Methods for determining target polymers with target biodegradability

Abstract

The present invention relates to a method for determining a composite specification including a target biodegradability. A target biodegradability is provided, indicative of the biodegradation properties of a polymer. A digital representation of a potential target composite specification is provided, indicative of the physicochemical properties of the polymer. A habitat is provided, indicative of habitat descriptive values ​​of habitat descriptors. A habitat-based model is provided, adapted to determine the biodegradability of the polymer in the habitat. The biodegradability of the potential target polymer is determined based on the provided model and the digital representation. The determined biodegradability is then compared to the target biodegradability and i) the potential target polymer is determined as the target polymer, or ii) a new potential target composite specification for the potential target polymer is provided and the determination of biodegradability is repeated using the new potential target composite specification.

Description

[Technical field]

[0001] FIELD OF THEINVENTION The present invention relates to a method, an apparatus, and a computer program product for determining a target composite specification that describes a target polymer having a target biodegradability. Further, the present invention relates to a training method, a training apparatus, and a training computer program for training a data-driven biodegradation model that can be utilized by the method, the apparatus, and the computer program product for determining the target composite specification. Furthermore, the present invention relates to a method and an apparatus for providing an interface for providing the target composite specification. [Background technology]

[0002] 2. Background of the Invention Generally, polymers are widely used in industrial and / or everyday products due to their wide range of application properties. The use of polymers includes, among others, coatings, personal care products, laundry detergents, lubricants, packaging, foams. However, such a wide range of applications leads, on the one hand, to a large amount of waste, including used polymers. On the other hand, the fact that in most cases the durability of polymers makes them useful for many applications, on the other hand, this very durability leads to several problems in waste management, especially because this polymer is also durable in waste. In particular, if non-biodegradable polymers are not properly recovered in the intended waste stream, this can result in an increase in microplastic pollution and bioaccumulation in the environment. Thus, there is a need not only for degradable polymers, but also a need to take into account knowledge of the biodegradability of polymers at an early stage of the product design process. In particular, it would be advantageous to be able to predict already during the product design process which polymers provide a certain biodegradability and are also suitable for the intended application. It would therefore be advantageous to provide the possibility to predict polymers with suitable biodegradability for an application in an accurate and computationally inexpensive manner. Summary of the Invention [Means for solving the problem]

[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 the determination of target polymer synthesis specifications, including target biodegradability, that allows for accurate determination and has low computational cost. Furthermore, a further object of the present invention is to provide a training method, a training apparatus and a computer program product that allows for the provision of a biodegradation model that can be used in the method, apparatus and computer program and that can be trained to provide good determination accuracy by utilizing less computational resources.

[0004] In a first aspect of the present invention, there is provided a computer implemented method for determining a target synthetic specification indicative of a target polymer comprising a target biodegradability, the method comprising: a) providing a target biodegradability, the biodegradability indicative of a biodegradation characteristic of the polymer; b) providing a digital representation of potential target synthetic specifications indicative of or related to physicochemical properties of the polymer; c) providing biodegradation habitats, the biodegradation habitats indicative of habitat descriptor values ​​for habitat descriptors that influence the biodegradation of the polymer in the respective 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 each biodegradation habitat. and the biodegradation model is a data-driven model parameterized with respect to a biodegradation habitat to determine the biodegradability of the polymer based on physicochemical properties; e) determining the biodegradability of the potential target polymer based on the provided biodegradation model and the digital representation; and f) comparing the determined biodegradability of the potential target polymer with a target biodegradability and based on the comparison, i) determining the potential target polymer as a target polymer and determining the potential target synthetic specification as a target synthetic specification, or ii) providing a new potential target synthetic specification for the potential target polymer and repeating the determination of biodegradability using the new potential target synthetic specification for the potential target polymer.

[0005] Since the biodegradation model is specifically adapted to determine the biodegradability of the potential target polymer with respect to a particular biodegradation habitat characterized by the respective habitat descriptor values ​​that affect the biodegradability of the polymer in the respective habitat, the biodegradability of the polymer, in particular the potential target polymer, for each habitat can be determined very accurately. Furthermore, since the biodegradation model is specifically trained for one or more particular biodegradation habitats, less training data is required for training, and the biodegradation model is more flexible to determine the biodegradability of new polymers that are not part of the training data set. In this way, an accurate determination of the biodegradability of the potential target polymer is provided in a computationally inexpensive manner. Since the determination of the target polymer is based on an accurate and computationally inexpensive determination of the biodegradability, the method also allows for an accurate and computationally inexpensive determination of target composition specifications that indicate the target polymer with the respective target biodegradability. Furthermore, whereas currently utilized testing methods for testing the biodegradability of polymers are very time-consuming and can take months to years to obtain results, the above-described method allows for the results, in particular potentially suitable polymers, to be provided essentially instantly. Thus, not only can the technical requirements for biodegradability determination be reduced, but also the time required for the design of new biodegradable products can be significantly reduced. Furthermore, by providing an easy possibility to take into account the accurate determination of biodegradability already during the design process of the product, it makes it possible to design products in such a way that polymer waste, especially in the form of microplastics, can be avoided. In particular, it can be ensured that the polymers used in the product will be biodegraded in the respective expected environment, for example the marine habitat.

[0006] Furthermore, the training of the respective biodegradation model can be improved since the physicochemical information of the polymer, for example the physicochemical properties including the quantum chemical information of the polymer, such as the solubility in water and octanol or the molar mass of the polymer, is utilized. In particular, the utilization of the physicochemical properties makes it possible to train such models with less training data, since part of the correlation information that needs to be learned is already presented to the model by using the physicochemical properties. This further makes it possible to save the tests and experiments required to provide the training data set.

[0007] The development of new chemical products tailored to application requirements is a major issue in the modern chemical industry. Recently, further requirements have also been raised related to the environmental burden of chemical products along their life cycle. One important aspect of the environmental burden is the prevention of microplastics and bioaccumulation. Microplastics are a growing problem that could be avoided if the polymeric material were biodegradable. A series of standardized tests are currently used to evaluate biodegradability. For biodegradability, various tests exist with specified conditions (ISO13432, ISO14852, ISO14855, ISO17556, OECD301, etc.). Standardized tests often strike a balance between time-efficient tests (as short as 14 days, as long as 24 months) and real-life conditions. In fact, to speed up the test time, temperatures higher than the real conditions are often used. Companies developing new polymers need to invest significant resources in self-assessment and certification of the sustainability of their products. A holistic biodegradability assessment, including laboratory space and equipment, tends to 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 of determining biodegradability disclosed herein allows for faster and more efficient development of new materials. Biodegradability can be determined at an early stage, even before the synthesis of the polymer. This allows for determining whether the polymer is suitable for market entry. This leads to a faster time to market. This also allows for a reduction in resource demand and waste generation, since the polymer does not need to be synthesized to determine biodegradability. The proposed method provides a digital twin that measures the biodegradability of a polymer.

[0008] Furthermore, standard measurements and tests of biodegradability are often time-consuming, including waiting times of up to several months or years. Especially when developing new polymers for the respective application, such time-consuming tests can severely limit the development process. In this context, the present invention makes it possible to provide immediate results for new polymers, significantly shortening the time until the results are available.

[0009] Furthermore, since the number of possible polymers suitable for a particular application is enormous and many of them are often not well studied, today technical product engineers are given the technical task of finding polymers that are not only suitable for a particular application but also satisfy the respective target properties, and in particular, to find each polymer that can fit the application, they have to synthesize and test a huge amount of possible polymers or look through huge data sets and libraries in which potential polymers are stored. Even when using sophisticated designs of planning methods, a very large number of possible polymers still have to be synthesized and experimentally tested. In this context, the above-mentioned method supports users, for example technical product engineers, and allows them to find potentially suitable polymers automatically and more quickly. In particular, by using the above-mentioned method, the user only has to synthesize and test potentially suitable polymers that are determined to be highly likely to satisfy the respective target properties, in particular the target biodegradability. Thus, unnecessary synthesis and testing of polymers can be avoided. In this way, the method allows users to perform the technical task of finding suitable polymers for technical applications more quickly and efficiently.

[0010] The method refers to a computer-implemented method and therefore may be carried out by a general or dedicated computer adapted to carry out the method, for example by executing the respective computer program. The method is adapted to determine, in particular predict, a target synthesis specification that indicates a target polymer with a target biodegradability. In general, the synthesis specification includes instructions on how a particular relevant polymer can be produced. For example, the synthesis specification may refer to the starting products and production conditions that, when applied, will result in the synthesis of the polymer in a production process. Thus, the synthesis specification always relates to the polymer that is produced when the synthesis specification is carried out, for example by utilizing appropriate laboratory or industrial equipment. In particular, the synthesis specification may also be considered as a recipe for the production method of the relevant polymer. Since the target synthesis specification and the target polymer correspond to each other, i.e., when the target synthesis specification is carried out according to it, the target polymer is produced, in the following both terms may be used simultaneously, for example, when the target synthesis specification is determined, the target polymer is also determined and vice versa.

[0011] Biodegradable polymers refer to polymers that can be degraded by biological processes, in particular those that can be assimilated by bacteria and / or fungi to give environmentally friendly products, i.e. decomposed into non-polluting residues, for example by producing inorganic carbon and / or biomass. In general, biodegradability refers to the biodegradation properties of a polymer. In particular, biodegradability refers to the degradation, i.e. indicator of degradation, of a polymer caused by biological processes, i.e. processes involving biological materials, in particular microorganisms, that participate 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 polymer. In this context, the intrinsic property of a polymer refers to the properties of a polymer that are caused by and therefore reflect the properties of the polymer, i.e. its structure, composition, etc., with respect to a particular context. In particular, biodegradability reflects the properties of a polymer when present in a particular biologically active environment. Target biodegradability can refer to any quantification of the biodegradability of a polymer. For example, the target biodegradability may refer to only one value, e.g., the half-life of the polymer in the respective habitat, or to two or more values, e.g., the degradation function of the polymer over time in a particular habitat. The target biodegradability of a polymer preferably refers to any one of the mineralization properties, biotransformation properties, and / or degradation half-life of the polymer. Preferably, the target biodegradability is provided in the form of a percentage value of biodegradation after a given time frame. Furthermore, biodegradability is a technical property of a polymer, i.e., knowledge of the biodegradability of a polymer strongly influences the technical applicability and utilization of the polymer.

[0012] In general, the polymer can be any polymer. Preferably, the target polymer is a synthetic polymer. In one embodiment, the synthetic polymer can be a compound produced by chemical production from one or more starting materials, such as monomers, and comprising at least two monomer units. A monomer unit can be considered as a subunit of a polymer. A polymer can be prepared from monomers by generally known polymerization techniques. A polymer can be produced from a single type of monomer or from different monomers. A polymer can be produced by a single polymerization technique or by a combination of different polymerization techniques. The monomer units can be randomly distributed or present as blocks within the polymer. A polymer can be a linear polymer. A polymer can be a branched polymer. A polymer can be a cross-linked polymer. A polymer can be chemically modified after polymerization.

[0013] In a first step, the method includes providing a target biodegradability indicative of a biodegradation characteristic of the polymer. In particular, providing may refer to receiving the target biodegradability from, for example, a user's input 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, for example, another source via a network connection and providing the received biodegradability. In general, the target biodegradability may refer to one target value, for example, a target half-life of the polymer in a particular habitat, or may refer to a range of values ​​that the polymer in a particular habitat should meet. Furthermore, the target biodegradability may also refer to any kind of target function, for example, a time sequence of biodegradation. For example, the target biodegradability may indicate that the target polymer should have a first range of biodegradability values ​​during a first time range, and then a second range of target biodegradability values ​​during a subsequent time range. Such more complex targeted biodegradability may be advantageous when it is desired that a polymer not biodegrade in a particular habitat for some time, e.g., the average life of the polymer, and then rapidly biodegrade in the same or another habitat.

[0014] The method includes providing a digital representation of a potential target synthetic specification, which indicates physicochemical properties of the potential target polymer. In particular, providing may refer to receiving the digital representation, for example, from a user's input using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the digital representation is already stored. Furthermore, providing may also include receiving the physicochemical properties from another source, for example, via a network connection, and providing the received physicochemical properties as a digital representation. Moreover, indicating or associating a physicochemical property of a polymer is defined as allowing access to information of the physicochemical property. For example, the digital representation may directly include the physicochemical property, for example, in the form of a value of the respective quantity. However, the digital representation may also be a link to the respective physicochemical property, through which the physicochemical property may be accessed, or the digital representation may refer to an identifier associated with the physicochemical property and enabling the utilization of a respective look-up storage to access the physicochemical property. Furthermore, the digital representation may also refer to information enabling the derivation of the physicochemical property using one or more known relationships. For example, the synthetic specifications or structural formulas of polymers can be utilized as digital representations that allow for the derivation of their respective physicochemical properties using known chemical and physical laws and relationships.

