the biodegradation models can be trained, and thus generated, and stored in the respective databases at any time before and after determining the specific biodegradation degree for a particular formulation. However, training, and therefore generation of a biodegradation model, can of course also be performed when it is determined that a specific biodegradation model for a particular habitat is needed, for example.

[0034] In one embodiment, the biodegradation model is parameterized based on a training dataset that includes measured biodegradation in each habitat associated with each formulation in the training dataset. The measured biodegradation may be measured for each habitat using a predetermined biodegradation test method, such as any of the test methods described above. Thus, the biodegradation model represents the measured biodegradation of the training formulation.

[0035] The provided biodegradation model is then adapted to determine the biodegradation degree of the formulation in each biodegradation habitat. In particular, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitat so that the biodegradation degree of the formulation can be determined based on the digital representation. Preferably, the biodegradation model is trained to determine the biodegradation degree based on characterization parameters of the formulation, preferably based on the components of the formulation and the amounts of the components derivable from the digital representation. In addition, at least one of the morphology, processing conditions, and reservoir conditions derivable from the digital representation can also be used as characterization parameters and inputs for the biodegradation model to determine the biodegradation degree. With respect to the components of the formulation, the components themselves can be used as inputs to the biodegradation model. However, derivable parameters for the components can also be used as input characterization parameters, i.e., as characterization parameters input to the biodegradation model. For example, at least one of the chemical structure and physicochemical parameters of the components can be used as input characterization parameters. Furthermore, the amounts of the components provided as input characterization parameters can refer to any of the mass, volume, and ratio of the respective components. For morphology, the input characterization parameters may refer to, for example, morphology descriptors, as already explained above. Processing conditions may refer to at least one of mixing conditions. Storage conditions may refer to at least one of temperature, pressure, atmosphere, and time interval associated with storage of the formulation. The term "to" is herein interpreted as meaning that, when the physicochemical parameters of the formulation are provided as input, the parameterization adapts the biodegradation model, thereby enabling the biodegradation model to provide a biodegradation rate for the habitat. For example, the biodegradation model relates the physicochemical parameters of the formulation of the past digital representation of the formulation specification and the past digital representation of the habitat to the biodegradation rate. This allows the digital representation of the formulation specification to be determined based on the target biodegradation rate. The term "data-driven" is used herein to emphasize that the model is primarily based on the respective data inputs and not, for example, on intuition, personal experience, or knowledge.Preferably, the biodegradation model refers to a machine learning-based model based on known machine learning algorithms, such as neural networks, regression models, classification algorithms, etc. Regression models based on linear regression, random forests, boosted trees, lasso, ridge regression, and MARS algorithms, among others, have been found to be suitable for most related applications, while random forests, logistic regression, and SVM algorithms, among others, have been found to be suitable for classification models. Generally, the biodegradation model is parameterized during a training process in which the digital representation of the formulation or one or more characterization parameters derived from the digital representation are utilized along with the corresponding biodegradation degree for a specific biodegradation habitat, as described above. Based on such a training dataset, e.g., for a range of values ​​and / or values ​​of a particular habitat descriptor specific to the biodegradation habitat, the respective parameters of the data-driven model can be determined using known training methods, such that the biodegradation model can also determine the biodegradation degree of formulations that are not part of the training dataset.

[0036] Furthermore, in a preferred embodiment, the biodegradation model may also be adapted to determine the biodegradation rate of the formulation further based on habitat descriptor values ​​as input. In particular, the biodegradation model may be trained by utilizing a training dataset, as described above, that includes the characterization parameters of the formulation and / or derivables as described above and the associated biodegradation rate for a specific habitat, resulting in a biodegradation model that indirectly takes the specific habitat into account. However, the training dataset may also optionally include specific habitat descriptor values ​​for each habitat. In this case, the biodegradation model may be provided with the habitat descriptor values ​​as input in addition to the characterization parameters of the formulation and / or derivables as described above, and the biodegradation model may be trained to determine the biodegradation rate further based on the habitat descriptor values. This has the advantage that the biodegradation rate may be determined even more accurately, especially when the biodegradation rate is strongly dependent on the specific habitat descriptor values ​​of the habitat. For example, in marine habitats, the temperature or salt concentration may vary greatly in different regions of the world, and for some formulations, this may result in different biodegradation rates. Therefore, for such cases, it may be advantageous to provide the habitat descriptor values ​​directly as inputs to the biodegradation model. However, instead of providing habitat descriptor values ​​as inputs to the biodegradation model, it is also possible to train two different biodegradation models and indirectly treat different areas as different habitats.

[0037] The method further includes determining the biodegradability of the potential target formulation based on the provided biodegradation model and digital representation. In particular, as described above, the potential target formulation specification, i.e., the digital representation of the provided ingredients and amounts of ingredients of the formulation, can be provided to the biodegradation model as input characterizing parameters. However, additional characterizing parameters, provided by or derived from the digital representation of the potential target formulation specification, can also be used as inputs, as described above. The biodegradation model then provides the biodegradability of the potential target formulation as an output. If the digital representation does not directly include the characterizing parameters, determining the biodegradability can also include first determining the characterizing parameters, for example, as described above. The characterizing parameters thus determined can then be provided as inputs to the biodegradation model.

[0038] The determination of biodegradation using a biodegradation model can be considered a virtual measurement of biodegradation. In particular, the biodegradation model is based on measurement data, e.g., the measured biodegradation of the formulation used to train the biodegradation model. Thus, the biodegradation model includes information provided by these previous measurements. Furthermore, physicochemical parameters may also refer to measured properties of the formulation in some cases. Thus, the biodegradation of a new formulation determined using a biodegradation model can also be considered to be based at least in part on measurement results.

[0039] In the following step, the determined biodegradability of the potential target formulation is compared with the target biodegradability. Based on this comparison, it is determined whether the potential target formulation is determined as the target formulation and whether the potential target formulation specification is determined as the target formulation specification, in which case the iterations can be stopped at this point. Furthermore, based on the comparison, it can also be determined to provide a new potential target formulation specification for a new potential target formulation and repeat the determination of the biodegradability using the new potential target formulation specification for the new potential target formulation. Thus, at this point, an iteration is performed in which the determination of the biodegradability using the biodegradation model and the characterization parameters for the potential target formulations is repeated until one of the potential target formulations is determined as the target formulation. In particular, the comparison can include determining whether the determined biodegradability of the potential target formulation is within a predetermined range around the target biodegradability, in which case the target can be considered met and the potential target formulation is determined as the target formulation. If the determined biodegradability falls outside a predetermined range around the target biodegradability, the target is determined to be not met, and new potential target formulation specifications are provided for new potential target formulations that may meet the target biodegradability.

[0040] In general, the iterations performed may refer to any exploration or directed exploration of the potential target formulation space. For example, new potential target formulation specifications or new potential target formulations can be simply selected arbitrarily from a large number of potential target formulations generated in silico. However, specific rules for generating new potential target formulations, and therefore new potential target formulation specifications, can also be applied based on a comparison between the determined biodegradability and the potential target formulation, with or without considering the simultaneous optimization of additional target properties of the formulation. For example, the components can be modified or changed. Furthermore, not only the components but also interactions between components that may be essential to the formulation can be optimized. The components of a formulation are usually widely known materials whose new combinations provide the effects for which the formulation is known. Therefore, in many cases, optimizing only the components cannot provide the changes necessary to meet the target biodegradability. Therefore, instead of designing new components for a formulation using the means disclosed herein, the formulation specifications can instead be modified to reach the target biodegradability. Doing so saves resources related to the preparation of new materials and allows standard chemicals to be combined more efficiently. Generally, known methods for generating new potential target formulations and / or new potential target preparation formulations can be utilized, for example, evolutionary algorithms or Bayesian optimizer programs can be used.

[0041] Iterations can then be performed over steps to determine the biodegradation rate of the new potential target formulation by utilizing the characterization parameters for the biodegradation habitat and the new potential target formulation, as described above. Optionally, determining characterization parameters from the digital description of the new potential target formulation can also be part of the iteration if characterization parameters are not already provided in the digital description of the new potential target formulation. Furthermore, it is preferred that the same biodegradation model be used in all iteration steps to determine the biodegradation rate. However, in some cases, different biodegradation models can also be used in different iteration steps. For example, if other characterization parameters for the new potential target formulation are utilized, a different biodegradation model may be more appropriate.

[0042] After the iterations have stopped, e.g., after a potential target formulation has been determined as the target formulation, or if a new potential target formulation has not been selected or generated, the results of the iterations may be provided to the user. For example, if none of the potential target formulations met the target biodegradability, the user may be notified that the target formulation determination failed. If the target formulation can be determined, the target formulation may be provided to the user as output. For example, the determined target formulation and target formulation specification may then be provided to an output unit or a calculation unit for further processing. Preferably, providing the target formulation specification and target formulation leads to further processing utilizing the target formulation specification.

