Method for determining the biodegradability of polymers

Abstract

The present invention relates to a method for determining the biodegradation degree of a polymer. A digital representation of a polymer is provided, which indicates physicochemical properties of the polymer. Further, habitats are provided, which indicate habitat descriptor values ​​that influence the biodegradability of the polymer. The habitat descriptors indicate environmental properties of the habitats. A biodegradation model is provided based on the habitats, the biodegradation model is adapted to determine the biodegradation degree of the polymer in each habitat, the biodegradation model being a data-driven model parameterized with respect to the habitats such that the biodegradation model may determine the biodegradation degree of the polymer based on the physicochemical properties. The biodegradation degree of the polymer is then determined based on the provided biodegradation model and the digital representation of the polymer.

Description

[Technical field]

[0001] FIELD OF THEINVENTION The present invention relates to a method, an apparatus and a computer program product for determining the degree of biodegradation that can be used to verify the biodegradability of a polymer.Furthermore, 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 used by the method, the apparatus and the computer program product for determining the degree of biodegradation of a polymer.Furthermore, the present invention relates to a method and an apparatus for providing an interface for the determination of the degree of biodegradation of a polymer. [Background technology]

[0002] 2. Background of the Invention In general, polymers are widely used in industrial products and / or everyday items due to their wide range of applications. The use of polymers includes coatings, personal care products, detergents, lubricants, packaging, and foams, among others. However, this wide range of applications leads to a huge amount of waste containing used polymers. In fact, it is mostly due to the durability of polymers that they have become popular in many applications, but their durability in waste also creates several problems, especially in waste management. In particular, if non-biodegradable polymers are not properly recovered in the intended waste stream, this can result in increased microplastic pollution and bioaccumulation in the environment. Therefore, not only is there a need for polymers that degrade, but there is also a need to take knowledge of the biodegradability of polymers into account in the early stages of the product design process. Therefore, it would be advantageous to provide the possibility to predict the biodegradability of polymers accurately and in a computationally inexpensive manner. Summary of the Invention [Problem to be solved by the invention]

[0003] Summary of the Invention It is an object of the present invention to provide a method, an apparatus and a computer program product that allows an accurate determination of the biodegradation degree of a polymer, which is computationally inexpensive and can be robustly applied to new polymers. Furthermore, it is also an object of the present invention to provide a training method, a training apparatus and a computer program product that can be used in the method, the apparatus and the computer program product and that can provide a biodegradation model that can be trained to provide good determination accuracy by utilizing less computational resources. [Means for solving the problem]

[0004] In a first aspect of the present invention, a computer-implemented method for determining a biodegradation degree that can be used to verify the biodegradability of a polymer is provided, the method comprising: i) providing a digital representation of a polymer indicative of or associated with physicochemical properties of the polymer; ii) providing biodegradation habitats, the biodegradation habitats indicative of habitat descriptor values ​​of habitat descriptors that affect the biodegradability of the polymer in the respective habitats, the habitat descriptors indicative of environmental properties of the habitats; iii) providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine the biodegradation degree of the polymer in the respective biodegradation habitats, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats such that the biodegradation model may determine the biodegradation degree of the polymer based on the physicochemical properties; and iv) determining the biodegradation degree of the polymer based on the provided biodegradation model and the digital representation of the polymer.

[0005] Since the biodegradation model is specifically adapted to determine the biodegradation of the polymer in a specifically provided biodegradation habitat characterized by the respective habitat descriptor values ​​that affect the biodegradation of the polymer in the respective habitat, the biodegradation of the polymer in each habitat can be determined very accurately. Furthermore, since the biodegradation model is specifically trained for one or more specific biodegradation habitats, less training data is required for training, making the biodegradation model more flexible with respect to determining the biodegradation of new polymers that are not part of the training data set. Thus, the method allows for accurate determination of biodegradation that is computationally inexpensive and can be flexibly applied to new polymers. Furthermore, the above method allows for essentially immediate results to be provided, whereas the test methods traditionally used to test the biodegradation of polymers are very time-consuming and can take months or years to obtain results. Thus, not only can the technical requirements for the determination of biodegradation be reduced, but the time required to design new biodegradable products can also be significantly reduced. Furthermore, by offering a simple possibility to take into account the accurate determination of the biodegradability of a product already in the design process, it becomes possible to design products in such a way that polymer waste, especially in the form of microplastics, can be avoided, and in particular it can be ensured that the polymers used in the product biodegrade in the respective expected environment, for example in marine habitats.