[0015] In general, throughout the following description, referring to a parameter or characteristic includes referring to both the respective quantity and also the specific value of that quantity if not explicitly defined. For example, a parameter that is temperature always refers to a quantity that is temperature and the specific value of temperature that is set for that quantity. In most cases, the explicit value of the parameter may be different for different embodiments and applications, so the value is not generally mentioned. However, providing a parameter or characteristic generally means providing information about a quantity, e.g., that the value is temperature, and also the value of the quantity or characteristic itself.

[0016] In particular, the physicochemical properties of the polymer can be quantified by the physicochemical parameters of the polymer. Preferably, the digital representation preferably directly includes the physicochemical parameters that refer to the polymer descriptors, the physicochemical parameters of the polymer indicating the physicochemical properties of the polymer. In particular, the physicochemical parameters of the polymer indicate the parameters that quantify the physicochemical properties of the polymer. In this context, the term "physicochemical properties" refers to the physical and / or chemical properties of the polymer. However, the digital representation can also be provided to allow the deriving of the physicochemical properties, for example in the form of polymer descriptors, for example by providing a representation of potential target synthesis specifications, where the respective physicochemical properties are already stored or can be determined, for example, by calculation of the respective polymer descriptors. Preferably, the digital representation refers to at least one of the recipe, structural formula, trade name, IUPAC name, chemical identifier, and CAS number of the polymer.

[0017] The potential target synthetic specification may also be considered as a starting synthetic specification indicating for which polymer or in which region of the potential polymer space the biodegradability should be determined first in the search for a synthetic specification that will result in a polymer with a target biodegradability. In general, the potential target synthetic specification may be provided by the user or automatically, for example according to a predefined rule, or also arbitrarily. For example, the user may select a promising potential target synthetic specification as a starting point. However, also any target synthetic specification may be used, or a set of rules may be used to provide a potential target synthetic specification without user intervention. Preferably, the potential target synthetic specification is provided based on a rule that takes into account constraints on the potential target synthetic specification space, i.e. the target polymer space.

[0018] In a preferred embodiment, the physicochemical parameters of a polymer are parameters that quantify the physicochemical properties of subgroups of the polymer. In this embodiment, a digital representation may also be provided that allows the physicochemical parameters of the polymer to be derived by determining subgroups of the polymer and to determine the physicochemical parameters of the polymer based on the physicochemical properties of the determined subgroups. In general, a subgroup refers to a part of a polymer, all subgroups of the polymer together forming the polymer. For example, a subgroup may refer to a part of a polymer, the subgroups being linked together in a chain or network in a continuous manner to form the polymer. Preferably, a subgroup of a polymer refers to a repeating unit that represents a part of the polymer that, when repeated, produces a complete polymer chain. However, in some cases, a subgroup may also refer to a single part of a polymer that is not repeated. Furthermore, it is preferred that a subgroup comprises a repeating part, for example a subgroup of a polymer may comprise a repeating core that is also present in other subgroups and further additional parts that are not present in other subgroups. Preferably, a subgroup refers to at least one of a polymerized monomer or an oligomeric fragment. More preferably, a subgroup refers to a polymerized monomer. In this context, polymerized monomers refer to those monomers after polymerization, which may also be called "mer units" or "mers". In particular, polymerized monomers do not refer to monomers present in the reaction mixture before polymerization, i.e. raw material, but rather to repeating units derived from monomers that have been altered during or after polymerization. Thus, the subgroup descriptors determined for polymerized monomers are different from the subgroup descriptors determined for unreacted monomers before polymerization. In particular, it has been found by the inventors that polymerized monomers allow for the determination of polymer descriptors from the subgroup descriptors of polymerized monomers, which allows for an accurate determination of biodegradability. In a preferred embodiment, the digital representation of the polymer includes subgroups provided as molecular models showing the chemical structure of the subgroups after polymerization.Even more preferably, the molecular model of the subgroup is determined in a manner suitable for quantum chemical calculations of the number of atoms characteristic of the subgroup in the polymer and their connectivity. Furthermore, in addition to or instead of the molecular model of the subgroup treating the subgroup as a monomeric structure, a molecular model may also be used that refers to an oligomeric model taking into account the influence of the neighboring molecular structures of the subgroup in the polymer.

[0019] In general, if the digital representation of the potential target synthetic specification does not directly include the physicochemical parameters of the polymer, the physicochemical parameters of the polymer are preferably determined by determining a subgroup of polymers associated with the potential target synthetic specification. For example, the respective subgroups of polymers may be determined utilizing known methods. However, the determination of the subgroups of polymers is preferably performed according to the embodiment of the present invention described below. In particular, it is preferred that the subgroups are determined such that between the atoms of the different subgroups of polymers, the bonds are as least polarizable as possible and preferably have the smallest bond order as possible, for example, single C-C bonds. In addition, it is preferred that the subgroup representing the polymer contains the same number of active non-hydrogen atoms as the polymer. Besides the active atoms, the subgroup may also contain further atoms that may be ignored while calculating the physicochemical parameters of the subgroup. Furthermore, it is preferred that the subgroups are determined in such a way that polymers containing moieties built by different polymerization techniques are sufficiently covered and satisfy the aforementioned conditions. An example of this is polyethers used as raw materials for polyurethanes. In general, a database or archive is generated using multiple reactions between polymer moieties, and the subgroups can be derived from the respective reaction structures. For example, specific chemical languges such as SMILES and SMARTS can be utilized to easily derive subgroups of polymers. For example, a database of reaction SMARTS can be created and then the corresponding reaction SMARTS can be selected based on the polymerization of each polymer. From the selected reaction SMARTS, the SMILES of the monomers of the polymer can then be directly derived and for example RDkit can be used to determine the SMILES of the subgroups, i.e. the number of atoms and connectivity, from the SMILES of the monomers.

[0020] The determined subgroups of polymers are associated with subgroup physicochemical parameters, which represent parameters quantifying the physicochemical properties of the subgroups in the polymer. In particular, if the physicochemical parameters of the polymer are not directly provided by the digital representation, it is preferred that the physicochemical parameters of the polymer are determined by determining the respective subgroup physicochemical parameters for each of the subgroups, and determining the physicochemical parameters of the polymer based on the subgroup physicochemical parameters of the subgroups, for example by averaging. Thus, the method preferably comprises first providing or determining the subgroups from the digital representation of the potential target synthetic specification for the potential target polymer, then determining or providing the physicochemical parameters of the subgroups, i.e. the values ​​of the parameters quantifying the physicochemical properties, and then determining the physicochemical parameters of the polymer based on the physicochemical parameters of the subgroups of the polymer.

[0021] Preferably, the physicochemical parameters of the polymer refer to polymer descriptors, which refer to at least one of constitutional descriptors, count descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors, and / or higher dimensional descriptors, which indicate parameters that quantify the physicochemical properties of the polymer. In a preferred embodiment, the polymer descriptor refers to a 3D descriptor, in particular a quantum chemical descriptor. Furthermore, the inventors have found that in particular the molar mass describes the biodegradation of a polymer very accurately. It is therefore particularly preferred that the physicochemical parameters include the molar mass of the polymer. In general, the physicochemical parameters of the polymer can be derived from the physicochemical parameters of the subgroups, and therefore the physicochemical parameters of the subgroups can also refer to the same physicochemical parameters as those mentioned above. However, the physicochemical parameters can also be derived without making use of the subgroups, for example by quantum chemical simulation of the entire polymer. In the following, the possible physicochemical parameters are defined in more detail. Also, in these cases, the defined physicochemical parameters can refer directly to the physicochemical parameters of the polymer, or, optionally, to the physicochemical parameters of the subgroups.

[0022] The physical descriptor may refer to any of the following: electric potential, average molecular weight, polydispersity, charge, spin, boiling point, melting point, enthalpy of fusion, dissociation constant, Hansen parameters, proticity, polarity, dispersity contribution, Abraham parameters, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, inhibition constant, mutagenicity, LD50, bioaccumulation, toxicity, biodegradation profile, and viscosity.

[0023] 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-hydrogen atoms, number of H, B, C, N, O, P, S, Hal, and heavy atoms, number of hydrogen donor and hydrogen acceptor atoms, number of bonds, number of non-hydrogen or multiple bonds, number of double bonds, triple bonds, 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.

[0024] The physicochemical parameters of the polymer referring to the list of structural fragment descriptors may refer to at least one of the following: 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 the following: MACCS keys in bit format or total format, Morgan and other circular fingerprints, preferably in bit format or total format, topological twist, atom pairs, infrared and related spectra, fingerprint counts, PubChem fingerprints, substructure fingerprints, and Klekota-Roth fingerprints. The graph invariant / topological index descriptors preferably include at least one of the following: topostructural index and topochemical index.

[0025] In a preferred embodiment, the physicochemical parameters of the polymer are 3D descriptors including at least one of the following: volume as a sum over atoms, average volume per atom, area as a sum over atoms, area as an average per atom, area over all atoms, area as an average per atom, solvent accessible surface, dispersion energy, dielectric energy, H donors, H acceptors, polar and non-polar surface areas, atomically resolved H donors, H acceptors, polar and non-polar surface areas, shape, sphericity, dipole and higher electric moments, polarizability, dielectric energy, proticity, polar and non-polar surface areas, orbital energies and orbital gaps, ionization energy, electron affinity, hardness, electronegativity, electrophilicity, excitation energy and intensity, infrared and ultraviolet absorption bands, reactivity measurements, redox potential, bond reference points, partial charges, charge surface areas, atomic orbital contributions, bond order, atomic radius. In particular, the physicochemical parameters of the polymer preferably refer to 3D descriptors including at least one of the following: total volume over all atoms, average volume per atom, total 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, protonity, polar and / or non-polar surface area, excitation energy and intensity, infrared and / or ultraviolet absorption bands, reactivity measurements, particle charge, and / or charge surface area. The preferred high-dimensional descriptors may include at least one of the following: 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 polymer, density, viscosity, conformer-weighted volume and area, conformer-weighted H donor, H acceptor, protonity, polar and / or non-polar surface area, charge distribution, conformer dipole moment, and molecular refraction. Preferably, higher-dimensional descriptors are used that include at least one of the following: solubility, vapor pressure and activity coefficient, surface activity, conformer-weighted H donor, H acceptor, protonity, polar and non-polar surface area, and charge distribution.

[0026] The method further comprises providing a biodegradation habitat, the biodegradation habitat indicating a habitat descriptor value of a habitat descriptor that affects the biodegradation of the polymer in the respective habitat. In particular, providing may refer to receiving the biodegradation habitat from, for example, a user's input using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the biodegradation habitat is already stored. Furthermore, providing may also refer to pre-configuration of the biodegradable habitat. For example, if the method is utilized in a very specific situation in which only one specific biodegradable habitat is sensible, the respective biodegradable habitat may be pre-configured and therefore does not need to be provided as a specific input. Furthermore, providing may also comprise receiving the habitat descriptor value of the habitat descriptor directly from another source, for example via a network connection, and providing the habitat descriptor value of the received habitat descriptor as the biodegradation habitat. The provided biodegradation habitat may refer to a general habitat, for example a marine habitat, and then the respective habitat descriptor value of the habitat descriptor for this habitat is already stored on the respective storage, which can be accessed. However, the provided biodegradation habitat may also directly include respective habitat descriptor values ​​of the biodegradation habitat to provide further specification of the biodegradation habitat. Further, providing the biodegradation habitat may include providing a digital representation of the biodegradation habitat, which may then indicate respective habitat descriptor values ​​of habitat descriptors that affect the biodegradation of the polymer in the respective habitat.

[0027] In general, the habitat descriptors indicate the environmental characteristics of the habitat. In particular, the environmental characteristics of a biodegradation habitat may affect the biological activity in the respective habitat, for example the presence, growth or absence of a particular bacterium. Thus, the environmental characteristics defined by the habitat descriptors also indirectly affect the biodegradation of polymers in the respective habitat. For example, if a polymer is biodegraded by a particular bacterium that requires a particular salt concentration, the polymer will be biodegraded quickly in a habitat that provides such a salt concentration, such as a marine habitat, but much slower in a habitat where the salt concentration is not suitable, such as wastewater. Here again, indicating a habitat descriptor of a habitat or being associated with a habitat descriptor is defined as allowing access to the information of the habitat descriptor. For example, a habitat may directly include the habitat descriptor, for example in the form of a value of the respective quantity. However, the habitat may be a link to the respective habitat descriptor, through which the habitat descriptor may be accessed, or the habitat may refer to an identifier that is associated with the habitat descriptor and allows utilizing a respective lookup storage to access the habitat descriptor. Additionally, a habitat may refer to information that allows one or more known relationships to be used to derive a habitat descriptor, for example, the geographic location of an environment may be utilized along with the habitat to allow knowledge of the respective geographic locations to be used to derive the respective habitat descriptor.