[0043] Preferably, processing the target formulation specification comprises determining control signals for controlling a manufacturing process based on the determined target formulation specification. Preferably, the manufacturing process refers to a manufacturing process of the target formulation utilizing the target formulation specification. Furthermore, it is preferred that the target formulation specification references a machine-executable formulation specification of the target formulation, such that the control signals can directly reference the control of the respective laboratory or process equipment that enables the formulation specification to be executed to produce the formulation. In one embodiment, providing the target formulation specification for the target formulation comprises providing control signals adapted to control an industrial plant to manufacture the target formulation according to the target formulation specification.

[0044] In a preferred embodiment, the digital representation of the potential target formulation specification indicates the components of the potential target formulation, the biodegradation rate is determined for each component individually, the overall biodegradation rate of the formulation is determined based on the determined biodegradation rates of the components, and then the overall biodegradation rate is compared to the target biodegradation rate. Preferably, the overall biodegradation rate is set to the biodegradation rate of the component formulation with the lowest biodegradation rate. However, the overall biodegradation rate can also be determined based on other predetermined rules, such as the average biodegradation rate of all components, and the digital representation can further indicate the amount of components in the formulation. In this case, the overall biodegradation rate can be further determined based on the amount, such as a weighted average, with the weight determined by the amount. Furthermore, in one embodiment, the biodegradation rate of each component can be compared individually with the target biodegradation rate, and respective rules can be used to determine under what conditions the target biodegradation rate is met. For example, the rules can determine that the biodegradation rate of all components must meet the target biodegradation rate, or that only some components must meet the biodegradation rate. In particular, a target biodegradation rate may be provided specifically for at least some of the components such that the formulation will meet the target biodegradation rate if the specified biodegradation rate is met by each component. Generally, in these embodiments, the biodegradation of each component may be determined as already described above. For example, the same biodegradation model may be utilized for each component, and each characterization parameter of the component may be provided as an input to the respective biodegradation model. However, different biodegradation models may also be used for different components, e.g., biodegradation models specifically trained for each component.

[0045] In one embodiment, a target application for the formulation is further provided, indicating the intended application of the target formulation, and a biodegradation habitat is provided based on the target application. The target application for the formulation may indicate the intended application context of the formulation, for example, if the formulation is intended to be used as a coating, in personal care products, in laundry detergents, in lubricants, or in product packaging. Such a target application indicates a specific biodegradation habitat. For example, with respect to product packaging, it may be interesting if the formulation biodegrades in compost. In another example, if the target application indicates the use of the formulation in personal care products, it is highly likely that the formulation will sooner or later be found in an aquatic environment. Thus, each target application indicates a respective biodegradation habitat. In this regard, a predetermined list can be provided on a storage device in which each target application and its corresponding biodegradation habitat are stored. The target application for the formulation can then be provided, for example, by providing a list of target applications to a user and allowing the user to select each target application, with each target application being associated with one or more biodegradation habitats. A target formulation can then be determined for each biodegradation habitat with which the target application is associated, or again, the user can select each biodegradation habitat associated with the target application. Additionally or alternatively, information can be provided that indicates the formulation's intended end-of-life treatment. For example, the end-of-life treatment can indicate whether the formulation is intended to biodegrade in a particular environment or whether it should be subjected to a particular treatment, for example, in a bioreactor. Thus, the intended end-of-life treatment information can also be utilized to determine a biodegradation habitat for the formulation, as described above.

[0046] In one embodiment, further information indicating the accessible surface area of ​​the formulation in its intended form is provided, and the biodegradation model is further trained to determine the biodegradation rate based on the accessible surface area, and the method further comprises determining the biodegradation rate based on the accessible surface area. For example, the information can indicate whether the intended product is provided in a solid, crushed, foamed, pelleted, or any other form. Preferably, the information indicates the surface area of ​​the product per mass or the geometric shape of the smallest independent part of the product. Generally, the biodegradation rate of a formulation is an inherent property of the formulation, but the precise timing of biodegradation of a product containing the formulation may also depend, for example, on the surface area accessible by microbial components of the habitat involved in biodegradation. Therefore, further determining the biodegradation rate based on the surface area of ​​a product containing the formulation can improve the accuracy of predicting the biodegradation rate of the final product, thereby also improving the accuracy of determining a target formulation suitable for the final product.

[0047] In one embodiment, target technical application properties of the target formulation are provided, and potential target formulation specifications are provided based on the provided target technical application properties so that potential target formulations meet the provided target technical application properties. In particular, the technical application properties may refer to any property of the formulation and / or a substance at least partially comprising the formulation, thereby enabling the technical applicability of each provided formulation to be evaluated after preparation. Preferably, the technical application properties include at least one of mechanical properties, optical properties, physicochemical properties, chemical properties, and biological properties. Generally, mechanical properties can refer to any of adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, tear, tear strength, rebound, compressibility, abrasion, leakage, morphology, tactile properties, stress at break, elongation at break, particle size distribution, and packing. Optical properties can generally include any of color, turbidity, opacity, gloss, reflection, appearance, absorption, scattering, color intensity, cloud point, matteness, optical density, spectrum, and refractive index. Furthermore, the physicochemical properties can refer to any of density, viscosity, K value, molar weight, dispersity, molar mass distribution, particle size distribution, solubility, partition coefficient, interfacial properties, surface tension, dispersibility, storage stability, odor, segregation, coagulation, electrical conductivity, capacitance, surface area, flow time, vapor pressure, VOC, solids content, hygroscopicity, magnetism, miscibility, thixotropy, phase transition properties, glass transition temperature, corrosion inhibition, solvent separation, aggregation, self-heating ability, impact sensitivity, loss on drying, response angle, electrostatic charge, minimum film formation temperature, and charge density. Chemical properties can refer to the number of functional groups, atom type count, functional group density, atom type density, chemical resistance, reaction timing, demold time, growth, hard / soft segment content, crystallinity, reaction temperature, reaction pressure, decomposition, thermal decomposition, photodecomposition, acidity, pK aThe biological properties may include any of the following: pH, moisture / water content, flammability, burning rate, spontaneous combustion, flash point, flammable gas production, fire response, deflagration rate, residual monomer count, by-product production, salinity, heat resistance, oxidation properties, reduction properties, reactivity, ash content, non-volatile matter content, stability, chelating capacity, calorific value, and saponification value. Furthermore, the biological properties may include any of the following: biodegradability, biological resistance, toxicity, biotransformation, ecotoxicology, sensitization, bacterial count, enzyme activity, environmental distribution, bioaccumulation, and biological exposure. In a preferred embodiment, the technical application property may further refer to biodegradability, e.g., biodegradability in another habitat. For example, a first target biodegradability may refer to a marine habitat, and a second target biodegradability, i.e., the technical application property in this case, may refer to wastewater.

[0048] Then, potential target formulation specifications are provided so that the associated potential target formulations satisfy the provided target technical application characteristics. For example, a database in which formulations and corresponding technical application characteristics are already stored can be utilized, and a target formulation and associated formulation specification that satisfies the provided target technical application characteristics can be selected from the database. In general, formulations that satisfy the target technical application characteristics can be considered to form a potential target formulation space that can be explored during an iterative process to find a target formulation. Then, a first potential target formulation, and therefore a first potential target formulation specification, can be selected from the selected target formulations that satisfy the target technical application characteristics.

[0049] In one embodiment, providing a new potential target formulation specification is based on modifying the provided target application characteristics and providing a new potential target formulation specification so that the potential target formulation satisfies the modified target application characteristics. In particular, if a new potential target formulation specification must be provided, a comparison of the determined biodegradability with the target biodegradability indicates that the determined biodegradability of the current potential target formulation does not satisfy the target biodegradability. In such cases, a new potential target formulation specification, and thus a new potential target formulation, can be provided if such a respective formulation exists, so that the new potential target formulation still satisfies the target technical application characteristics. However, in many cases, it is not possible to provide such a new potential target formulation, or it may not be technically prudent to provide such a new potential target formulation that still satisfies the target technical application characteristics. In these cases, it is advantageous to utilize a less strict target technical application characteristic, e.g., modifying the target technical application characteristic to refer to a range of values ​​instead of one specific value, or, if referring to a range of values, to refer to a wider range of values. New potential target formulations can then be selected or generated to meet the revised target application characteristics.