[0006] Furthermore, the training of the respective biodegradation model may be improved since physicochemical properties, including physicochemical information of the polymer (e.g., solubility in water or octanol, or quantum chemical information of the polymer, such as the molar mass of the polymer), are utilized. In particular, the utilization of physicochemical properties allows such models to be trained with less training data, since by using the physicochemical properties, part of the correlation information that needs to be learned is already presented to the model. This further allows reducing the tests and experiments required to provide the training data set.

[0007] The development of new chemical products adapted to the requirements of the application is a major challenge in the modern chemical industry. In recent years, further requirements have arisen related to the environmental impact of chemical products during their life cycle. One important aspect of the environmental impact is the prevention of microplastics and bioaccumulation. Microplastics are a growing problem that can be avoided if the polymeric material is biodegradable. Traditionally, a series of standardized tests are used to evaluate biodegradability. There are various tests for biodegradability with specified conditions (e.g. ISO13432, ISO14852, ISO14855, ISO17556, and OECD301). Standardized tests often make a compromise between time-efficient tests (as short as 14 days and as long as 24 months) and real-life conditions. In fact, to reduce the test time, higher temperatures than the real-life conditions are often used. Companies developing new polymers have to devote significant resources to self-assessment and auditing of the sustainability of their products. The entire biodegradability evaluation, including laboratory space and equipment, becomes costly and time-consuming. Therefore, there is a need to identify the biodegradability of new materials early in the development process. The proposed method for determining biodegradability disclosed herein allows for faster and more efficient development of new materials. The biodegradability can be determined at an early stage, even before the synthesis of the polymer. This allows to determine whether the polymer is suitable for market entry or not, which leads to a shorter time to market. This also reduces resource demand and waste generation, since the polymer does not need to be synthesized to determine the biodegradability. The proposed method provides a digital twin of the measurement of the biodegradability of the polymer.

[0008] Furthermore, standard measurements and tests of biodegradability are often time-consuming, for example involving waiting periods of months or even 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 results are available.

[0009] Furthermore, due to the large number of polymers that may potentially be suitable for a particular application and that are often even under-explored, today, product technology engineers are given the technical task of finding a polymer that is not only suitable for a particular application but also meets the respective target properties, especially the target biodegradability, and must synthesize and test a huge amount of possible polymers, or must consult huge data sets and libraries in which possible polymers are stored, in order to find each polymer that may fit the application. Even when using sophisticated experimental design methods, a very large number of possible polymers still have to be synthesized and experimentally tested. In this regard, the above method allows a user (e.g., a product technology engineer) to help automatically and much faster find potentially suitable polymers. In particular, by using the above method, the user only needs to synthesize and test potentially suitable polymers that are determined to be highly likely to meet the respective target properties, especially the target biodegradability. Thus, unnecessary synthesis and testing of polymers can be avoided. Thus, the method allows a user to perform the technical task of finding a suitable polymer for a technical application more quickly and more efficiently.

[0010] The method refers to a computer-implemented method and thus may be carried out by a general-purpose 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, the biodegradation degree of a given polymer. A biodegradable polymer refers to a polymer that can be degraded by biological processes, in particular a biodegradable polymer may refer to a polymer that can be digested by bacteria and / or fungi to result in environmentally friendly products, i.e. decomposed into non-polluting residues, such as by producing inorganic carbon and / or biomass. In general, the determined biodegradation degree may refer to any quantification of the biodegradation degree of a polymer. For example, the determined biodegradation degree may refer to only one value (e.g. the half-life of the polymer in each habitat) or to multiple values ​​(e.g. the time-dependent degradation function of the polymer in a particular habitat). Preferably, the biodegradation degree refers to a percentage value of biodegradation after a given time frame. In general, biodegradability refers to a measure of the degradation, or decomposition, of a polymer caused by biological processes, i.e. processes involving biological materials, particularly microorganisms, involved in the degradation process. Thus, biodegradability does not refer to a purely chemical degradation process that does not involve microbial activity. Biodegradability is an intrinsic property of a polymer. In this context, intrinsic property of a polymer refers to properties of a polymer that arise from and thus reflect the properties of the polymer with respect to a particular situation, i.e. structure, composition, etc. In particular, biodegradability reflects the properties of a polymer when present in a particular biologically active environment. For example, the biodegradability of a polymer preferably refers to any one of the mineralization properties, biotransformation properties, and / or degradation of the polymer after a particular 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. Biodegradability can be used to verify the biodegradability, or decomposition properties, of a polymer, in particular, for example with respect to a particular biodegradation environment, i.e. biodegradation habitat.For example, the biodegradation rate can be compared to the biodegradation rate required for a particular application, thereby determining whether each polymer is suitable for each application.