[0028] Preferably, the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a calcareous 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 the following: salt concentration, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, e.g., nitrogen, phosphate, potassium, and / or dissolved organic carbon concentration, pH value, environment type, oxygen content, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to a calcareous habitat, and the habitat descriptor refers to at least one of the following: salt concentration, 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 wastewater, and the habitat descriptor refers to at least one of the following: water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test duration, solids content, and enzyme environment. Moreover, in this habitat, sludge can also be another habitat. Thus, in one embodiment, the habitat can also be a sludge habitat, for example as the aerobic part of a wastewater treatment plant, and the habitat descriptors refer to at least one of the following: solids content, pH, nutrient content, heavy metal content, microbial community. In a further preferred embodiment, the biodegradation habitat refers to soil, and the habitat descriptors refer to at least one of the following: temperature, composition, for example sand content and / or clay, pH value, moisture content, nutrient concentration, microbial community, nitrogen content, water holding capacity, and enzyme environment. In a further preferred embodiment, the biodegradation habitat refers to compost, and the habitat descriptors refer to at least one of the following: temperature, compost activity, pH value, moisture content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, solids content, water holding capacity, and enzyme environment. In general, the habitat can also refer to the habitat of a standard test utilized to determine the biodegradability of a polymer. For example, the standard tests defined by ISO 13432, ISO 14852, ISO 14855, ISO 17556, and OECD 301 also define the specific habitats in which biodegradation occurs.Thus, providing a biodegradation habitat may also include, for example, selecting one of the standard tests via user input, with the habitat descriptor referring to a particular characteristic of the test, i.e., the test environment and thus the particular characteristic of the test habitat. Furthermore, the habitat may also be defined by the biodegradation of a reference polymer or other reference chemical. In this case, the habitat may be provided by providing a reference material and its biodegradation. In this case, the reference material and its biodegradation refer to the habitat descriptor.

[0029] 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, a plurality of biodegradation models may be stored in the biodegradation storage, each biodegradation model trained for one or more different biodegradation habitats. Preferably, each biodegradation model is trained for different values ​​or ranges of values ​​of the habitat descriptor values ​​of the biodegradation habitat in particular. Based on the provided biodegradation habitats exhibiting habitat descriptor values, then a respective suitable biodegradation model may be selected from the plurality of biodegradation models. For example, a biodegradation model is suitable if the exhibited habitat descriptor value is within the range of habitat descriptor values ​​on which the biodegradation model is trained. For example, a respective look-up table may be provided that allows an easy comparison between the exhibited habitat descriptor value and the range of descriptor values ​​on which the biodegradation model stored in the storage is trained, so that a suitable biodegradation model may be directly selected. However, in another embodiment, providing a biodegradation model based on the provided biodegradation habitat may also refer to a user selection of a biodegradation model. For example, the user may be provided with a pre-selection of biodegradation models that refer to the provided biodegradation habitats, and then be allowed to select the respective biodegradation models to be used. In general, possible stored biodegradation models refer to biodegradation models that have already been parameterized based on respective training data sets for one or more habitats. As will be described in more detail below, since the training data sets used to parameterize the biodegradation models are historical data, the biodegradation models may be trained and generated at any time before the specific biodegradation for a particular polymer is determined, and may be stored in the respective database after training. However, the training and thus generation of the biodegradation models may of course also be performed at the time when it is determined that a particular biodegradation model is needed, for example, a biodegradation model for a particular habitat.

[0030] The provided biodegradation model is then adapted to determine the biodegradability of the polymer in the respective biodegradation habitat. In particular, the biodegradation model is a data-driven model that is parameterized with respect to the biodegradation habitat so that the biodegradability of the polymer can be determined based on the physicochemical parameters of the polymer represented by the digital representation. Here, the term "so" should be interpreted as the parameterization being adapted when the physicochemical parameters of the polymer are provided as input, thereby allowing the biodegradation model to provide the biodegradability for the habitat. For example, the biodegradation model relates the physicochemical parameters of the polymer of the past digital representation of the synthetic specification and the past digital representation of the habitat to biodegradability. This allows the digital representation of the synthetic specification to be determined based on the target biodegradability. Here, the term "data-driven" is used to emphasize that the model is primarily based on the respective data input and is not based on, for example, 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. For most applications in this context, regression models based on linear regression, random forest, boosted tree, lasso, ridge regression and MARS algorithms are particularly suitable, while for classification models, random forest, logistic regression and SVM algorithms have been found to be particularly suitable. Preferably, the biodegradation model is based on a neural network algorithm. In general, the biodegradation model is parameterized during a training process in which the physicochemical parameters of the polymer derived from parameters quantifying the physicochemical properties of the polymer are utilized together with the biodegradability corresponding to a particular biodegradation habitat. Based on such a training data set, for example with respect to 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, so that the biodegradation model can also determine the biodegradation of polymers that are not part of the training data set.

[0031] Furthermore, in a preferred embodiment, the biodegradation model may also be adapted to determine the biodegradability of the polymer further based on the habitat descriptor values ​​as input. In particular, the biodegradation model may be trained by utilizing a training data set comprising the physicochemical parameters of the polymer and the associated biodegradability for a particular habitat, as described above, resulting in a biodegradation model that indirectly takes into account the particular habitat. However, the training data set may also optionally comprise the specific habitat descriptor values ​​for the respective habitat. In this case, the biodegradation model may be trained such that in addition to the physicochemical parameters of the polymer, the habitat descriptor values ​​may also be provided as input, and the biodegradation model then determines the biodegradability further based on the habitat descriptor values. This has the advantage that the biodegradability may be determined more accurately, especially in cases where the biodegradability strongly depends on the habitat descriptor values ​​specific to that habitat. For example, in marine habitats, the temperature or salinity may vary greatly in different regions of the world, and for some polymers, this also results in different biodegradation. Therefore, for such cases, it may be advantageous to directly provide the habitat descriptor values ​​as input to the biodegradation model. However, it is also possible, instead of providing habitat descriptor values ​​as input to the biodegradation model, to train two different biodegradation models and indirectly treat different areas as different habitats.

[0032] The method further includes determining the biodegradability of the potential target polymer based on the provided biodegradation model and the digital representation. In particular, if the digital representation of the potential target synthetic specification directly includes the physicochemical parameters of the polymer, the physicochemical parameters of the polymer are provided as input to the biodegradation model, which then provides the biodegradability of the potential target polymer as an output. If the digital representation does not directly include the physicochemical parameters of the polymer, the determination of the biodegradability may also include first determining the physicochemical parameters of the polymer, for example, as described above. The physicochemical parameters of the polymer so determined may then be provided as input to the biodegradation model.

[0033] The determination of biodegradability using a biodegradation model can be considered as a virtual measurement of biodegradability. In particular, the biodegradation model is based on measurement data, e.g., the measured biodegradability of the polymer 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 polymer in some cases. Thus, the biodegradability of a new polymer determined using a biodegradation model can also be considered to be based at least in part on the measurement results.

[0034] In the following step, the biodegradability of the potential target polymer is compared to the target biodegradability. Based on the comparison, it is determined whether the potential target polymer is determined as the target polymer and the potential target synthetic specification is determined as the target synthetic specification, in which case the iteration may stop at this point. Furthermore, based on the comparison, it may also be determined to provide a new potential target synthetic specification for a new potential target polymer and repeat the determination of biodegradability using the new potential target synthetic specification for the new potential target polymer. Thus, at this point, an iteration is performed in which the determination of biodegradability using the biodegradation model and the polymer physicochemical parameters for the potential target polymers is repeated until one of the potential target polymers is determined as the target polymer. In particular, the comparison may include determining whether the determined biodegradability of the potential target polymer is within a predetermined range around the target biodegradability, in which case the target may be considered to be met and the potential target polymer is determined as the target polymer. If the determined biodegradability is outside the predetermined range around the target biodegradability, the target is determined to be not met and a new potential target synthetic specification for a new potential target polymer that may meet the target biodegradability is provided.

[0035] In general, the iteration carried out may refer to any search or directed search of the potential target polymer space. For example, new potential target synthetic specifications or new potential target polymers may simply be selected arbitrarily from a vast amount of potential target polymers generated in silico. However, specific rules may also be applied to generate new potential target polymers, and thus new potential target synthetic specifications, based on a comparison between the determined biodegradability and the potential target polymers, with or without considering the simultaneous optimization of additional target properties of the polymer. In general, known methods for generating new target polymers may be utilized, for example, evolutionary algorithms or Bayesian optimizers may be used.

[0036] Then, iterations can be performed over the steps of determining the biodegradability of the new potential target polymer by utilizing the biodegradation habitat and the polymer physicochemical parameters for the new potential target polymer as described above. Optionally, the determination of the polymer physicochemical parameters from the digital description of the new potential target synthetic specification can also be part of the iterations, if the polymer physicochemical parameters are not already provided with the digital description of the new potential target synthetic specification. Furthermore, it is preferred that the same biodegradation model is used in all iteration steps to determine the biodegradability. However, in some cases, different biodegradation models can also be used in different iteration steps. For example, when physicochemical parameters of other polymers for the new potential target polymer are utilized, another biodegradation model may be more suitable.

[0037] After the iterations stop, e.g., after a potential target polymer has been determined as the target polymer, or if no new potential target polymer has been selected or generated, the results of the iterations may be provided to a user. For example, if none of the potential target polymers meet the target biodegradability, the user may be notified of the failure to determine the target polymer. If the target polymer can be determined, the target polymer may be provided to the user as output. For example, the determined target polymer and the target synthesis specification may be provided to an output unit or a computing unit for further processing. Preferably, the provision of the target synthesis specification and the target polymer leads to further processing utilizing the target synthesis specification.

[0038] Preferably, processing the target synthesis specification comprises determining control signals for controlling a production process based on the determined target synthesis specification. Preferably, the production process refers to a production process of the target polymer utilizing the target synthesis specification. Furthermore, the target specification preferably refers to a machine executable synthesis specification of the target polymer, such that the control signals may directly refer to the control of the respective laboratory or process equipment enabling the execution of the synthesis specification to produce the polymer. In one embodiment, providing the target synthesis specification of the target polymer comprises providing control signals adapted to control an industrial plant to produce the target polymer according to the target synthesis specification.

[0039] In one embodiment, a target application of the polymer is further provided, which refers to the intended application of the target polymer, and the biodegradation habitat is provided based on the target application. The target application of the polymer may refer to the context of the intended application of the polymer, for example, if the polymer is intended to be used as a coating, in a personal care product, in a laundry detergent, in a lubricant, or in product packaging. Such a target application indicates a specific biodegradation habitat. For example, in the case of product packaging, it may be of interest whether the polymer will biodegrade in compost. In another example, if the target application refers to the use of the polymer in a personal care product, it is very likely that the polymer will be found in an aqueous environment sooner or later. In this way, each target application indicates a respective biodegradation habitat. In this context, a predefined list may be provided on the storage, in which each target application and the corresponding biodegradation habitat are stored. Then, the target application of the polymer may be provided, for example, by providing a list of target applications to a user and allowing the user to select each target application, and each target application is connected to one or more biodegradation habitats. A target polymer may then be determined for each of the biodegradation habitats to which the target application is connected, or again, the user may select each biodegradation habitat to which the target application is connected. Additionally or alternatively, information may be provided indicating the intended end-of-life treatment of the polymer. For example, an end-of-life treatment may be indicated, such as whether the polymer should be biodegraded in a particular environment or subjected to a particular treatment, such as in a bioreactor. In this manner, the intended end-of-life treatment information may also be utilized to determine the biodegradation habitat of the polymer, as described above.

[0040] In one embodiment, further information is provided indicating the accessible surface area of ​​the polymer in the intended form, and the biodegradation model is further trained to determine the biodegradability based on the accessible surface area, and the method further comprises further determining the biodegradability based on the accessible surface area. For example, this information may refer to whether the intended product is provided in a solid, crushed, foamed, pelletized, or other form. Preferably, the information indicates the surface area of ​​the product per mass, or the shape of the smallest independent part of the product. Generally, the biodegradability of a polymer is an intrinsic property of the polymer, but the exact timing of biodegradation of a product containing the polymer may also depend on, for example, the surface area that can be accessed by the microbial components of the habitat responsible for biodegradation. Thus, further determining the biodegradability based on the surface area of ​​the product containing the polymer allows for a more accurate prediction of the biodegradability of the final product, and thereby also for a more accurate determination of a target polymer suitable for the final product.