[0050] In one embodiment, providing potential target formulation specifications based on the provided target technical application properties includes utilizing a decision model adapted to determine the technical application properties of the formulation based on a digital representation of the formulation, the decision model being a data-driven model parameterized to determine the technical application properties associated with the formulation based on the digital representation including characterization parameters of the formulation. The decision model may refer to any known data-driven decision model that allows for determining technical application properties based on a digital representation of the formulation including characterization parameters. In general, the decision model preferably follows the same principles as those described above with respect to the biodegradation model. In fact, the decision model may be based on or utilize the same machine learning algorithms and training methods, only utilizing different training data, i.e., training data including other respective technical application properties of the formulation instead of biodegradability. Thus, all of the embodiments described above with respect to the biodegradation model may also be implemented with respect to a decision model for determining technical application properties. Using such a decision model has the advantage that iterations can be performed in a fast and computationally inexpensive manner not only for the biodegradability of the formulation but also for one or more additional technical application properties, thereby resulting in a target formulation that satisfies not only the target biodegradability but also one or more additional target technical application properties.

[0051] In one embodiment, habitat descriptor values ​​for the habitat descriptors are stored in association with respective geographic locations, and providing a biodegradation habitat refers to providing the geographic location of the habitat and retrieving the habitat descriptor value for that geographic location from storage. The geographic location may refer to, for example, coordinates or other area identifiers. For example, the geographic location may refer to a city name, a country name, a country region name, a sea area name, a geographic feature, etc. Based on such geographic location, each habitat and / or habitat descriptor, for example, an average value or a minimum and maximum value of the habitat descriptor, may be stored. Thus, by providing a geographic location, each habitat descriptor value for that geographic location may be provided. An advantage of this is that the user does not need to know the exact habitat or the exact habitat descriptor value of a certain area. Thus, the user can simply provide a location where the target formulation is expected to biodegrade in that area.

[0052] In one embodiment, the characterization parameters indicated by the digital representation of the formulation refer to at least one of recipe parameters from the preparation, compositional descriptors, count descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors, and / or higher dimensional descriptors that indicate the chemical properties of the formulation and / or its components. Each association between the digital representation and, for example, previously calculated characterization parameters or further information about the formulation may already be stored and associated with the respective digital representation. For example, if the digital representation references a brand name, the components corresponding to the brand name, their respective structural formulas, amounts, and / or physicochemical parameters may already be stored, for example, in the brand name owner's storage.

[0053] In a further aspect, an interface method for providing an interface is presented, the interface method comprising: a) receiving as input via a user interface a target biodegradation rate, a digital representation, and a habitat, and providing the received target biodegradation rate, digital representation, and habitat to a processor executing the method; and b) providing as a result a target formulation specification for the formulation, the result being received from the processor executing the method.

[0054] In a further aspect, a computer-implemented training method for training a data-driven biodegradation model for parameterizing the biodegradation model is presented, the training method comprising: a) providing training data related to a predetermined biodegradation habitat, the training data including: i) digital representations of a plurality of training formulations, and ii) biodegradation degrees for each biodegradation habitat associated with each training formulation; b) providing a data-driven trainable biodegradation model; c) training the provided data-driven biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine biodegradation degrees of formulations based on the digital representations of the formulations; and d) providing a trained biodegradation model.

[0055] In a further aspect, an apparatus for determining a target formulation specification indicative of a target formulation comprising a target biodegradation degree is provided, the apparatus comprising: a) a target biodegradation degree providing unit for providing a target biodegradation degree, the biodegradation degree indicating a biodegradation characteristic of the formulation; b) a digital representation providing unit for providing a digital representation of a potential target formulation specification of a potential target formulation; c) a habitat providing unit for providing a biodegradation habitat, the biodegradation habitat indicating habitat descriptor values ​​of habitat descriptors that affect the biodegradation of the formulation in each habitat, the habitat descriptors indicating environmental characteristics of the habitat; and d) a model providing unit for providing a biodegradation model based on the provided biodegradation habitat, the biodegradation model indicating a biodegradation characteristic of each biodegradation. e) a biodegradation determination unit for determining the biodegradation degree of a potential target formulation based on the selected biodegradation model and the digital representation; and f) a repetition control unit for comparing the determined biodegradation degree of the potential target formulation with the target biodegradation degree, and based on the comparison, either i) determine the potential target formulation as the target formulation and determine the potential target formulation specification as the target formulation specification, or ii) provide a new potential target formulation specification for the potential target formulation and repeat the determination of the biodegradation degree using the new potential target formulation specification for the potential target formulation.

[0056] In a further aspect, an interface device for providing an interface is presented, the interface device comprising: a) an input interface unit for receiving a target biodegradability, a starting digital representation, and a habitat as input via a user interface and providing the received target biodegradability, starting digital representation, and habitat to the above-mentioned device; and b) a result interface for providing a habitat descriptor value of the formulation as a result, the result being received from the above-mentioned device.

[0057] In a further aspect, a training device for training a data-driven based biodegradation model for parameterizing the biodegradation model is presented, the training device comprising: a) a training data providing unit for providing training data related to a predetermined biodegradation habitat, the training data including i) digital representations of a plurality of training formulations and ii) biodegradation degrees for each biodegradation habitat associated with each training formulation; b) a trainable model providing unit for providing a data-driven based trainable biodegradation model; c) a training unit for training the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine biodegradation degrees of the formulations based on the digital representations; and d) a trained model providing unit for providing a trained biodegradation model.

[0058] In a further aspect of the present invention, the use of the method as described above is provided, wherein the method is used to determine target formulations comprising target biodegradability for any of: i) formulations comprising polyesters, in particular formulations used in mulch film and packaging applications, such as aromatic-aliphatic copolyesters; ii) formulations comprising polyalkoxylates, in particular formulations used in home care and personal care applications; iii) formulations comprising polyurethane dispersions; iv) formulations used in aroma applications; v) formulations used in paper coatings for packaging applications based on multi-layer blends; and vi) formulations comprising polyurethanes used in adhesives.

[0059] In a further aspect of the present invention, a system is provided, the system comprising: i) a control signal comprising a formulation formulation specification indicating one or more ingredients for producing the formulation, the control signal being generated according to the method described above; and ii) one or more ingredients indicated by the formulation specification in the control signal.

[0060] A further aspect of the present invention provides the use of control signals generated according to the above-described method for controlling manufacturing processes, particularly manufacturing processes involving the production of formulations.

[0061] In a further aspect of the present invention, a control signal is provided, the control signal being generated in accordance with the method described above. Preferably, the control signal comprises a machine-executable formulation specification for producing a target formulation.

[0062] In a further aspect, a computer program product for determining a target formulation comprising a target biodegradability is presented, the computer program product comprising program code means for causing the above-mentioned apparatus to perform the above-mentioned method.

[0063] In a further aspect, a computer program product for training a biodegradation model is presented, the computer program product comprising program code means for causing the above-mentioned apparatus to perform the above-mentioned method.

[0064] It is to be understood that the above-mentioned method, the above-mentioned device and the above-mentioned computer program product have similar and / or identical preferred embodiments, in particular those defined in the dependent claims. Furthermore, the above-mentioned training method, the above-mentioned training device and the above-mentioned training computer program product also have similar and / or preferred embodiments, in particular those defined in the dependent claims.

[0065] It should be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or the above-mentioned embodiments with the respective independent claims.

[0066] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

[0067] BRIEF DESCRIPTION OF THE DRAWINGS The drawings are as follows: [Brief explanation of the drawings]

[0068] [Figure 1] 1 illustrates, in a schematic and exemplary manner, one embodiment of a system comprising an apparatus for determining a target formulation specification indicative of a target formulation including a target biodegradability. [Figure 2] 1 shows, by way of example only, a flow chart of a method for determining target formulation specifications indicative of a target formulation including a target biodegradability. [Figure 3] 1 shows, schematically and exemplarily, a flow chart of a method for training a biodegradation model for determining the biodegradation rate of a formulation. [Figure 4] 1 shows, by way of example only, a flow chart of a preferred and more detailed embodiment of a method for determining target formulation specifications indicative of a target formulation including a target biodegradability. [Figure 5] 1 shows, by way of example only, a flow chart of a preferred and more detailed embodiment of a method for determining target formulation specifications indicative of a target formulation including a target biodegradability. [Figure 6] 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture for a system and apparatus for determining target formulation specifications indicative of a target formulation including a target biodegradability. [Figure 7] 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture for a system and apparatus for determining target formulation specifications indicative of a target formulation including a target biodegradability. [Figure 8] 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture for a system and apparatus for determining target formulation specifications indicative of a target formulation including a target biodegradability. DETAILED DESCRIPTION OF THE INVENTION

[0069] Detailed Description of the Embodiments 1 illustrates, in a schematic and exemplary manner, one embodiment of a system 100 comprising an apparatus 110 for determining a target formulation specification indicative of a target formulation having a target biodegradation degree. Additionally, the system 100 includes a training apparatus 130 for training a biodegradation model utilized in the apparatus 110, a database 140 capable of storing results of the determination of the target formulation specification, and a manufacturing system 120 for producing a product, in particular a product comprising the determined target formulation that can be controlled utilizing the determined target formulation specification.