[0041] In one embodiment, target technical application properties of the target polymer are provided, and potential target synthesis specifications are provided based on the provided target technical application properties, such that the potential target polymers meet the provided target technical application properties. In particular, the technical application properties may refer to any property of the polymer and / or the substance at least partly comprising the polymer, which allows to evaluate the technical applicability of the respective polymer provided after synthesis. Preferably, the technical application properties include at least one of mechanical properties, optical properties, physicochemical properties, chemical properties, and biological properties. In general, the mechanical properties may refer to any of the following: adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, tear, tear strength, rebound, compressibility, abrasion, run-off, morphology, tactile properties, stress at break, elongation at break, grain size, and packing. The optical properties may generally include any of the following: color intensity, turbidity, opacity, brightness, reflectance, appearance, absorbance, scattering, color intensity, cloud point, mattness, optical density, spectrum, and refractive index. Further, the physicochemical properties may 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, cohesion, 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, cohesion, self-heating, shock sensitivity, loss on drying, response angle, electrostatic charge, minimum filming temperature, and charge density. Chemical properties may refer to functional group count, 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, photolysis, acidity, pK a, pH, moisture / water content, flammability, burning rate, spontaneous ignition, flash point, flammable gas production, response to fire, deflagration rate, residual monomer count, by-product production, degree of polymerization, salinity, heat resistance, oxidation properties, reduction properties, reactivity, ash content, non-volatile matter content, stability, chelating capacity, calorific value, saponification value. Furthermore, the biological properties may include any of biodegradability, biological resistance, toxicity, biotransformation, ecotoxicity, sensitization, bacterial count, enzyme activity, environmental distribution, bioaccumulation, biological exposure. In a preferred embodiment, the technical application property may further refer to biodegradability, for example, biodegradability in another habitat. For example, the first target biodegradability may refer to a marine habitat and the second target biodegradability, i.e. the technical application property in this case, may refer to wastewater.

[0042] Then, potential target synthesis specifications are provided so that the associated potential target polymers satisfy the provided target technical application properties. For example, a database in which polymers and corresponding technical application properties are already stored can be utilized, and from the database, a target polymer and associated synthesis specification that satisfy the provided target technical application properties can be selected. In general, the polymers that satisfy the target technical application properties can be considered as forming a potential target polymer space that can be explored during an iterative process to find a target polymer. From the selected target polymers that satisfy the target technical application properties, a first potential target polymer, and therefore a first candidate target synthesis specification, can then be selected.

[0043] In one embodiment, the provision of a new potential target synthetic specification is based on modifying the provided target application properties and providing a new potential target synthetic specification so that the potential target polymer satisfies the modified target application properties. In particular, if a new potential target synthetic specification must be provided, the comparison of the determined biodegradability with the target biodegradability shows that the determined biodegradability of the current potential target polymer does not satisfy the target biodegradability. In such a case, a new potential target synthetic specification, and therefore a new potential target polymer, may be provided so that the new potential target polymer still satisfies the target technical application properties, if such a respective polymer exists. However, in many cases, it is not possible to provide such a new potential target polymer, or it may not be technically prudent to provide such a new potential target polymer that still satisfies the technical application properties. In such a case, it is advantageous to modify the target technical application properties, for example, to utilize less strict target technical application properties, for example, to modify the target technical application properties, for example, to refer to a range of values ​​instead of one specific value here, or to refer to a wider range of values ​​when referring to a range of values. In doing so, new potential target polymers can then be selected or generated to meet the revised target application properties.

[0044] In one embodiment, the provision of the potential target synthetic specification based on the provided target technical application properties comprises utilizing a decision model adapted to determine the technical application properties of the polymer based on a digital representation of the polymer, the decision model being a data-driven model parameterized to determine the technical application properties related to the polymer based on the digital representation including the physicochemical parameters of the polymer. The decision model may refer to any known data-driven decision model that allows for determining the technical application properties based on the digital representation of the polymer including the physicochemical parameters of the polymer. In general, the decision model preferably follows the same principles as described above for 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 another respective technical application property of the polymer instead of biodegradability. Thus, all the embodiments described above for the biodegradation model may also be realized for the decision model for determining the technical application properties. By utilizing such a decision model, iterations can be performed in a fast and computationally inexpensive manner not only for the biodegradability of the polymer, but also for one or more further technical application properties, thereby resulting in a target polymer that satisfies not only the target biodegradability, but also one or more further target technical application properties.

[0045] In one embodiment, the habitat descriptor values ​​for the habitat descriptors are stored in association with the respective geographical locations, and providing the biodegradation habitat refers to providing the geographical location of the habitat and retrieving the habitat descriptor values ​​for the geographical location from the storage. The geographical location may refer to, for example, coordinates, or other area identifiers. For example, the geographical location may refer to a city name, a country name, a country region name, a sea area name, a geographical feature, etc. Based on such geographical location, the respective habitats and / or habitat descriptors may be stored, for example, average values, or minimum and maximum values ​​of the habitat descriptors. Thus, by providing a geographical location, the respective habitat descriptor values ​​for this geographical location may be provided. The advantage of this is that the exact habitat or the exact habitat descriptor values ​​of a certain area do not need to be known to the user. Thus, the user can simply provide the location where it is expected that the target polymer may biodegrade in this area.

[0046] In one embodiment, the physicochemical parameters of the polymer indicated by the digital representation of the polymer refer to at least one of the following: recipe parameters from the polymer synthesis, constitutional descriptors, count descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors, and / or high-dimensional descriptors indicating the chemical properties of the polymer. The respective connections of the digital representation with, for example, previously calculated physicochemical parameters of the polymer or further information about the polymer may already be stored and connected with the respective digital representation. For example, if the digital representation refers to a brand name, the respective structural formulas, subgroups and / or physicochemical parameters of the subgroups or physicochemical parameters of the polymer corresponding to the brand name may already be stored in, for example, the storage of the owner of the brand name.

[0047] In a preferred embodiment, the target polymers are searched for as a predefined polymer type, i.e. a well-defined group of polymers, where the potential target polymers are provided as belonging to the predefined polymer type. Preferably, the predefined polymer type is at least one of polyalkoxylates, polycondensates, addition polymers, vinyl polymers, natural polymers, polymer dispersions, polymer foils, biopolymers, polysilicones, resins, rubbers and polyketones, and the biodegradation model is trained specifically for each polymer type for which the polymers are searched. In particular, the training data for parameterizing the biodegradation model includes polymers of each polymer type. However, the biodegradation model can also be parameterized using training data of polymers of more than one polymer type.

[0048] In a preferred embodiment, the polymer type is a polyalcoholate and the habitat is a wastewater habitat, particularly a sludge habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of molar mass, composition, chemical moiety, water solubility and partition coefficient, more preferably, the physicochemical properties include molar mass, even more preferably, molar mass, composition, and even more preferably, molar mass, composition, and partition coefficient.

[0049] In a preferred embodiment, the polymer type is a polycondensate, preferably a polyester, polyamide, phenoplast, and the habitat is a wastewater habitat, in particular a sludge habitat, or a soil habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of the following: molar mass, composition, chemical moiety, water solubility, partition coefficient, index of hydrolytic stability, and crystallinity, more preferably the physicochemical properties include molar mass and composition, even more preferably the molar mass, composition, and chemical moiety, even more preferably the molar mass, composition, chemical moiety, and crystallinity.

[0050] In a preferred embodiment, the polymer type is an additional polymer, preferably polyurethane or polyurea, and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of the following: components, chemical moieties, ratio of chemical moieties, ratio of components, and crystallinity, more preferably the physicochemical properties include components, even more preferably the components and ratio of chemical moieties, even more preferably the components, ratio of chemical moieties, ratio of components.

[0051] In a preferred embodiment, the polymer type is a vinyl-based polymer, preferably polyvinyl, polyacrylate, polystyrene, polyvinyl ether, or polyvinyl alcohol, and the habitat is a wastewater habitat, in particular a sludge habitat, or a soil habitat.Furthermore, in this embodiment, it is preferred that the physicochemical property comprises at least one of molar mass, composition, chemical moiety, water solubility, partition coefficient, more preferably the physicochemical property comprises molar mass, even more preferably the molar mass and composition, even more preferably the molar mass, composition, and chemical moiety.

[0052] In a preferred embodiment, the polymer type is a natural polymer, preferably a polysaccharide, polynucleotide, lignin, suberin, cutin, melanin, natural rubber or polypeptide, and the habitat is a wastewater habitat, in particular a sludge habitat, or a soil habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of molar mass, chemical moiety, and water solubility and partition coefficient, more preferably the physicochemical properties include chemical moiety, even more preferably the chemical moiety and molar mass.

[0053] In a preferred embodiment, the polymer type is a polymer dispersion and the habitat is a marine habitat or a soil habitat. Further, in this embodiment, it is preferred that the physicochemical properties include at least one of composition, chemical moiety, water solubility, and particle size, more preferably, the physicochemical properties include composition and particle size, and even more preferably, the composition, chemical moiety, and particle size.

[0054] In a preferred embodiment, the polymer type is a polymer foil and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of the following: composition, molar mass, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical properties include composition and surface / volume ratio, even more preferably, the composition, chemical moiety, and surface / volume ratio, even more preferably, the composition, chemical moiety, crystallinity, and surface / volume ratio.

[0055] In a preferred embodiment, the polymer type is polysilicone and the habitat is a soil habitat, a wastewater habitat, particularly a sludge habitat, or a marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of composition, molar mass, chemical moiety, water solubility, partition coefficient, and surface / volume ratio, more preferably, the physicochemical properties include molar mass, even more preferably, molar mass and composition, and even more preferably, molar mass, composition, and partition coefficient.

[0056] In a preferred embodiment, the polymer type is a resin and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of composition, molar mass, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical properties include molar mass, even more preferably, molar mass and composition, and even more preferably, molar mass, composition, and surface / volume ratio.

[0057] In a preferred embodiment, the polymer type is rubber and the habitat is a soil habitat or a marine habitat.Further, in this embodiment, it is preferred that the physicochemical properties include at least one of composition, chemical moiety, water solubility, crystallinity, and surface / volume ratio, more preferably, the physicochemical properties include composition, even more preferably, composition, and surface / volume ratio, and even more preferably, composition, chemical moiety, crystallinity, and surface / volume ratio.

[0058] In a preferred embodiment, the physicochemical properties include at least one of molar mass, chemical moiety, solubility in water and / or octanol, crystallinity, and surface / volume ratio. More preferably, the physicochemical properties include chemical moiety, and even more preferably, molar mass, chemical moiety, and solubility in water. When the polymer type is polyalkoxylate, polycondensate, vinyl polymer, or polysilicone, the physicochemical properties preferably further include at least one of partition coefficient and composition. When the polymer type is polycondensate, addition polymer, polymer foil, resin, rubber, or polyketone, the physicochemical properties preferably further include at least one of crystallinity and hydrolytic stability index. When the polymer type is resin, rubber, addition polymer, or polysilicone, the physicochemical properties preferably further include surface / volume ratio.

[0059] In a further aspect, an interface method for providing an interface is presented, the interface method including: a) receiving as input via a user interface a target biodegradability, a digital representation, and a habitat, and providing the received target biodegradability, digital representation, and habitat to a processor executing the above-mentioned method; and b) providing as a result a target synthesis specification for the polymer, the result being received from the processor executing the above-mentioned method.

[0060] In a further aspect, a computer-implemented training method for training a data-driven based biodegradation model for parameterizing the biodegradation model is presented, the training method comprising: a) providing training data related to a given biodegradation habitat, the training data including: i) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers, and ii) biodegradability for a respective biodegradation habitat associated with each training polymer; b) providing a data-driven based trainable biodegradation model; c) training the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine the biodegradability of a polymer based on the physicochemical properties, preferably the physicochemical parameters indicated by the digital representations of the polymers; and d) providing a trained biodegradation model.

[0061] In a further aspect, an apparatus for determining a target synthetic specification indicative of a target polymer comprising a target biodegradability is provided, the apparatus comprising: a) a target biodegradability providing unit for providing the target biodegradability, the target biodegradability providing unit indicative of a biodegradation characteristic of the polymer; b) a digital representation providing unit for providing a digital representation of a potential target synthetic specification indicative of or related to a physicochemical characteristic of the polymer; c) a habitat providing unit for providing a biodegradation habitat, the biodegradation habitat indicative of habitat descriptor values ​​for habitat descriptors affecting the biodegradation of the polymer in the respective habitat, the habitat descriptors indicative of 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 indicative of the biodegradation of the polymer in the respective biodegradation habitat. a model providing unit adapted to determine, and the biodegradation model is a data-driven model parameterized with respect to a biodegradation habitat so as to determine the biodegradability of the polymer based on the physicochemical properties, preferably the physicochemical parameters of the polymer, more preferably the polymer descriptors represented by the digital representation; e) a biodegradability determining unit for determining the biodegradability of the potential target polymer based on the selected biodegradation model and the digital representation; and f) an iterative control unit for comparing the determined biodegradability of the potential target polymer with a target biodegradability and, based on the comparison, i) determining the potential target polymer as a target polymer and determining the potential target synthetic specification as a target synthetic specification, or ii) providing a new potential target synthetic specification of the potential target polymer and repeating the determination of biodegradability using the new potential target synthetic specification of the potential target polymer.

[0062] In a further aspect, an interface device for providing an interface is presented, 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 polymer as a result, the result being received from the above-mentioned device.

[0063] 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 polymers indicating physicochemical properties of each of the training polymers, and ii) biodegradability for a respective biodegradation habitat associated with each training polymer; 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 the biodegradability of a polymer based on the physicochemical properties, preferably the physicochemical parameters of the polymer represented by the digital representation; and d) a trained model providing unit for providing a trained biodegradation model.

[0064] In a further aspect of the present invention, the use of the method as described above is presented, which is used to determine target polymers with target biodegradability for any of the following: i) polymers referred to polyesters, in particular those used in multi-film and packaging applications, e.g. aromatic-aliphatic copolyesters; ii) polymers referred to polyalkoxylates, in particular those used in home and personal care applications; iii) polymers referred to polyurethane dispersions; iv) polymers used in aroma applications; v) polymers used in paper coatings for packaging applications based on multi-layer blends; and vi) polymers referred to polyurethanes used in adhesives.

[0065] In a further aspect of the invention, a system is provided, the system comprising: i) a control signal comprising a polymer synthesis specification indicating one or more ingredients for producing a polymer, the control signal being generated according to the method described above; and ii) one or more ingredients indicated by the synthesis specification in the control signal.

[0066] In a further aspect of the invention, there is provided the use of a control signal generated according to the above-described method for controlling a production process, in particular a production process involving the production of polymers.

[0067] In a further aspect of the invention, a control signal is provided, the control signal being generated according to the method described above. Preferably, the control signal comprises a machine executable synthesis specification for producing a target polymer.

[0068] In a further aspect, there is provided a computer program product for determining a target polymer having a target biodegradability, the computer program product comprising program code means for causing the above-mentioned apparatus to carry out the above-mentioned method.

[0069] In a further aspect there is provided a computer program product for training a biodegradation model, the computer program product comprising program code means for causing an apparatus as described above to perform the method as described above.

[0070] 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 as 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 as defined in the dependent claims.

[0071] It shall 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.

[0072] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Brief description of the drawings]

[0073] [Figure 1] FIG. 1 is a schematic, exemplary diagram illustrating one embodiment of a system including an apparatus for determining a target composition specification indicative of a target polymer having a target biodegradability. [Diagram 2] FIG. 1 shows, in a schematic and exemplary manner, a flow chart of a method for determining a target synthesis specification indicative of a target polymer having a target biodegradability. [Diagram 3] FIG. 1 shows, in a schematic and exemplary manner, a flow chart of a method for training a biodegradation model for determining the biodegradability of a polymer. [Figure 4] FIG. 2 is a schematic, exemplary flow chart of a preferred and more detailed embodiment of a method for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Diagram 5] FIG. 2 is a schematic, exemplary flow chart of a preferred and more detailed embodiment of a method for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 6] FIG. 13 shows, in a schematic and exemplary manner, an optional extension of the method for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 7] FIG. 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture of a system and apparatus for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 8] FIG. 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture of a system and apparatus for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 9] FIG. 1 shows, in a schematic and exemplary manner, a block diagram of a system architecture of a system and apparatus for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 10] 1A-1C are schematic and exemplary diagrams illustrating output and input screens of an exemplary user interface. [Figure 11] FIG. 2 shows, generally and exemplarily, a further flow chart of a preferred and more detailed embodiment of a method for determining target synthesis specifications indicative of a target polymer having a target biodegradability. [Figure 12] FIG. 2 shows, generally and exemplarily, a further flow chart of a preferred and more detailed embodiment of a method for determining target synthesis specifications indicative of a target polymer having a target biodegradability. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0074] Detailed Description of the Embodiments 1 shows, in a schematic and exemplary manner, an embodiment of a system 100 comprising an apparatus 110 for determining a target composition specification indicative of a target polymer comprising a target biodegradability. Furthermore, the system 100 comprises a training apparatus 130 for training a biodegradation model utilized in the apparatus 110, a database 140 in which the results of the determination of the target composition specification can be stored, and a production system 120 for producing a product, particularly including the determined target polymer, which can be controlled utilizing the determined target composition specification.

[0075] 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 composite specification and / or provide a control signal for controlling a production process of the production system 120 based on the determined composite specification.

[0076] The target biodegradability providing unit 111 is adapted to provide a target biodegradability indicative of a desired biodegradability characteristic of the polymer. The target biodegradability providing unit 111 may refer to, for example, an input unit through which a user can input the respective target biodegradability. Furthermore, the target biodegradability providing unit 111 may refer to, or be part of, a user interface that allows a user to interact with the device 110 for providing a target biodegradability. However, the target biodegradability providing unit 111 may also refer to, for example, a storage unit in which a target biodegradability for a particular application is already stored, or may be communicatively coupled to a storage unit.

[0077] The digital representation providing unit 112 is adapted to provide a digital representation indicative of a potential target synthetic specification indicative of polymer physicochemical parameters for a potential target polymer. The digital representation providing unit 112 may for example refer to an input unit through which a user can input the respective digital representation. Furthermore, the digital representation providing unit 112 may 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 may also refer to or be communicatively coupled to a storage unit in which the digital representations of the polymers are already stored. In general, the digital representation may directly include the polymer physicochemical parameters indicative of parameters quantifying the physicochemical properties of the respective polymer. However, instead of directly providing the polymer physicochemical parameters, only the polymer synthetic specification may be provided. In this case, it is preferred that the digital representation providing unit 112 is further adapted to determine the polymer physicochemical parameters from the synthetic specification. In particular, it is preferred that the digital representation providing unit 112 is adapted to identify the type and amount of a subgroup of polymers from the potential target synthetic specification and to determine the polymer physicochemical parameters based on the type and amount of the identified subgroup. In particular, the digital representation providing unit 112 may be adapted to determine for each identified subgroup the respective subgroup physicochemical parameters, for example by accessing a database in which the respective physicochemical parameters are stored for the most relevant subgroups. The physicochemical parameters of the polymer may then be determined based on the subgroup physicochemical parameters of the subgroups, preferably also based on the determined amount and type of subgroups, for example by a weighted average of the subgroup physicochemical parameters of the subgroups. The digital representation providing unit 112 is then adapted to provide the digital representation comprising the physicochemical parameters of the polymer to the biodegradability determining unit 115, for example.

[0078] 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 the 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 the user to indicate a geographic location, for example by marking a location on a map, by indicating coordinates, or by providing an area name, such as, for example, a political or geological area, 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 sea area, 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.

[0079] In general, the biodegradation habitats indicate habitat descriptor values ​​for habitat descriptors that affect the biodegradation of the polymer in the respective habitat. In particular, the habitat descriptors indicate environmental characteristics of the habitat, e.g., in the case of a marine habitat, the salt concentration may strongly affect the biodegradation of the polymer in the marine habitat. Typical specific habitat descriptor values ​​for each habitat may be stored in the database. However, the user may also enter the respective specific habitat descriptor values, e.g., if it is known that the habitat descriptor values ​​for each habitat deviate from the typical habitat descriptor values.

[0080] 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 on 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 on specific habitat descriptor values ​​or ranges of values ​​that define which biodegradation habitats the respective biodegradation model is suitable for. 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, for example based on the habitat descriptor values ​​of the biodegradation habitat. However, the model providing unit 114 may also configure or refer to an input unit, whereby the biodegradation model may be provided, for example by a user selection or user input indicating which biodegradation model should be used.

[0081] The biodegradation model is a data-driven model parameterized to determine the biodegradability of the polymer based on the digital representation, in particular based on the physicochemical parameters of the polymer that indicate the physicochemical properties related to the biodegradability of the polymer. Optionally, the biodegradation model can also be trained to further utilize the habitat descriptor values ​​provided 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 classification model-based algorithm. The regression model-based algorithm can be based on any of the following algorithms: neural network algorithm, linear regression algorithm, LASSO algorithm, ridge regression algorithm, MARS algorithm, random forest algorithm, and boosted tree algorithm. The classifier-based model algorithm can be based on any of the following algorithms: random forest algorithm, logistic regression algorithm, SVM algorithm. The inventors have found that for most applications, in particular the linear regression, random forest, neural network, and MARS-based algorithms are suitable.

[0082] The biodegradation model may be trained, for example, with the aid of a training device 130. In particular, the training device 130 comprises a training data providing unit 131 for providing training data for training the data-driven based biodegradation model. The training data includes a) physicochemical parameters of a plurality of training polymers, and b) a biodegradability associated with each training polymer for one or more different habitats. Optionally, the training data set may further include habitat descriptor values ​​for a particular habitat for which the respective biodegradability of the polymers has been determined. Preferably, in the training data, the biodegradability provided for each training polymer refers to the biodegradability being measured according to the same measurement method. However, the biodegradability may also be provided for different measurement methods, in which case it is preferable to clearly indicate which biodegradability is associated with which measurement method, so that the biodegradation model may be trained to distinguish between the different measurement methods. In general, the training data may be designed to cover a predefined habitat space of the biodegradation model to be trained, the habitat space being defined by the range of values ​​of the respective habitat descriptors for which the biodegradation model is to be trained. For example, the training data may be designed to cover a predefined polymer type for a predefined habitat. Known methods can be used to design and optimize training data for a given habitat space such that the habitat space is adequately covered by the training data and random outliers are avoided.

[0083] Further, the training device 130 comprises a model providing unit 132 adapted to provide a data-driven based 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 can be already stored in a storage unit that the model providing unit 132 can access to provide the same. Further, the training device 130 comprises a training unit 133 for training the provided data-driven based 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 biodegradability of a polymer based on the digital representation, in particular the physicochemical parameters of the polymer. In general, known training algorithms for training data-driven, in particular machine learning based, models may be utilized. Preferably, during the training of the biodegradation model, also the physicochemical parameters of the polymer that most affect the biodegradability in the respective habitat are determined, and then the model is trained based on these most influential physicochemical parameters. To determine these most influential physicochemical parameters, for example, cluster analysis or PCA analysis tools may be utilized. In particular, the physicochemical parameters can be utilized to determine the application space of the training data, which is then defined by the physicochemical parameters of the polymer and the habitat descriptors covered by the data. The determination of the most influential physicochemical parameters and / or habitat descriptors can then be performed as a dimensionality reduction of the application space. An algorithm is then applied to optimize the training data in the application space, e.g. to cover the application space with as little training data as possible.

[0084] 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 the trained biodegradation models for different habitats and / or different types of polymers and / or physicochemical parameters of the polymers, 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.

[0085] In all cases, the biodegradation model providing unit 114 is then adapted to provide a suitable trained biodegradation model to the biodegradability determining unit 115. The biodegradability determining unit 115 can then utilize the biodegradation model and the provided digital representation to determine the biodegradability. In particular, the biodegradability determining unit 115 can be adapted to utilize the physicochemical parameters of the polymer represented by the digital representation as input to the trained biodegradation model, as already described above, and then provide as output the trained biodegradability determination.

[0086] Furthermore, the apparatus comprises an iterative control unit 116 adapted to control the iterative process for determining the target synthetic specification. In particular, the iterative control unit 116 is adapted to compare the determined biodegradability of the potential target polymer with the target biodegradability. Based on this comparison, the iterative control unit 116 is then adapted to determine whether a further iterative step is necessary to determine the target synthetic specification or whether the iteration has reached an end, in particular whether the potential target polymer can be set as the target polymer and thus the potential target synthetic specification as the target synthetic specification. Preferably, the comparison of the determined biodegradability of the potential target polymer 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 below a predetermined threshold. However, the comparison may also refer to a more complex mathematical function, and the condition for determining the potential target polymer as the target polymer 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 polymer is the target polymer and the potential target synthetic specification is the target synthetic specification, and terminates the iteration.

[0087] If the above condition is 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 synthesis specification of the potential target polymer and repeat the determination of the biodegradability using the new potential target synthesis specification of the new potential target polymer. For example, the new potential target polymer synthesis specification can be provided on a database in which a number of potential target synthesis specifications are already stored, from which the iteration control unit 116 can select the new potential target synthesis specification arbitrarily or according to a predetermined rule. Such a rule can be, for example, a function of the comparison of the determined biodegradability with the target biodegradability of the potential target polymer. 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 parts of the new potential target polymer are similar in the potential target polymer. In such a case, these rules may allow the iterative control unit 116 to be adapted to select a new potential target polymer that is more similar to the potential target polymer if the determined biodegradability of the potential target polymer is already similar to the target polymer, 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 synthetic specification, for example based on the potential target synthetic specification and predefined rules, or arbitrarily. Again, for the rules, the same principles as those described above may be applied.

[0088] Furthermore, the iteration control unit 116 may also be adapted to apply an iteration interruption criterion that indicates failure to find a suitable target synthetic 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 predefined number of iteration steps, i.e., determining a predefined number of new potential target synthetic specifications. However, other interruption criteria may also be utilized.