[0070] The apparatus 110 comprises a target biodegradability providing unit 111, a digital representation providing unit 112, a habitat providing unit 113, a model providing unit 114, a biodegradability determining unit 115, an iterative control unit 116, and optionally an output and / or control unit 117 that may be adapted to output the determined target preparation specifications and / or to provide control signals for controlling the manufacturing process of the manufacturing system 120 based on the determined preparation specifications.

[0071] The target biodegradability providing unit 111 is adapted to provide a target biodegradability indicating a desired biodegradation property of the formulation. The target biodegradability providing unit 111 can, for example, refer to an input unit through which a user can input the respective target biodegradability. Furthermore, the target biodegradability providing unit 111 can refer to or be part of a user interface that allows a user to interact with the device 110 for providing the target biodegradability. However, the target biodegradability providing unit 111 can also, for example, refer to or be communicatively coupled to a storage unit in which a target biodegradability for a particular application is already stored.

[0072] The digital representation providing unit 112 is adapted to provide digital representations showing potential target formulation specifications for potential target formulations. The digital representation providing unit 112 can, for example, refer to an input unit through which a user can input the respective digital representation. Furthermore, the digital representation providing unit 112 can refer to or be part of a user interface that allows a user to interact with the device 110 and / or the database 140. However, the digital representation providing unit 112 can also refer to or be communicatively coupled to a storage unit in which the digital representations of the formulations are already stored. Generally, the digital representation can directly include characterizing parameters of the formulation, such as ingredients and amounts of ingredients and / or physicochemical parameters of each formulation and / or formulation's components. However, instead of directly providing the characterizing parameters, it is also possible to provide only the formulation's formulation specifications. In this case, the digital representation providing unit 112 is preferably further adapted to determine the characterizing parameters from the formulation specifications. In particular, the digital representation providing unit 112 can be adapted to determine the characterizing parameters, for example, by accessing a database of the most relevant characterizing parameters of the formulation. The digital representation providing unit 112 is then adapted to provide the digital representation comprising, for example, the characterization parameters to the biodegradation determining unit 115 .

[0073] The habitat providing unit 113 is adapted to provide a biodegradation habitat. The habitat providing unit 113 may, for example, refer to an input unit through which a user can input a respective biodegradation habitat. For example, a user interface may be provided that allows the user to select from a plurality of predefined biodegradation habitats. In a preferred embodiment, the habitat providing unit 113 may be communicatively coupled to or refer to a user interface that allows indicating a geographic location, for example, by marking a location on a map, by indicating coordinates, or by providing a region name such as a political or geological region, and the habitat providing unit may then be adapted to provide a biodegradation habitat based on the geographic location. For example, if the geographic location indicates a particular ocean region such as the North Sea or the Atlantic Ocean, the habitat providing unit may be adapted to determine a marine habitat as the biodegradation habitat.

[0074] Generally, the biodegradation habitats indicate habitat descriptor values ​​of habitat descriptors that affect the biodegradation of the compound in the respective habitat. In particular, the habitat descriptors indicate environmental characteristics of the habitat, for example, in a marine habitat, the salt concentration may strongly affect the biodegradation of the compound in the marine habitat. Typical specific habitat descriptor values ​​for each habitat may be stored in the database. However, for example, if it is known that the habitat descriptor values ​​for each habitat deviate from the typical habitat descriptor values, the user may also input the respective specific habitat descriptor values.

[0075] The model providing unit 114 is adapted to provide a biodegradation model based on the provided biodegradation habitat. In particular, the model providing unit 114 is preferably adapted to select a biodegradation model from a plurality of biodegradation models already stored in the database. For example, the biodegradation model may be trained on training data corresponding to one or more specific biodegradation habitats. These specific biodegradation habitats may be defined with respect to specific habitat descriptor values ​​or ranges of values ​​that define for which biodegradation habitat each biodegradation model is suitable. For example, a look-up table may be provided that allows the model providing unit to select which biodegradation model is suitable based on the biodegradation habitat, e.g., based on the habitat descriptor values ​​of the biodegradation habitat. However, the model providing unit 114 may also comprise or point to an input unit, through which a biodegradation model may be provided, e.g., by user selection or user input indicating which biodegradation model should be used.

[0076] The biodegradation model is a data-driven model parameterized to determine the biodegradability of the formulation based on the digital representation, in particular the formulation's characterization parameters, biodegradability. Optionally, the biodegradation model can also be trained to further utilize the provided habitat descriptor values ​​as input. In a preferred embodiment, the data-driven model refers to a machine learning model that utilizes, for example, a regression model-based algorithm or a classifier model-based algorithm. The regression model-based algorithm may be based on any of a neural network algorithm, a linear regression algorithm, a LASSO algorithm, a ridge regression algorithm, a MARS algorithm, a random forest algorithm, and a boosted tree algorithm. The classifier model algorithm may be based on any of a random forest algorithm, a logistic regression algorithm, and an SVM algorithm. The inventors have found that for most applications, linear regression, random forest, and MARS-based algorithms, in particular, are suitable.

[0077] The biodegradation model can be trained, for example, using a training device 130. In particular, the training device 130 includes a training data providing unit 131 for providing training data for training the data-driven biodegradation model. The training data includes a) digital representations of a plurality of training formulations and b) biodegradability associated with each training formulation for one or more different habitats. Optionally, the training dataset may further include habitat descriptor values ​​for the specific habitats for which the respective biodegradability of the formulations has been determined. Preferably, the biodegradability provided for each training formulation in the training data refers to biodegradability measured according to the same measurement method. However, biodegradability may also be provided for different measurement methods, in which case it is preferably clearly indicated which biodegradability is associated with which measurement method so that the biodegradation model can be trained to distinguish between the different measurement methods. Generally, the training data can be designed to cover a predetermined habitat space for the biodegradation model to be trained, the habitat space being defined by the range of values ​​of each habitat descriptor for which the biodegradation model is trained. For example, the training data can be designed to cover predetermined formulation types for predetermined habitats. Known methods for designing and optimizing training data for a given habitat space can be used to ensure that the habitat space is adequately covered by the training data and that random outliers are avoided.

[0078] Furthermore, the training device 130 includes a model providing unit 132 adapted to provide a data-driven, trainable biodegradation model, e.g., a biodegradation model including parameters that can be set during a training process to train the biodegradation model. For example, the trainable biodegradation model may be already stored in a storage unit accessible by the model providing unit 132 for providing it. Furthermore, the training device 130 includes a training unit 133 for training the provided data-driven, biodegradation model based on the provided training data. In particular, training may refer to varying parameters of the biodegradation model based on the respective training data until the biodegradation model is adapted to determine the biodegradation degree of the formulation based on the digital representation, in particular the characterization parameters. Generally, any known training algorithm for training a data-driven, in particular machine learning-based, model may be utilized. Preferably, during the training of the biodegradation model, the characterization parameters of the formulation that most affect the biodegradation degree in each habitat are also determined, and then the model is trained based on these most influential characterization parameters. To determine these most influential characterization parameters, for example, a cluster analysis or PCA analysis tool may be utilized. In particular, the characterization parameters can be used to determine the application space of the training data, where the application space is then defined by the characterization parameters of the formulation and the habitat descriptors covered by the data. Determining the most influential characterization parameters and / or habitat descriptors can then be performed as a dimensionality reduction of the application space. An algorithm can then be applied to optimize the training data within the application space, for example, to cover the application space with as little training data as possible.

[0079] The training device 130 then comprises a trained model providing unit 134 adapted to provide the trained biodegradation model, for example to a storage unit in which trained biodegradation models for different habitats and / or different types of formulations and / or characterization parameters, respectively, are stored. However, the trained model providing unit 134 may also be adapted to provide the trained biodegradation model directly, for example to the biodegradation model providing unit 114 of the device 110.

[0080] In all cases, the biodegradation model providing unit 114 is then adapted to provide a suitable trained biodegradation model to the biodegradation degree determining unit 115. The biodegradation degree determining unit 115 can then utilize the biodegradation model and the provided digital representation to determine the degree of biodegradation. In particular, the biodegradation degree determining unit 115 can be adapted to utilize the characterization parameters indicated by the digital representation as input to the biodegradation model, which has already been trained as described above, and then provide as output a determination of the degree of biodegradation (in relation to which the biodegradation model has been trained).

[0081] The apparatus further includes an iterative control unit 116 adapted to control the iterative process for determining the target formulation specification. In particular, the iterative control unit 116 is adapted to compare the determined biodegradability of the potential target formulation with the target biodegradability. Based on this comparison, the iterative control unit 116 is then adapted to determine whether further iterative steps are necessary to determine the target formulation specification, or whether the iteration has reached an end, in particular whether the potential target formulation can be set as the target formulation, and thus the potential target formulation specification, as the target formulation specification. Preferably, the comparison of the determined biodegradability of the potential target formulation with the target biodegradability refers to determining whether the determined biodegradability is within a predetermined range around the biodegradability, for example, by determining whether the difference between the determined biodegradability and the target biodegradability is less than a predetermined threshold. However, the comparison may also refer to a more complex mathematical function, and the condition for determining a potential target formulation as a target formulation may refer to any condition based on the comparison of the determined biodegradability with the target biodegradability. Generally, if a predetermined condition is met, e.g., if the determined biodegradability is within a predetermined range around the target biodegradability, the iteration control unit 116 determines that the potential target formulation is the target formulation and the potential target formulation specification is the target formulation specification, and terminates the iteration.

[0082] If the above conditions are not met, for example, if the determined biodegradability is not within a predetermined range around the target biodegradability, the iteration control unit 116 is adapted to determine that a further iteration step is necessary. In this case, the iteration control unit 116 is adapted to provide a new potential target formulation specification for the potential target formulation and repeat the determination of the biodegradability using the new potential target formulation specification for the new potential target formulation. For example, the new potential target formulation specification can be provided in a database in which multiple potential target formulation specifications are already stored, from which the iteration control unit 116 can select a new potential target formulation specification arbitrarily or according to a predetermined rule. Such a rule can, for example, be a function of a comparison of the determined biodegradability with the target biodegradability of the potential target formulation. For example, the function can refer to the size of the difference between the determined biodegradability and the target biodegradability; the smaller the difference, the more similar the portions of the new potential target formulation are in the potential target formulation. In such a case, these rules may cause the iterative control unit 116 to be adapted to select a new potential target formulation that is more similar to the potential target formulation if the determined biodegradability of the potential target formulation is already similar to the target formulation, or less similar if the difference between the determined biodegradability and the target biodegradability is large. However, completely different rules may also be applied. Furthermore, the iterative control unit 116 may also be adapted to generate a new potential target formulation specification, for example, based on the potential target formulation specification and predetermined rules, or arbitrarily. Again, the same principles as described above may be applied to the rules.

[0083] Furthermore, the iteration control unit 116 may also be adapted to apply an iteration interruption criterion that indicates failure to find a suitable target formulation specification for the respective target biodegradability. For example, the iteration control unit 116 may be adapted to apply an interruption criterion that refers to a predetermined number of iteration steps, i.e., that refers to determining a predetermined number of new potential target formulation specifications. However, other interruption criteria may also be utilized.

[0084] An output unit, e.g., a display, may then be adapted to output the determined target formulation specification or target formulation in the form of, for example, a visual representation of the formulation, identification information of the formulation, a chemical formula representing the formulation, ingredients and amounts of the formulation, etc. Furthermore, the output unit may additionally or alternatively be adapted to provide the determined target formulation specification to a database 140 for storing each determined target formulation specification in association with a respective target biodegradation degree for future use. Optionally, the apparatus 110 may comprise a control unit 117 adapted to provide a control signal based on the determined target formulation specification to control the manufacturing process of the manufacturing system 120. In particular, the control signal preferably indicates a machine-executable formulation specification for the target formulation to be generated based on the determined target formulation specification in order to produce a target formulation that satisfies the target biodegradation degree. However, the control unit 117 may also be adapted to control the manufacturing process of another product based on the determined target formulation specification, e.g., to provide a control signal indicating a machine-executable formulation specification for another product that utilizes or includes the respective target formulation.

[0085] FIG. 2 shows a schematic and exemplary flowchart of a method for determining a target formulation specification indicative of a target formulation having a target biodegradability. The method 200 includes a first step 210 of providing a target biodegradability. Furthermore, in step 220, digital representations of potential target formulation specifications for potential target formulations are provided. In particular, the provision of the target biodegradability and the digital representation may follow the principles described above with respect to the target biodegradability providing unit 111 and the digital representation providing unit 112, respectively. Furthermore, in step 230, biodegradation habitats are provided, indicating habitat descriptor values ​​of habitat descriptors that affect the biodegradation of the formulation in the respective habitats. For example, the principles described above with respect to the habitat providing unit 113 may also be applied to this step 230. Furthermore, in step 240, a biodegradation model is provided, adapted to determine the biodegradability of the formulation based on the digital representation. As already described in more detail above, providing a biodegradation model may also refer to selecting a biodegradation model based on the provided biodegradation habitat. Furthermore, the biodegradation model is preferably a data-driven model parameterized with respect to the biodegradation habitat so that the biodegradation rate of the formulation can be determined based on the formulation's characterization parameters. Generally, steps 210, 220, 230, and 240 can be performed in any order, or even simultaneously. In a subsequent step 250, the biodegradation rate is determined based on the provided digital representation of the potential target formulation and the biodegradation model. In step 260, the determined biodegradation rate of the potential target formulation is then compared with the target biodegradation rate. Based on this comparison, either the potential target formulation is determined as the target formulation and the potential target formulation specification is determined as the target formulation specification, or a new potential target formulation specification for a new potential target formulation is provided, and the determination of the biodegradation rate is repeated using the new potential target formulation specification. In optional step 270, after the target formulation specification has been determined using the above steps, the determined target formulation specification, together with the determined target formulation and target biodegradation rate, can be provided to a user via an output unit.Furthermore, in step 270, the potential target formulation specification may also be utilized to generate control signals that allow for control of the manufacturing process of a product, e.g., the target formulation or a product containing the target formulation, as already described in detail above.

[0086] FIG. 3 schematically and exemplarily shows a flowchart of a method for training a data-driven biodegradation model, for example, as utilized in the method 200 described with reference to FIG. 2 . In general, the method 300 may be performed by, for example, each unit of the training device 130 as described with reference to FIG. 1 . The method 300 includes a step 310 of providing training data for training the data-driven biodegradation model. The training data includes a) digital representations of a plurality of training formulations and b) biodegradation degrees associated with each training formulation in a respective biodegradation habitat, for example, for a particular habitat descriptor value. Optionally, the training data set may further include each particular habitat descriptor value. In particular, the training data may be provided according to the principles described above with reference to the training data providing unit 131 described with reference to FIG. 1 . The method further includes a step 320 of providing a data-driven trainable biodegradation model, for example, a machine learning-based biodegradation model such as a neural network. In general, steps 310 and 320 may be performed in any order or even simultaneously. The method 300 then further includes step 330 of training the provided data-driven biodegradation model based on the provided training data, e.g., by varying parameters in the data-driven trainable biodegradation model, such that the trained biodegradation model is adapted to determine the biodegradation rate of the formulation based on the digital representation of the formulation. In step 340, the trained biodegradation model may then be provided, e.g., by storing the trained biodegradation model in storage or by directly providing the trained biodegradation model to the device 130, as described with respect to FIG. 1.

[0087] In the following, a more detailed preferred embodiment of the above-mentioned method and the corresponding device will be described. A schematic and exemplary flowchart of an exemplary and preferred embodiment of the method is provided by FIG. 4. In this exemplary embodiment, the method begins by requesting target values ​​for the target application, in particular the target biodegradation rate, for example, via a user interface. Additionally, in a next step, optimization is initiated by providing a potential target formulation specification, i.e., a starting recipe. Optionally, constraints on the recipe, i.e., formulation specification, can be taken into account in this process, for example, if the user provides such constraints. Constraints may refer, for example, to constraints on the manufacture of the formulation, constraints on the starting materials to be used in the synthesis of the formulation, etc. Furthermore, additional application conditions may be requested, in particular indicating the biodegradation habitat of the target formulation. Furthermore, the additional application conditions may also indicate further information about the target formulation that should be satisfied. For example, the requested additional application conditions may refer to geographic locations indicating where the formulation is expected to biodegrade. Based on these geographic locations, biodegradation habitats and respective habitat descriptors can be determined, for example, by utilizing a database in which each associated biodegradation habitat and biodegradation physicochemical parameter is already stored. Based on the above steps, optimization for determining a target formulation, i.e., a target formulation specification, can be initiated. In the first step of optimization, characterization parameter values ​​can be derived from a provided starting recipe, i.e., from a provided potential target formulation specification. However, deriving characterization parameters can also refer to accessing storage in which respective characterization parameter values ​​for each potential target formulation are already stored. Furthermore, if the provided digital representation of the potential target formulation already includes the characterization parameters, this step can be omitted. Based on required additional application conditions, in particular based on the biodegradation habitat, respective decision models, i.e., biodegradation models, can be provided.Based on the provided decision model and the digital representation of the potential target formulation specification, a value of the target application, i.e., biodegradability, of the potential target formulation can be provided. In a next step, it is determined whether the determined performance value, i.e., the determined biodegradability, meets the target value, i.e., the target biodegradability, within a predetermined limit. If not, i.e., if this condition is not met, the formulation of the potential target formulation specification is modified and a new potential target formulation specification is determined, optionally taking into account the previously provided constraints. Then, a new iteration can be started for the potential target formulation specification. If, at a certain point, the determined performance value meets the target value within its limit, i.e., if the respective condition is met, the potential target formulation specification is determined as the target formulation specification and is provided to a user or a control unit, for example, for producing the respective determined target formulation.