[0089] An output unit, e.g. a display, may then be adapted to output the determined target synthesis specification or the target polymer, e.g. in the form of a visual representation of the polymer, an identification of the polymer, a chemical formula representing the polymer, etc. Furthermore, the output unit may additionally or alternatively be adapted to provide the determined target synthesis specification to a database 140 for storing each determined target synthesis specification in association with a respective target biodegradability for future use. Optionally, the apparatus 110 may comprise a control unit 117 adapted to provide a control signal based on the determined target synthesis specification to control the production process of the production system 120. In particular, the control signal is preferably indicative of a machine executable synthesis specification of the target polymer to be produced based on the determined target synthesis specification in order to produce a target polymer that satisfies the target biodegradability. However, the control unit 117 may also be adapted to control the production process of another product based on the determined target synthesis specification, e.g. providing a control signal indicative of a machine executable synthesis specification for another product that utilizes or includes the respective target polymer.

[0090] Fig. 2 shows a schematic and exemplary flow chart of a method for determining a target synthetic specification indicative of a target polymer comprising a target biodegradability. The method 200 comprises a first step 210 of providing a target biodegradability. Furthermore, in step 220, a digital representation of a potential target synthetic specification is provided, indicative of physicochemical parameters of a potential target polymer. 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, indicative of habitat descriptor values ​​of habitat descriptors influencing the biodegradation of the polymer in the respective habitat. For this step 230 too, for example, the principles described above with respect to the habitat providing unit 113 may be applied. Furthermore, in step 240, a biodegradation model is provided, adapted to determine the biodegradability of the polymer based on the digital representation. As already described in detail above, the provision of a biodegradation model may also refer to selecting a biodegradation model based on the provided biodegradation habitat. Furthermore, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitat so that the biodegradability of the polymer can be determined based on the physicochemical parameters of the polymer. In general, steps 210, 220, 230, and 240 can be performed in any order or simultaneously. In the following step 250, the biodegradability is determined based on the digital representation and the biodegradation model of the provided potential target polymer. In step 260, the determined biodegradability of the potential target polymer is then compared with the target biodegradability. Based on this comparison, the potential target polymer is determined as the target polymer and the potential target synthetic specification is determined as the target synthetic specification, or a potential target synthetic specification of a new potential target polymer is provided and the determination of the biodegradability is repeated using the new potential target synthetic specification of the potential target polymer. In an optional step 270 after the target synthetic specification is determined using the above steps, the determined target synthetic specification can be provided to a user via an output unit together with the determined target polymer and the target biodegradability.Furthermore, in step 270, the potential target synthesis specifications may also be utilized for the generation of control signals that allow for control of the production process of, for example, the target polymer or of a product including the target polymer, as already described in detail above.

[0091] FIG. 3 shows a schematic and exemplary flow chart of a method for training a data-driven based biodegradation model, for example used in the method 200 described with reference to FIG. 2. In general, the method 300 may be executed by a respective unit of the training device 130 as described with reference to FIG. 1. The method 300 comprises a step 310 of providing training data for training the data-driven based biodegradation model. The training data comprises a) physicochemical parameters of a plurality of training polymers, and b) a biodegradability, e.g. a specific habitat descriptor value, associated with each training polymer in a respective biodegradation habitat. Optionally, the training data set may further comprise a respective specific 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 comprises a step 320 of providing a data-driven based 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 simultaneously. The method 300 then further includes a 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 biodegradability of the polymer based on the digital representation of the polymer. In step 340, the trained biodegradation model may then be provided, e.g., by storing the trained biodegradation model in a storage or by directly providing the trained biodegradation model to the device 130, as described with respect to FIG. EXAMPLES

[0092] In the following, a more detailed preferred embodiment of the above-mentioned method and the corresponding device is described. A schematic and exemplary flow chart of an exemplary preferred embodiment of the method is provided by Fig. 4. In this exemplary embodiment, the method starts by requesting, for example via a user interface, the target values ​​of the target application, in particular the target biodegradability. Furthermore, in a next step, the optimization is initialized by providing a potential target synthesis specification, i.e. a starting recipe. Optionally, constraints on the recipe, i.e. the synthesis specification, can be taken into account in this process, for example if the user provides such constraints. The constraints can refer, for example, to constraints on the production of the polymer, on the starting materials to be used for the synthesis of the polymer, etc. Furthermore, additional application conditions can be requested, in particular indicating the biodegradation habitat of the target polymer. Furthermore, the additional application conditions can also indicate further information regarding the target polymer to be satisfied. For example, the requested additional application conditions can refer to geographical locations indicating where it is expected that the polymer can be biodegraded, and based on these geographical locations, the biodegradation habitats and the respective habitat descriptors can be determined, for example by utilizing a database in which the respective associated biodegradation habitats and biodegradation physicochemical parameters are already stored. Based on the above steps, an optimization for determining a target polymer, i.e. a target synthetic specification, can be initialized. In a first step of the optimization, physicochemical parameter values ​​of the polymer can be derived from the provided starting recipe, i.e. from the provided potential target synthetic specification. A more detailed and preferred possibility for the derivation of the physicochemical parameter values ​​is explained with respect to FIG. 6. However, the derivation of the physicochemical parameters of the polymer can also refer to accessing a storage in which the respective physicochemical parameter values ​​for each potential target polymer are already stored. Furthermore, this step can also be omitted if the digital representation of the provided potential target synthetic specification already contains the physicochemical parameters of the polymer. Based on the required additional application conditions, in particular based on the biodegradation habitat, a respective decision model, i.e. a biodegradation model, can be provided.Based on the provided decision model and the digital representation of the potential target composite specification, a value of the target application, i.e. biodegradability, of the potential target polymer 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 given limit. If not, i.e. if this condition is not met, the formulation of the potential target composite specification is modified and a new target composite specification is determined, optionally taking into account the previously provided constraints. The iteration can then start anew for the new potential target composite specification. If at a certain point in time the determined performance value meets the target value within a limit, i.e. if the respective condition is met, the potential target composite specification is determined as the target composite specification and is provided, for example, to a user or to a control unit for producing the respective determined target polymer. Figure 11 shows the same method specifically for the target technical application property being biodegradability, in a schematic and exemplary manner.

[0093] FIG. 5 shows a further preferred embodiment of the above-mentioned method for determining a target composition specification with a given target biodegradability, in which in addition to the target biodegradability it is desired that the target polymer also fulfills 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, in particular, the method follows the same principles as described above with reference to FIG. 4. However, with the additional target value, additional conditions have to be fulfilled during optimization. Therefore, in the following, only the main differences with respect to the above-mentioned method are pointed out. In particular, in this preferred embodiment, the optimization module not only optimizes for the first target value, i.e. the target biodegradability, but also for the second target value. Preferably, for the second target value too, a decision model is utilized that is adapted to determine the value of the technical application property based on the physicochemical parameters of the polymer. Thus, in addition to the above-mentioned method for the second target application, a second decision model is provided, which makes it possible to determine the application property value based on the physicochemical parameters of the polymer for the second target application. The second determination model can be based on, for example, the same algorithm as the biodegradation model, only trained with a different data set to determine another property of the polymer.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 property value meets the target second application property value within limits.Predetermined rules can be utilized to determine when the iteration continues, i.e., when a new formulation is provided as a new potential target composite specification, and under what conditions the potential target composite specification is determined as the target composite specification.For example, the user can predetermine the weighting of which conditions need to be satisfied to what extent.For example, it may be more important for the user that biodegradability is satisfied, but other target application properties are less important.In this case, the limits within which the second target application property can be satisfied can be set wider or the weighting of satisfying this condition can be made smaller. In this context, too, Pareto optimization methods can be used to find the optimal trade-off between the different targets. If at a certain point in the iteration the conditions are met and it is determined that they satisfy the predefined rules, the respective potential target synthesis specification can be determined as the target synthesis specification and can be provided to the user as output or can be used to generate a control file for producing the respective target polymer. Figure 12 shows the same method specifically for the target technical application property being biodegradable, in a schematic and exemplary manner.

[0094] Fig. 6 shows, in a schematic and exemplary manner, a preferred method 600 for deriving physicochemical parameter values ​​from a digital representation of a new polymer. In a first step 610, a digital representation of the polymer is provided. This digital representation may directly contain the physicochemical parameters of the polymer, in which case the steps shown in Fig. 6 up to step 650 may be omitted. However, in many cases, the physicochemical parameters of the polymer must first be determined based on the provided digital representation, for example with reference to a synthetic recipe of the polymer or a chemical representation of the polymer showing the chemical components and bonds in the polymer. In this step 610, the digital representation may also include any one or more of the following: amounts of monomeric components; amounts of non-monomeric components such as initiators, fillers, additives; reaction conditions such as temperature, vessel, pressure, stirring speed; condition profiles, e.g., temperature profile, pH value, solvent; feed profile; polymerization type, e.g., radical polymerization, anionic polymerization, polycondensation, polyaddition, polyether formation; post-treatment such as amounts of components, conditions; and temperature and feed profile; type of post-treatment, e.g., radical, cationic, anionic, polycondensation, polyaddition, polyether formation; chemical information about components such as mixtures, connectivity of non-polymeric pure compounds, composition of polymeric pure compounds based on subgroups, connectivity of monomers related to subgroups in polymeric pure components; and for block copolymers, information about which block each monomer and reactive prepolymer is included; and for structured / layered materials and composites, information about which phase / layer each component is included. If such information is not directly provided from the digital representation, in optional step 620, reactive components and subgroups may also be derived from the digital representation, e.g., from a recipe.

[0095] If the information provided indicates the presence of a mixture, in a subsequent step the mixture is decomposed into its pure components and each polymer component is treated as an input polymer. Furthermore, if desired, the polymer composition may also be converted to mole %, weight %, volume %, or absolute mole %.

[0096] In the next step 630, the polymerizable components can be converted into subgroups, e.g. repeating units, and the subgroups are determined as different types. For example, the polymerizable subgroups can be determined based on the connectivity information of the non-polymerizable pure compounds, e.g., by using SMARTS via a KNIME workflow. Also, the connectivity information of all possible subgroups can be derived from the connectivity information of the non-polymerizable pure compounds, e.g., by using reaction SMARTS via a KNIME workflow.

[0097] After the subgroups and their types are determined, in step 640, the type of physicochemical parameters to be utilized may be provided. However, the physicochemical parameters may also be determined without first selecting the type of subgroup. In order to reduce the computational resources of the method, in step 641, it is preferably determined whether the subgroup physicochemical parameters associated with the respective type of subgroup are already stored in the database, for example whether an entry for the subgroup with the same connectivity information is already present in the database. In this case, the physicochemical parameters of the respective associated subgroup may be directly downloaded, for example in step 644. If the determined subgroup type is not stored in the database, the physicochemical parameters of the subgroup associated with the respective subgroup type may be determined, for example in step 642. For example, based on the connectivity information, a 3D structure of the subgroups of the respective type may be derived and an automatic calculation of the subgroup physicochemical parameters may be initiated, for example using a computer cluster, or an already existing machine learning determination may be utilized as the subgroup physicochemical parameters. In general, if a calculation for a new subgroup is required, in step 643, the results are preferably stored in the database after the calculation is completed. Optionally, further physicochemical parameters of the subgroups may be provided from topological analysis of the subgroups, quantum chemical calculations, molecular dynamics calculations, coarse graining methods, finite element calculations and dynamics simulations. In particular, polymer reaction engineering methods may be used to derive subgroup descriptors that allow taking into account the microstructure of the polymer.

[0098] In step 631, the amount of the subgroups, i.e. of each type of subgroup, is determined, for example based on the provided recipe information of the polymer, and provided in step 632. For example, the amount can be determined by counting the amount of polymerizable groups per polymerizable component, optionally including prepolymer. In this case, information on the polymerizable groups can be derived from the non-polymerizable components, and the amount so determined can be added to the count of the number of, optionally non-polymerizable, polymerizable groups of the subgroup for the polymerizable component based on the composition of the polymerizable component to determine the resulting amount. Furthermore, it is preferred that the amount of polymerizable groups originating from the agent used for post-treatment after polymerization is removed from the obtained amount.

[0099] However, while it is preferred that the polymer physicochemical parameters are derived from a subgroup of polymers, in other embodiments of the invention the polymer physicochemical parameters may also be derived in other ways, for example by directly determining the polymer physicochemical parameters from the complete polymer. Furthermore, the polymer physicochemical parameters for the respective polymer may also already be stored in a storage unit, so that deriving the polymer physicochemical parameters from the digital representation of the polymer may refer to determining information about the polymer from the digital representation that allows accessing a database to retrieve the physicochemical parameters of the corresponding polymer.