[0088] FIG. 5 illustrates a further preferred embodiment of the above-described method for determining a target formulation specification having a predetermined target biodegradability. In this embodiment, in addition to the target biodegradability, it is desirable for the target formulation to also satisfy a further target value, i.e., a target technical application property. The additional target technical application property may refer to any technical application property, such as, for example, an additional biodegradability in another habitat or another technical application property. In general, the method follows the same principles as those described above with reference to FIG. 4. However, due to the additional target value, additional conditions must be met during optimization. Therefore, only the main differences from the method described above will be pointed out below. In particular, in this preferred embodiment, the optimizer module not only optimizes for the first target value, i.e., the target biodegradability, but also for a second target value. Preferably, for the second target value, a decision model adapted to determine the value of the technical application property based on a characterization parameter is also utilized. Therefore, in addition to the method described above for the second target application, a second decision model is provided that enables the application property value to be determined based on a characterization parameter for the second target application. The second determination model can be based on the same algorithm as the biodegradation model, but is simply trained using a different data set to determine another characteristic of the formulation. The comparison then refers to determining not only whether the determined biodegradability meets the target biodegradability within limits, but also whether the determined second application characteristic value meets the target second application characteristic value within limits. A predetermined rule can be used to determine when to continue the iteration. That is, a new formulation is provided as a new potential target formulation specification, and a potential target formulation specification is determined for that condition. For example, the user can predetermine weight values ​​to weight which conditions must be met to what extent. For example, it may be more important for the user that biodegradability be met, but other target application characteristics are less important.In this case, the limits on which the second target application characteristic can be met may be set wider, or the weighting of meeting this condition may be reduced. Also in this regard, Pareto optimization methods may be utilized to find the optimal trade-off between the different objectives. Then, at some point in the iteration, if it is determined that the conditions are met and satisfy the predetermined rules, each potential target formulation specification may be determined as the target formulation specification and may be provided as an output to a user or may be utilized to generate a control file for producing each target formulation.

[0089] FIG. 6 shows a block diagram of an exemplary system architecture of an automated laboratory system 1000 for preparing formulations, including a laboratory equipment control device 1102, a network 1150, a formulation specification (i.e., recipe) module 1100 / 1110, and a client device 1108. The automated laboratory system includes a laboratory equipment control device layer 1152 as part of the laboratory equipment control device 1102, a formulation specification module layer 1154 associated with the formulation specification module, and a remote control or client layer 1156 associated with the client device 1108. The laboratory equipment control device layer can be divided into several hierarchical layers: a hardware layer, a middleware layer, and an interface layer. The hardware layer relates specifically to hardware resources, such as sensors and actuators, for controlling the formulation preparation. The middleware relates to any of the known middleware for laboratory or plant preparation operations. One example is LABS / QM, which provides various abstractions over hardware, networks, and operating systems, such as low-level device control and message passing. The communication layer relates to communication protocols, one of which may be REST, which may be implemented over different transport protocols (i.e., UDP, TCP, telemetry) that allow the exchange of messages between the laboratory equipment control device and the laboratory equipment devices. Such a software architecture makes it possible to control and monitor laboratory equipment without the need to interact with hardware.

[0090] The formulation specification module layer 1154 may include a mass storage layer, a computing layer, and an interface layer. The storage layer is configured to provide mass storage for a data-driven biodegradation model for providing a formulation recipe, i.e., a formulation specification, that meets a target biodegradation level, as described in detail above. In particular, the functions performed by the device as described above may be provided as program code stored in the mass storage. Furthermore, formulation specifications for multiple formulations may be stored in the mass storage. Such data may be stored in a structured database, such as an SQL database, a distributed file system, such as HDFS, or a NoSQL database, such as HBase or MongoDB. The computing layer may include an application layer that enables customization of functions provided by standard cloud services to execute computing processes based on target characteristics. Such functions may include determining a digital representation of a target formulation based on a target biodegradation level and a biodegradation model, generating a formulation specification from the digital representation of the target formulation, and providing the formulation specification as control data to a laboratory equipment control device.

[0091] The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. For communication with laboratory equipment control devices, a REST API is implemented.

[0092] The client layer 1156 provides an interface to an end user. For the end user, the client layer 1156 can execute a client-side web application that provides an interface to the preparation specification module layer 1154 or the laboratory equipment control device layer 1152. The user may be provided with a UI for selecting a target biodegradation level and a biodegradation habitat for the target biodegradation level, which may also include a range of biodegradation values. In other examples, the user may be provided with a UI for selecting multiple target biodegradation levels and their respective values. The application may be configured for a user to remotely monitor and control the laboratory equipment control device and operation. In other examples, the client device layer and preparation specification module layer may be integrated into one device. The alternatives described herein are for illustrative purposes only and should not be considered limiting.

[0093] 7 shows a block diagram of an exemplary system architecture of a system and apparatus for generating a biodegradation model for determining biodegradation having a network 2150 and a model generation module 2100 / 2110, which may be considered as a training model device or may comprise a training model device, a preparation specification module 1100 / 1110, and a client device 2108. The system for generating a biodegradation model includes a model generation module layer 2154 as part of the model generation module and a client layer 2156 associated with the client device 2108.

[0094] The model generation module layer 2154 may include a mass storage layer, a computing layer, and an interface layer. The storage layer is configured to provide mass storage for the data-driven biodegradation model described above. Furthermore, the mass storage is configured to store formulation specifications for the formulation and the measured biodegradation rates of one or more habitats. Such data may be stored in a structured database, such as an SQL database, or a distributed file system, such as HDFS, or a NoSQL database, such as HBase or MongoDB. The computing layer may include an application layer that enables customization of functionality provided by standard cloud services to execute computing processes for generating a biodegradation model for determining the biodegradation rate of the formulation. Such functionality may include receiving, for at least two previously measured formulations, respective digital representations associated with formulation specifications, and at least one measurement data of biodegradation in at least one habitat for each of the at least two previously measured formulations; receiving, in a model generation module, a digital representation of at least one unmeasured formulation; training a model according to the above training principles based on a similarity measure between the digital representations of the at least two previously measured formulations, the measurement data of biodegradation in at least one habitat for each of the at least two previously measured formulations, and preferably between the digital representations associated with the formulation specifications for each of the at least two previously measured formulations and the respective digital representations associated with the formulation specifications for the at least one unmeasured formulation; and providing a biodegradation model via an output interface. The model generation module layer may be configured to deploy the generated model and formulation specification database to the formulation specification module layer. This may include storing the generated model and formulation specification database in a mass storage device associated with the formulation specification module.

[0095] The model generation module layer may further be configured to determine, from the recipe specification, a digital representation of the formulation associated with the recipe specification. The digital representation may include a set of characterization parameters associated with the recipe specification for each measured formulation. One way to derive these characterization parameters may be by applying the SMILES algorithm or any other principle already described above. If the model is generated based on a digital representation derived from a recipe, the relationship between the recipe specification and the characterization parameters may be stored in a mass storage device associated with the model generation module. In such a case, developing the model includes providing that relationship.

[0096] The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. In this example, a REST API is implemented for communication with client devices. The client layer 2156 provides access to a mass storage device containing formulation formulation specifications and at least one biodegradability index for at least two formulations. The client layer also provides an interface to an end user. For end users, the client layer 2156 can execute a client-side web application that provides an interface to the model generation module layer 2154 or to a mass storage device associated with the client layer. The user may be provided with a UI for selecting the test method and / or habitat for which biodegradability is to be determined. The user may also be provided with a UI for selecting formulation specification data. The user interface may also provide options for uploading the selected data to the model generation module layer and, optionally, for starting model generation.