[0100] Optionally, the amounts of derived subgroups can be used for further interpretation of the polymer composition, for example the total number of polymerized functional groups, such as double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups, and functional groups formed, such as amide groups, ester groups, thioester groups, urea groups, urethane groups, thiourethane groups, ether groups, can be determined. Also, molar weighted total number of polymerized functional groups, mass weighted total number of polymerized functional groups, total number of residual functional groups, e.g., double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups, molar weighted total number of residual functional groups, mass weighted total number of residual functional groups, sum of all residual functional groups, ratio between functional groups after polymerization, number of crosslinks in the polymer, mole fraction of crosslinks in the polymer, optionally mass weighted average number of atoms per subgroup, optionally by weight, average number of non-H atoms per subgroup, optionally by weight, average number of bonds between non-H atoms per subgroup, optionally by weight, average number of rotors per subgroup, optionally by weight, average number of rotors between non-H atoms per subgroup, optionally by weight, average number of rings per subgroup, optionally by weight, The average polar surface area per group, optionally by weight, the average refractive index per subgroup, optionally by weight, the total number of blocks, the molar size of the first block, the molar size of the last block, the HLB value of the polymer, optionally at the area weighted HLB value, the HLB value of the block with the minimum HLB value, optionally at the area weighted HLB value, the HLB value of the block with the maximum HLB value, optionally at the area weighted HLB value, the HLB value of the first block, optionally at the area weighted HLB value, the HLB value of the last block, optionally at the area weighted HLB value, the mass of the first block, the mass of the last block, the area of ​​the block with the minimum HLB value, the area of ​​the block with the maximum HLB value, the difference in the HLB values ​​of the blocks, optionally at the area weighted HLB value, the hydrophilic area of ​​the polymer, the lipophilic area of ​​the polymer, the number of arms for ring opening polymerization, or the length of the arms for ring opening polymerization may be determined.

[0101] In step 650, the determined amounts and types of subgroups and the physicochemical parameters of the relevant subgroups may be utilized to calculate the physicochemical parameters of the polymer. For example, the physicochemical parameters of the polymer may be determined by one or more of molar weighted averages, e.g., arithmetic, harmonic or logarithmic averages, mass weighted averages, e.g., arithmetic, harmonic or logarithmic averages, volume weighted averages, e.g., arithmetic, harmonic or logarithmic averages, surface area weighted averages, e.g., arithmetic, harmonic or logarithmic averages of the relevant physicochemical parameters of the subgroups. Further, the physicochemical parameters of the polymer may be determined by determining one or more of the molar weighted standard deviation, mass weighted standard deviation, volume weighted standard deviation, surface area weighted standard deviation, molar weighted maximum, mass weighted maximum, volume weighted maximum, surface area weighted maximum, molar weighted minimum, mass weighted minimum, volume weighted minimum, surface area weighted minimum, molar weighted sum, mass weighted sum, volume weighted sum, surface area weighted sum, and maximum difference from the physicochemical parameters of the relevant subgroups.

[0102] In step 660, the derived or provided physicochemical parameters of the polymer can be provided to a trained biodegradation model for determining the biodegradability, for example as described with respect to FIG. 4. In general, as already mentioned above, differently trained biodegradation models can also be utilized to determine the biodegradability for different habitats. However, the biodegradation model can also be adapted to determine the biodegradability for more than one biodegradation habitat. The biodegradation model can be trained based on an automatic statistical pre-processing of the training data, in particular the physicochemical parameters of the training polymer, for example using feature engineering. For example, feature engineering can include first determining the physicochemical parameters of a plurality of different polymers of the polymer, for example based on the physicochemical parameters of a subgroup, and pre-selecting from this plurality of physicochemical parameters those that are related with a predetermined probability to the biodegradability of the polymer in a particular habitat. Based on the related physicochemical parameters, a cluster analysis is preferably performed to identify groups of strongly correlated physicochemical parameters. Such groups make it possible to select only one of the members of the group, i.e. one physicochemical parameter of the group, to represent the entire group of physicochemical parameters. Thus, based on the cluster analysis, the number of related physicochemical parameters can be further reduced. The same process can be optionally performed on the habitat descriptors to determine the habitat descriptors most relevant for determining the biodegradability of the polymer in a particular habitat. Based on the remaining physicochemical parameters of the polymer, and optionally also on the habitat descriptors, an application space can be determined and optimized. The application of the trained biodegradation model, for example to a particular habitat or to the physicochemical parameters of the polymer, can then be determined by a space augmented by the training data forming an application space. This space can be optimized, for example, by amending the training data to cover the application space periodically, by removing strong outliers, by adding training data to parts of the space not yet covered, etc. This also makes it possible to maximize the application space. Based on the optimized training data, the biodegradation model is then trained.The biodegradation model may generally refer to sparse, e.g., spline, LASSO regression, PLS, and non-sparse, e.g., ridge regression, tree methods, kernel-based methods, statistical learning models for relating physicochemical parameters of a polymer to biodegradability in a particular habitat. Furthermore, the biodegradation model may further provide a reliability estimate of the determination depending on the respective biodegradation model used. In step 670, the determined biodegradability, i.e., technical application property, may then be provided to a user, e.g., via a user interface.

[0103] FIG. 7 shows a block diagram of an exemplary system architecture of an automated laboratory system 1000 for synthesizing polymers, having a laboratory equipment control device 1102, a network 1150, and synthesis specifications, i.e. recipes, modules 1100 / 1110, and client devices 1108. The automated laboratory system includes a laboratory equipment control device layer 1152 as part of the laboratory equipment control device 1102, and a synthesis specification module layer 1154 associated with a synthesis 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 layers, namely a hardware layer, a middleware layer, and an interface layer. The hardware layer is particularly related to hardware resources such as sensors and actuators for controlling the synthesis of polymers. The middleware is related to any of the known middleware for synthesis operations in a laboratory or plant. One example is LABS / QM, which provides different abstractions for 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 on top of different transport protocols (i.e. UDP, TCP, Telemetry) that allow message exchange between the laboratory equipment control device and the laboratory equipment devices. Such a software architecture makes it possible to control and monitor the laboratory equipment without interacting with the hardware.

[0104] The synthetic 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 recipe, i.e., a synthetic specification, of a polymer that meets the target biodegradability, as described in detail above. In particular, the functions performed by the device as described above may be provided as program code means stored in the mass storage. Furthermore, synthetic specifications of multiple polymers may be stored in the mass storage. 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, MongoDB, etc. The computing layer may include an application layer that allows customizing the functions provided by standard cloud services to perform computing operations based on the target properties. Such functions may include determining a digital representation of a target polymer based on the target biodegradability and the biodegradation model, generating a synthetic specification from the digital representation of the target polymer, and providing the synthetic specification to a laboratory equipment control device as control data.

[0105] The interface layer can implement web services, network interfaces as UDP or TCP, or web socket interfaces. For communication with laboratory equipment control devices, a REST API is implemented.

[0106] The client layer 1156 provides an interface for end users. For end users, the client layer 1156 can run a client-side web application that provides an interface to the composite specification module layer 1154 or the laboratory equipment control device layer 1152. The user can be provided with a UI to select a target biodegradability and a biodegradation habitat for the target biodegradability, which can also include a range of biodegradability values. In other examples, the user can be provided with a UI to select two or more target biodegradabilities and respective values. The application can be configured to allow the user to remotely monitor and control the laboratory equipment control device and operation. In other examples, the client device layer and the composite specification module layer can be integrated into one device. The alternatives described herein are merely illustrative and should not be considered limiting.

[0107] 8 shows a block diagram of an exemplary system architecture of a system and device for generating a biodegradation model for determining biodegradability, including a network 2150, a model generation module 2100 / 2110 that may be considered or includes a training model device, a synthesis 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.

[0108] 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, as described above. Furthermore, the mass storage is configured to store the polymer synthesis specifications and the measured biodegradability for one or more habitats. Such data may be stored in a structured database, such as a SQL database, or a distributed file system, such as HDFS, HBase, NoSQL database, such as MongoDB, etc. The computing layer may include an application layer that allows customizing the functionality provided by standard cloud services to perform computing processes for generating a biodegradation model for determining the biodegradability of the polymer. Such functionality may include receiving, for at least two previously measured polymers, their respective digital representations associated with a synthetic specification, and for each of the at least two previously measured polymers, at least one measurement data of biodegradability in at least one habitat; receiving, in a model generation module, a digital representation of at least one unmeasured polymer; training a model according to the training principles described above based on the digital representations of the at least two previously measured polymers, the measurement data of biodegradability in at least one habitat for each of the at least two previously measured polymers, and preferably a similarity measure between the digital representations associated with the synthetic specification of each of the at least two previously measured polymers and the respective digital representations associated with the synthetic specification of the at least one unmeasured polymer; and providing, via an output interface, a biodegradation model for biodegradability.

[0109] The model generation module layer may be configured to deploy the generated model and the synthesis specification database to the synthesis specification module layer. This may include storing the generated model and the synthesis specification database in a mass storage device associated with the synthesis specification module. The model generation module layer may further be configured to determine a digital representation of the polymer associated with the synthesis specification from the synthesis specification. The digital representation may include a set of polymer physicochemical parameters and polymer physicochemical parameter values ​​associated with the synthesis specification for each measured polymer. One way to derive these polymer physicochemical parameters may be to apply the SMILES algorithm or other principles already mentioned above. If the model is generated based on the digital representation derived from the recipe, the relationship between the synthesis specification and the physicochemical parameters may be stored in a mass storage associated with the model generation module. In such a case, deploying the model includes providing the relationship.

[0110] The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. For communication with client devices, a REST API is implemented in this embodiment. The client layer 2156 provides access to mass storage containing the polymer synthesis specifications and at least one biodegradability for at least two polymers. The client layer further provides an interface for end users. For end users, the client layer 2156 may execute a client-side web application that provides an interface to mass storage associated with the model generation module layer 2154 or the client layer. The user may be provided with a UI for selecting the test method and / or habitat for which the biodegradability is to be determined. The user may further be provided with a UI for selecting the synthesis specification data. The user interface may also provide an option for uploading the selected data to the model generation module layer and, optionally, for starting the model generation.

[0111] FIG. 9 shows an exemplary system 700 for producing a chemical product based on a synthesis specification generated according to the present invention. In this embodiment, the system comprises 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 according to the above-mentioned principles, in particular adapted to execute a computer-implemented method for determining a target polymer and / or synthesis specification based on a determined biodegradability, as described above. The control unit 740 is configured, for example, to receive control data generated according to the present invention, as described above, in particular to receive control data generated based on a synthesis specification of a polymer comprising a target biodegradability. In this embodiment, the control data is provided from a database 730, but in other embodiments, the control data may also be provided from a server or any other computing unit for distributing data. The containers 750, 752 each contain components of a chemical product, such as, for example, a prepolymer, a catalyst, etc. Although typically there are three or more containers, in this embodiment, only two containers are shown for illustrative purposes. Valves 760, 762 are associated with the containers 750, 752. Valves 750, 752 may be controlled to dose the appropriate amount of each component to reactor 770 according to the synthesis specification. A motor 800 of mixer 780 may also be controlled by the control unit according to the synthesis specification. An optional heater 790 may also be controlled according to the synthesis specification. Finally, an outlet valve 810 in fluid communication with the reactor may be controlled by the control unit to provide chemical products to a container or test system 820.

[0112] FIG. 10 exemplarily and diagrammatically shows a possible user interface for interfacing with a processor executing the above-mentioned method for determining a target polymer with a target biodegradability. In this example, an input screen is shown on the left. This input screen allows for defining the target biodegradability, for example in the form of a minimum and a maximum value. In this example, the target biodegradability refers to the percentage of the polymer degraded after 28 days. Optionally, the input can also refer to another target application property, in this example the target kinematic viscosity, and the weighting of the two targets. Furthermore, the input screen allows for providing constraints on the target polymer, for example for providing constraints on the polymer class, which can also be selected via a drop-down menu, as shown. Here, as an exemplary case, the polymer is constrained to the class of polyalkoxylates. Furthermore, as shown in FIG. 10, additional constraints on the polymer can be provided. However, this input option can also be omitted and, as mentioned above, a general synthesis specification can be provided from a database or generated by known methods. Furthermore, the input screen also allows for providing additional or other constraints on the target polymer or the target synthesis specification. Furthermore, the input screen allows the selection of the respective habitat and, optionally, the habitat descriptor value. In this example, the habitat is determined based on the measurements selected for biodegradability indicating that the habitat refers to wastewater. In general, the input may also refer to define further information, such as the habitat descriptor, the intended application, the measurement method, etc. In this example, it may be shown that a good prediction accuracy may be achieved by utilizing, as the polymer type, a descriptor referring to the molecular weight of the polymer, a descriptor referring to the amount of subgroups, and a descriptor referring to the hydrophilicity of the polymer. The values ​​of such polymer descriptors may be determined according to the principles described above for the starting polymer and also for each improved polymer until the target polymer can be found. An exemplary output screen is shown on the right side of FIG. 10. In this example, the output screen provides a target polymer that meets the target biodegradability and also the target kinematic viscosity.Additionally, each component of the polymer is provided with the type of component and associated components, amounts, and block format. Optionally, further information regarding the determined target polymer may also be provided, such as associated synthesis specifications.