[0097] FIG. 8 illustrates an exemplary system 700 for producing a chemical product based on a formulation specification generated in accordance with the present invention. In this example, the system includes a user interface 710 and a processor 720 associated with a control unit 740. The user interface 710 and the processor 720 may be associated with or implemented in accordance with the principles described above, and may be particularly adapted to execute a computer-implemented method for determining a target formulation and / or formulation specification based on a determined biodegradability, as described above. The control unit 740 may be configured to receive control data generated in accordance with the present invention, for example, as described above, and particularly to receive control data generated based on a formulation formulation specification including a target biodegradability. In this example, the control data is provided from a database 730; however, in other examples, the control data may be provided from a server or any other computing unit for distributing data. Vessels 750 and 752 each contain a component of a chemical product, e.g., an ingredient, a catalyst, etc. Typically, there are three or more vessels; however, in this example, only two are shown for illustrative purposes. Valves 760 and 762 are associated with the vessels 750 and 752. Valves 750 and 752 may be controlled to dose the appropriate amount of each component into reactor 770 according to the formulation specification. Motor 800 of mixer 780 may also be controlled by the control unit according to the formulation specification. Optional heater 790 may also be controlled according to the formulation specification. Finally, outlet valve 810 in fluid communication with the reactor may be controlled by the control unit to provide chemical product to a vessel or testing system 820.

[0098] A more detailed example of a biodegradation model is described below. In this example, the biodegradation model is trained based on a training dataset containing 100 or more data points, including each formulation, including its composition and biodegradability, for example, according to a specific standard, such as OECD 301 af. The composition of a formulation can be defined using the identities of the formulation's chemical components and their amounts. To train or apply the biodegradation model, the formulation's chemical components can then be classified into predetermined or learned component classes. For example, classes can include solvents, surfactants, pigments, etc. For each component in a class, a predetermined descriptor is provided, which can be stored in a respective database or derived as described in more detail above. The descriptor can be a partition coefficient, number of functional groups, pKa value, molar mass distribution, glass transition temperature, etc. The biodegradability of each component according to a specific standard (e.g., OECD 301 af) can also be used as a descriptor. Calculated descriptors, such as calculated polarity, partition coefficient, critical micelle concentration, solubility, etc., can also be used. Furthermore, if the chemical structure of the components is known, molecular fingerprints such as Morgan fingerprints can also be used as descriptors. Descriptors for each class can be standardized within the component class, for example, by z-score normalization. Furthermore, if water is a component used in the formulation, this component can be removed from the component list, and the remaining components can be normalized to 100% by weight. Then, for each descriptor, a descriptor value can be derived for all components from the component class of a given formulation, for example, as the average descriptor value or the minimum or maximum descriptor value. For example, averaging can refer to using the weight fraction of each component in the water-free formulation as a weighting factor. In the case of Morgan count fingerprints, the weighted fingerprints of the individual components of a component class can be summed. The total weight fraction within each component class can also be used as a descriptor. Furthermore, in addition to descriptors, calculated properties of the formulation, such as viscosity and solids content, can be additionally used as inputs for the biodegradation model.Based on these input parameters of the formulation, a random forest algorithm can be trained as a biodegradation model to determine the amount of biodegradation in a habitat, for example, according to a specific standard, such as OECD 301 af. The biodegradation model can be parameterized based on the respective training datasets. Furthermore, the respective test datasets can be used to test and validate the trained biodegradation model. The data for the training datasets and test datasets can be obtained by measuring the biodegradability of each formulation in each habitat in a comparable manner, for example, according to the OECD 301 af standard. Furthermore, existing databases, published datasets, or literature can also be used. An example of published biodegradation data for polymer blends can be found, for example, in "Blends of PBAT with plasticized starch for packaging applications: mechanical properties, rheological behavior can biodegradability," M. Dammak, et al., Industrial Crops & Products, 144, 112061 (2020)."

[0099] In the following, some further details regarding some of the above-mentioned embodiments are provided. Generally, in some applications, it is desirable to find a formulation that meets certain technical application characteristics, such as tensile strength, and also meets requirements regarding biodegradability. For this application, a method such as that described with reference to FIG. 5 is proposed. In an example embodiment of this embodiment suitable for this application, target requirements for biodegradability may be provided, and target application characteristics are also provided. Based on the target application characteristics, a determination model is selected, which preferably associates characterization parameters related to the formulation specification with the application characteristics. In addition, a further model is selected based on the habitat of the formulation. This biodegradation model preferably associates habitat information and characterization parameters related to the formulation specification with the biodegradability. Based on the target application requirements, characterization parameters based on the formulation specification are determined. In an optional step, additional descriptor values ​​related to the habitat are required based on the selected biodegradation model used. Based on the biodegradation model, the biodegradability can be determined. The determined biodegradability is then compared to the target biodegradability. Furthermore, the determination model is used to determine the application characteristics of the formulation, which are compared to the target characteristics. If the determined biodegradability meets the target biodegradability and the determined application properties meet the target properties, formulation specifications are provided. The formulation specifications may also refer to or include control data for controlling the plant that produces the formulation. If the target biodegradability and / or target application properties are not met, the target application properties may be lowered and the process may be re-run with lower target application requirements until the biodegradation requirements can be met. Tolerance ranges for the target application properties may be provided. If no formulation is found that meets the required targets for biodegradability and target application performance, the process may stop and the user may be notified.

[0100] Potential expressions of biodegradability can be one or more of the following: mineralization, which refers to whether a formulation is completely mineralized or the time until mineralization is achieved; biotransformation, which refers to a change in the chemical structure of a formulation that results in the loss of a particular property, such as toxicity, or the time until this is achieved; and half-life, which refers to the time until 50% of the formulation is degraded. The primary habitats are oceans, wastewater, and soil. In the ocean, the following parameters may affect biodegradation: salinity, sediment, water temperature, bacterial culture, etc. In some embodiments, marine habitat descriptors can be stored in a database along with geographic locations. In that case, a geographic location can be entered, and the values ​​of parameters associated with this geographic location can be retrieved from the database. In wastewater, the following parameters may affect biodegradation: temperature, bacterial population, type of bacteria, enzyme concentration, and enzymes. In soil, the following parameters may affect biodegradation: temperature, bacterial population, type of bacteria, enzyme concentration, and enzymes.

[0101] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0102] With respect to the processes and methods disclosed herein, the actions performed in the processes and methods may be performed in different orders. Furthermore, the outlined actions are provided only as examples, and some of the actions may be optional, combined into fewer steps and actions, supplemented with additional actions, or expanded into additional actions, without detracting from the essence of the disclosed embodiments.

[0103] In the claims, the word "comprising" does not exclude other elements or steps and the indefinite article "a" or "an" does not exclude a plurality.

[0104] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0105] The steps performed by one or more units or devices, such as providing a digital representation and a biodegradation model, determining the degree of biodegradation, providing the degree of biodegradation, can be performed by any number of other units or devices, and these steps can be implemented as program code means of a computer program and / or as dedicated hardware.

[0106] The computer program product may be stored on / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, or may be distributed in other forms, for example via the Internet or other wired or wireless telecommunications systems.

[0107] Any unit described herein may be a processing unit that is part of a classical computing system. A processing unit may include a general-purpose processor, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. Any memory may be physical system memory, which may be volatile, nonvolatile, or a combination of both. The term "memory" may include any computer-readable storage medium, such as non-volatile mass storage. If a computing system is distributed, processing and / or storage capabilities may also be distributed. A computing system may include multiple structures as "executable components." The term "executable component" is a structure well understood in the computing field, which may be software, hardware, or a combination thereof. For example, when implemented in software, those skilled in the art will understand that the structure of an executable component may include software objects, routines, methods, etc. that can be executed on a computing system. This may include both executable components in the computing system's heap or executable components on a computer-readable storage medium. The structure of the executable components may reside on a computer-readable medium that, when interpreted by one or more processors of a computing system, e.g., by processor threads, causes the computing system to perform functions. Such structure may be directly computer-readable by a processor, e.g., where the executable components are binary, or may be structured to be interpretable and / or compiled to generate such a binary, e.g., in a single stage or multiple stages, that is directly interpretable by a processor. In other examples, the structure may be hard-coded or hard-wired logic gates implemented exclusively or nearly exclusively in hardware, e.g., in a field programmable gate array (FPGA), application specific integrated circuit (ASIC), or other dedicated circuitry.Thus, the term “executable component” is a term for structures well understood by those skilled in the computing arts, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to operations performed by one or more processing units of a computing system. When such operations are implemented in software, one or more processors direct the operation of the computing system in response to execution of the computer-executable instructions that make up the executable components. A computing system may also include communications channels that enable the computing system to communicate with other computing systems, for example, over a network. A “network” is defined as one or more data links that enable the transmission of electronic data between computing systems and / or modules and / or other electronic devices. When information is transferred or provided to a computing system via a network or another communications connection, e.g., either hardwired, wireless, or a combination of hardwired and wireless, the computing system properly considers the connection to be a carrier medium. A carrier medium may include a network and / or data link that can be used to carry desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose computing system or a special-purpose computing system, or a combination thereof. Although not all computing systems require a user interface, in some embodiments a computing system includes a user interface system for use in interfacing with a user. The user interface serves as an input or output mechanism to the user, for example, via a display.