[0113] In the following, further details are provided regarding some of the above-mentioned embodiments. In general, in some applications, it is desirable to find a polymer that meets certain technical application properties, such as tensile strength, and also meets requirements regarding biodegradability. For this application, a method is proposed, for example as described with respect to FIG. 5. In this example embodiment suitable for this application, a target requirement for biodegradability may be provided, and further target application properties are provided. Based on the target application properties, a determination model is selected, which determines the physicochemical parameters of the polymer related to the synthesis specification with the application properties. In addition, a further model is selected based on the habitat of the polymer. This biodegradation model relates the habitat information related to the synthesis specification and the physicochemical parameters of the polymer with biodegradability. Based on the requirements of the target application, the physicochemical parameters of the polymer based on the synthesis specification are determined. In an optional step, based on the selected biodegradation model used, additional descriptive values ​​regarding the habitat are required. 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 properties of the polymer, and the determined application properties are compared to the target properties. If the determined biodegradability meets the target biodegradability and the determined application properties meet the target properties, a synthesis specification for the polymer is provided. The synthesis specification may refer to or include control data for controlling the plant producing the polymer. If the target biodegradability and / or target application properties are not met, the target application properties may be reduced and the process may be rerun with reduced target application until the biodegradation requirements are met. A tolerance range for the target application properties may be provided. If no polymer is found that meets the required targets of biodegradability and target application performance, the process may be stopped and the user may be notified.

[0114] The potential expression of biodegradability may be one or more of the following: mineralization, which refers to whether the polymer is completely mineralized or the time until mineralization is achieved; biotransformation, which indicates a change in the chemical structure that causes a particular property of the polymer, such as toxicity, to be lost or the time until this is achieved; and half-life, which refers to the time until 50% of the polymer is degraded. Prominent habitats are oceans, wastewater, and soil. In the case of oceans, the following parameters affect biodegradation: salinity, sediment, water temperature, bacterial culture, etc. In some embodiments, the marine habitat descriptors may be stored in a database together with the geographic location. The geographic location may then be entered and the values ​​of the parameters associated with this geographic location may be retrieved from the database. In the case of wastewater, the following parameters may affect biodegradation: temperature, bacterial count, type of bacteria, enzyme concentration, enzymes. In the case of soil, the following parameters may affect biodegradation: temperature, bacterial count, type of bacteria, enzyme concentration, enzymes.

[0115] Below, some examples of the results of determining biodegradability using the biodegradation models trained and provided as described above are provided. For each example, the biodegradation model was trained for a specific class of polymers using a training data set including 50-100 different polymers in the class of polymers and their respective biodegradation in the respective habitats. Furthermore, each model was trained to determine the biodegradability as the percentage of the polymeric material converted to CO2 after 28 days based on the calculated theoretical oxygen demand of the polymeric material assuming total carbon conversion. Furthermore, to increase comparability, the model was trained according to the standard OECD301-Test for polyalkoxylates in wastewater and according to ISO17556 for polyesters in soil.

[0116] In a first example, a biodegradation model was trained for polyalkoxylates in wastewater and was based on a two-step approach, where in the first step a random forest model was trained and used to select polymer descriptors that were utilized in the second step of a trained linear regression model to determine biodegradability. The trained biodegradation model (in this case a linear regression model) was applied to Plurafac LF 221 using its respective physicochemical properties. The output of the biodegradation model resulted in 91% biodegradation, which means that 91% of the polymeric material was converted to CO2 based on the theoretical oxygen demand of the polymeric material calculated assuming total carbon conversion, and the measured biodegradability of this polymer was reported to be greater than 60%.

[0117] In the second example, a biodegradation model was trained for polyalkoxylates in wastewater in the same manner as in the first example. In particular, the same biodegradation model can be used for both examples. The trained biodegradation model was then applied to Pluriol E 200 using its respective physicochemical properties. The output of the biodegradation model resulted in 96% biodegradation, which means that 96% of the polymeric material was converted to CO2 based on the theoretical oxygen demand of the polymeric material calculated assuming total carbon conversion, and the measured biodegradability of this polymer was reported to be greater than 70%.

[0118] In the third example, a biodegradation model was trained for amine-terminated polyalkoxylates in wastewater in the same manner as in the first example. Notably, the same biodegradation model can be used for both examples. This biodegradation model was then applied to Jeffamine D 230 using its respective physicochemical properties. The output of the biodegradation model resulted in 7% biodegradation, meaning that 7% of the polymeric material was converted to CO2 based on the theoretical oxygen demand of the polymeric material calculated assuming total carbon conversion, and the measured biodegradability of this polymer was reported as 7.2%.

[0119] In a fourth example, a biodegradation model was trained for polyester in soil. In this example, the trained biodegradation model is based on a trained partial least squares model. This biodegradation model was then applied to Lupraphen 1619 / 1 using its respective physicochemical properties. The output of the biodegradation model resulted in 49% biodegradation, which means that 49% of the polymeric material was converted to CO2 based on the theoretical oxygen demand of the polymeric material calculated assuming total carbon conversion, and the measured biodegradability of this polymer is reported to be greater than 60%.

[0120] The expected measurement error relative to the measured value is ±10% for wastewater and ±10% for soil, and the results of the trained biodegradation model are within the range of accuracy suitable for the intended application. Thus, training using a training dataset with data points of only 50 polymers already provides adequate accuracy. Utilizing a dataset with more polymers may lead to even higher accuracy. Thus, the biodegradation model can then be trained accordingly according to the accuracy suitable for the respective application.

[0121] 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.

[0122] For purposes and methods disclosed herein, the operations performed in the processes and methods may be performed in different orders. Moreover, the outlined operations are provided only as examples, and some of the operations may be optionally combined into fewer steps and operations, supplemented with additional operations, or expanded into additional operations, without detracting from the essence of the embodiments of the present disclosure.

[0123] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plurality.

[0124] 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.

[0125] The procedures performed by one or more units or devices, such as providing the physicochemical parameters and the biodegradation model of the polymer, determining the biodegradability, providing the biodegradability, may be performed by any number of other units or devices. These procedures may be implemented as program code means of a computer program and / or as dedicated hardware.

[0126] The computer program product may be stored / 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, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0127] Any unit described herein may be a processing unit that is part of a classical computing system. The 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, and may be volatile, non-volatile, or some combination of both. The term "memory" may include computer readable storage media, such as non-volatile mass storage. If the computing system is distributed, the processing and / or storage capabilities may also be distributed. The computing system may include multiple structures as "executable components." The term "executable components" is a structure well understood in the computing arts as being a structure that may be software, hardware, or a combination thereof. For example, if implemented in software, one skilled in the art will understand that the executable component structure may include software objects, routines, methods, etc. that may be executed on the computing system. This may include both executable components in the heap of the computing system or executable components on a computer readable storage medium. The structure of the executable components may reside on a computer-readable medium such that, when interpreted by one or more processors, e.g., processor threads, of a computing system, it causes the computing system to perform a function. 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 binary that is directly interpretable by a processor, e.g., whether in a single step or multiple steps. In other examples, the structure may be hard-coded or hard-wired logic gates implemented exclusively or nearly exclusively in hardware, such as in a field programmable gate array (FPGA), application specific integrated circuit (ASIC), or other dedicated circuitry.Thus, the term "executable component" refers to structures well understood by those skilled in the computing arts, whether implemented in software, hardware, or a combination thereof. All embodiments herein are described in terms of operations performed by one or more processors 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 executing the computer-executable instructions that make up the executable component. A computing system may also include communication channels that allow 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 allow the transport 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, for example, 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 transport the desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computing system or combination thereof. Although not all computing systems require a user interface, in some embodiments a computing system includes a user interface system for interfacing with a user. The user interface serves as an input or output mechanism to a user, for example, via a display.

[0128] Those skilled in the art will appreciate that at least a portion of the present invention may be implemented in a networked computing environment 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 glasses, etc. The present invention may also be implemented in a distributed system environment in which local and remote computing systems, linked, for example, over a network, by either hardwired data links, wireless data links, or a combination of hardwired and wireless data links, perform tasks together. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0129] Those skilled in the art will 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. When distributed, a cloud computing environment may be distributed internationally within an organization and / or may have components held across multiple organizations. For purposes of this specification and the claims that follow, "cloud computing" is defined as a model that allows on-demand network access to a shared pool of configurable computing resources, e.g., 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 of the figures includes various components or functional blocks that may implement various embodiments disclosed herein, as described. The various components or functional blocks may be implemented on a local computing system or may be implemented on a distributed computing system that includes elements that reside in the cloud or 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 system shown in the figures may include more or fewer components than those shown in the figures and may combine some of the components as circumstances permit.

[0130] Any reference signs in the claims should not be construed as limiting the scope.

[0131] The present invention relates to a method for determining a composite specification including a target biodegradability. A target biodegradability is provided, indicative of the biodegradation properties of a polymer. A digital representation of a potential target composite specification is provided, indicative of the physicochemical properties of the polymer. A habitat is provided, indicative of habitat descriptive values ​​of habitat descriptors. A habitat-based model is provided, adapted to determine the biodegradability of the polymer in the habitat. The biodegradability of the potential target polymer is determined based on the provided model and the digital representation. The determined biodegradability is then compared to the target biodegradability and i) the potential target polymer is determined as the target polymer, or ii) a new potential target composite specification for the potential target polymer is provided and the determination of biodegradability is repeated using the new potential target composite specification.

Claims

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

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

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

4. 3. The method of claim 2, wherein providing the potential target synthetic specifications based on the provided target technical application properties comprises utilizing a decision model adapted to determine technical application properties of a polymer based on the digital representation of the polymer, the decision model being a data-driven model parameterized to determine the technical application properties associated with the polymer based on the digital representation of the polymer including the polymer descriptor.

5. 10. The method of claim 1, wherein said providing said target synthesis specification for said target polymer comprises providing a control signal adapted to control an industrial plant to produce said target polymer in accordance with said target synthesis specification.

6. 10. The method of claim 1, wherein the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a calcareous 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, enzyme environment, and enzyme environment.

9. 2. The method of claim 1, wherein habitat descriptor values ​​for the habitat descriptors are stored in association with respective geographic locations, and wherein providing the biodegradable habitat refers to providing the geographic location of the habitat and retrieving the habitat descriptor values ​​for the geographic locations from storage.

10. 1. An interface method for providing an interface, 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 synthesis specification for the polymer 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 based biodegradation model for parameterizing a biodegradation model, said training method (300) comprising: providing (310) training data associated with a predetermined biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers; and b) biodegradability for the respective biodegradation habitat associated with each training polymer; 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 biodegradability of a polymer based on physicochemical properties of the polymer; providing the trained biodegradation model (340); A training method, including:

12. 1. An apparatus for determining a target synthesis specification indicative of a target polymer having a target biodegradability, the apparatus comprising: a target biodegradable providing unit (111) for providing a target biodegradability, wherein the biodegradability indicates the biodegradation characteristics of a polymer; a digital representation providing unit (112) for providing digital representations of potential target synthetic specifications indicative of or related to physicochemical properties of said polymer; 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 polymer 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 the biodegradation of the polymer in each biodegradation habitat, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats so as to determine the biodegradation of the polymer based on the physicochemical properties of the polymer; a biodegradability determination unit (115) for determining the biodegradability of the potential target polymer based on the selected biodegradation model and the digital representation; an iterative control unit (116) for comparing the determined biodegradability of the potential target polymer with the target biodegradability, and based on the comparison, i) determining the potential target polymer as the target polymer and determining the potential target synthetic specification as the target synthetic specification, or ii) providing a new potential target synthetic specification for the potential target polymer and repeating the determination of the biodegradability using the new potential target synthetic specification for the potential target polymer; An apparatus comprising:

13. 1. An interface device for providing an interface, comprising: an input interface unit for receiving as input via a user interface a target biodegradability, a starting digital representation, and a habitat, and for providing the received target biodegradability, starting digital representation, and the habitat to the device of claim 12; a result interface for providing the habitat descriptor values ​​of the polymer 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 parameterizing a biodegradation model, the training device (120) comprising: a training data providing unit (121) for providing training data associated with a predetermined biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers indicating physicochemical properties of each of the training polymers; and b) biodegradability for the respective biodegradation habitat associated with each training polymer; 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 biodegradability of a polymer based on physicochemical properties of the polymer; a trained model providing unit (124) for providing said trained biodegradation model; A training device comprising:

15. A computer program for determining a target polymer with a target biodegradability, the computer program comprising program code means for causing an apparatus according to claim 12 to carry out the method according to any one of claims 1 to 9.

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