[0108] Those skilled in the art will appreciate that at least portions of the present invention may be implemented in networked computing environments having many types of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, pagers, routers, switches, data centers, wearable devices such as eyeglasses, etc. The present invention may also be implemented in distributed system environments where local and remote computing systems, linked, for example, through a network, either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0109] Those skilled in the art will also appreciate that at least a portion of the present invention may also be implemented in a cloud computing environment. A cloud computing environment may be distributed, but this is not required. If distributed, a cloud computing environment may be distributed internationally within an organization and / or have components held across multiple organizations. For purposes of this specification and the claims that follow, "cloud computing" is defined as a model that enables on-demand network access to a shared pool of configurable computing resources, such as networks, servers, storage, applications, and services. The definition of "cloud computing" is not limited to any of the many other advantages that may be obtained when such a model is deployed. The computing system in the figures, as described, includes various components or functional blocks that may implement various embodiments disclosed herein. The various components or functional blocks may be implemented on a local computing system or on a distributed computing system that includes elements that reside in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or fewer components than those shown in the figures, and some of the components may be combined where circumstances warrant.

[0110] Any reference signs in the claims should not be construed as limiting the scope.

[0111] The present invention relates to a method for determining a formulation specification including a target biodegradation rate. A target biodegradation rate indicating the biodegradation characteristics of a formulation is provided. A digital representation of a potential target formulation specification is provided, indicating physicochemical properties of the formulation. A habitat is provided, indicating habitat descriptor values ​​for habitat descriptors. A model is provided based on the habitat, adapted to determine the biodegradation rate of the formulation in the habitat. The biodegradation rate of the potential target formulation is determined based on the provided model and digital representation. The determined biodegradation rate is then compared to the target biodegradation rate, and either i) the potential target formulation is determined as the target formulation, or ii) a new potential target formulation specification for the potential target formulation is provided, and the determination of the biodegradation rate is repeated using the new potential target formulation specification.

Claims

1. 1. A computer-implemented method for determining a target formulation specification indicative of a target formulation having a target biodegradability, the method (200) comprising: Providing a target biodegradability (210), the biodegradability indicating the biodegradability characteristics of a potential target formulation; providing (220) a digital representation of potential target formulation specifications for the potential target formulation; providing (230) biodegradation habitats, the biodegradation habitats exhibiting habitat descriptor values ​​for habitat descriptors that affect the biodegradation of the formulation in each habitat, the habitat descriptors indicating environmental characteristics of the habitat; providing (240) a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine the biodegradation rate of the formulation in each biodegradation habitat, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats to determine the biodegradation rate of the formulation based on a digital representation of the formulation; determining (250) the biodegradation rate of the potential target formulation based on the provided biodegradation model and the digital representation; comparing the determined biodegradability of the potential target formulation to the target biodegradability, and based on the comparison, either i) determining the potential target formulation as the target formulation and determining the potential target formulation specification as the target formulation specification, or ii) providing a new potential target formulation specification for the potential target formulation and repeating the determination of the biodegradability using the new potential target formulation specification for the potential target formulation (260); A method comprising:

2. 10. The method of claim 1, further comprising providing target technical application characteristics for the target formulation, and providing the potential target formulation specifications based on the provided target technical application characteristics, such that the potential target formulation satisfies the provided target technical application characteristics.

3. 3. The method of claim 2, wherein the providing of new potential target formulation specifications is based on modifying the provided target application characteristics and providing the new potential target formulation specifications such that the potential target formulation satisfies the modified target application characteristics.

4. 4. The method of claim 2 or 3, wherein the providing of the potential target formulation specifications based on the provided target technical application characteristics comprises utilizing a decision model adapted to determine technical application characteristics of the formulation based on the digital representation of the formulation, wherein the decision model is a data-driven model parameterized to determine the technical application characteristics associated with the formulation based on the digital representation of the formulation.

5. 10. The method of any one of the preceding claims, wherein said providing said target formulation specification for said target formulation comprises providing a control signal adapted to control an industrial plant to produce said target formulation in accordance with said target formulation specification.

6. 10. The method of any one of the preceding claims, wherein the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a freshwater lake habitat, a compost habitat, an anaerobic habitat, or a soil habitat.

7. 7. The method of claim 6, wherein the biodegradation habitat refers to a marine habitat and the habitat descriptors refer to at least one of salinity, sediment type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community.

8. 7. The method of claim 6, wherein the biodegradation habitat refers to soil and the habitat descriptors refer to at least one of temperature, sand content, pH value, moisture content, nutrient concentration, microbial community, and enzyme environment.

9. 10. The method of any one of the preceding claims, wherein habitat descriptor values ​​for said habitat descriptors are stored in association with respective geographic locations, and wherein said providing a biodegradable habitat refers to providing a geographic location of said habitat and retrieving said habitat descriptor values ​​for said geographic locations from storage.

10. 1. An interface method for providing an interface, the interface method comprising: receiving as input via a user interface a target biodegradability, a starting digital representation, and a habitat, and providing said received target biodegradability, digital representation, and said habitat to a processor executing the method (200) of any one of claims 1 to 9; - providing the target formulation specification for the formulation as a result, the result being received from the processor executing the method (200) of any one of claims 1 to 9; An interface method comprising:

11. 1. A computer-implemented training method for training a data-driven biodegradation model to parameterize said biodegradation model, said training method (300) comprising: providing (310) training data associated with a predetermined biodegradation habitat, the training data including: a) a digital representation of a plurality of training formulations; and b) a biodegradation rate for each biodegradation habitat associated with each training formulation; Providing a data-driven, trainable biodegradation model (320); training (330) the provided data-driven biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine a biodegradation rate of the formulation based on the digital representation of the formulation; Providing the trained biodegradation model (340). A computer-implemented training method (300) comprising:

12. 1. An apparatus for determining a target formulation specification indicative of a target formulation having a target biodegradability, said apparatus (110) comprising: a target biodegradability providing unit (111) for providing a target biodegradability, the target biodegradability providing unit (111) indicating the biodegradability characteristics of the potential target formulation; a digital representation providing unit (112) for providing a digital representation of a potential target formulation's ... potential target formulation's potential target formulation's digital representation providing unit (112) for providing a digital representation a habitat providing unit (113) for providing a biodegradation habitat, the biodegradation habitat exhibiting habitat descriptor values ​​of habitat descriptors that influence the biodegradation of the compound in the respective habitat, the habitat descriptors exhibiting environmental characteristics of the habitat; a model providing unit (114) for providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine a biodegradation degree of a formulation in each biodegradation habitat, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats so as to determine a biodegradation degree of the formulation based on the digital representation of the formulation; a biodegradation determination unit (115) for determining the biodegradation of the potential target formulation based on the selected biodegradation model and the digital representation; a repeat control unit (116) for comparing the determined biodegradability of the potential target formulation with the target biodegradability, and based on the comparison, either i) determining the potential target formulation as the target formulation and determining the potential target formulation specification as the target formulation specification, or ii) providing a new potential target formulation specification for the potential target formulation and repeating the determination of the biodegradability using the new potential target formulation specification for the potential target formulation; An apparatus (110) comprising:

13. 1. An interface device for providing an interface, said interface device comprising: an input interface unit for receiving as input via a user interface a target biodegradation rate, a starting digital representation, and a habitat environment, and for providing said received target biodegradation rate, starting digital representation, and said habitat environment to the device of claim 12; a result interface for providing the habitat descriptor values ​​of the formulation as results, the results being received from the device of claim 12; An interface device comprising:

14. A training device for training a data-driven based biodegradation model for parameterization of said biodegradation model, said training device (120) comprising: a training data providing unit (121) for providing training data related to a predetermined biodegradation habitat, said training data including: a) digital representations of a plurality of training formulations; and b) a biodegradability of a respective biodegradation habitat associated with each training formulation; a trainable model providing unit (122) for providing a data-driven based trainable biodegradation model; a training unit (123) for training the provided data-driven biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine a biodegradation degree of the formulation based on the digital representation of the formulation; a trained model providing unit (124) for providing said trained biodegradation model; A training device (120) comprising:

15. 13. A computer program product for determining a target formulation comprising a target biodegradability, said computer program product comprising program code means for causing an apparatus according to claim 12 to perform the method according to any one of claims 1 to 9.

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