[0011] In general, the polymer can be any polymer. Preferably, the target polymer is a synthetic polymer. In one embodiment, a 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 may be considered as a subunit of a polymer. A polymer can be prepared from monomers by commonly 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 crosslinked polymer. A polymer can be chemically modified after polymerization.

[0012] In a first step, the method includes providing a digital representation of the polymer, which is indicative of the physicochemical properties of the polymer. In particular, providing may refer to receiving the digital representation from a user's input, for example using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the digital representation is already stored. Furthermore, providing may also include receiving the physicochemical properties, for example via a network connection from another source, and providing the received physicochemical properties as a digital representation. Furthermore, indicating a physicochemical property of a polymer or being associated with a physicochemical property of a polymer is defined as making information of the physicochemical property accessible. 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, via which the physicochemical property can be accessed, or the digital representation may refer to an identifier associated with the physicochemical property and making it possible to utilize a respective look-up storage to access the physicochemical property. Furthermore, the digital representation may also refer to information making it possible to derive the physicochemical property using one or more known relationships. For example, synthetic specifications or polymer structural formulas may be available as digital representations so that respective physicochemical properties can be derived using known chemical and physical laws and relationships.

[0013] In general, throughout the following description, reference to a parameter or characteristic includes reference to both the respective quantity and a specific value of that quantity, unless explicitly defined otherwise. For example, a parameter being temperature always refers to the quantity being temperature and to a specific value of temperature being set to the quantity. In most cases, the explicit value of a parameter is not generally mentioned, since it may be different in different embodiments and application cases. However, providing a parameter or characteristic generally means providing information about the quantity, e.g., that the value is temperature, as well as the value of the quantity or characteristic itself.

[0014] In particular, the physicochemical properties of the polymer can be quantified by the physicochemical parameters of the polymer. Preferably, the digital representation indicates and / or comprises the physicochemical parameters of the polymer, preferably referring to polymer descriptors, the physicochemical parameters of the polymer indicating the physicochemical properties of the polymer. In particular, the physicochemical parameters of the polymer indicate 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 such that the physicochemical properties can be derived in the form of polymer descriptors, for example, by providing a representation of the polymer, in which 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, brand name, IUPAC name, chemical identifier, and CAS number of the polymer.

[0015] In a preferred embodiment, the physicochemical parameters of the polymer are parameters that quantify the physicochemical properties of a subgroup of the polymer. In this embodiment, a digital representation may also be provided to allow the physicochemical parameters of the polymer to be derived by determining subgroups of the polymer and determining the physicochemical parameters of the polymer based on the physicochemical properties of the determined subgroups. Generally, a subgroup refers to a portion of a polymer, where all subgroups of the polymer together form a polymer. For example, a subgroup may refer to a portion of a polymer, where the subgroups are linked together in series along a chain or network to form a polymer. Preferably, a subgroup of a polymer refers to a repeating unit that represents a portion of the polymer that, when repeated, results in a complete polymer chain. However, in some cases, a subgroup may also refer to a single, non-repeated portion of a polymer. Furthermore, it is preferred that a subgroup includes a repeating portion, for example, a subgroup of a polymer may include a repeating core that is also present in other subgroups and further additional portions 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, a polymerized monomer refers to a monomer after polymerization, sometimes also referred to as a "mer unit" or "mer". In particular, polymerized monomers do not refer to the monomers present in the reaction mixture before polymerization, i.e. raw materials, but to repeat 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. The inventors have found that polymerized monomers in particular allow for the determination of the polymer descriptors from the subgroup descriptors of the polymerized monomers, which allows for an accurate determination of the degree of biodegradation. In a preferred embodiment, the digital representation of the polymer includes subgroups that are 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 and type of atoms characteristic of the subgroup and their bonding in the polymer. Furthermore, in addition to or instead of the molecular model of the subgroup treating the subgroup as a monomeric structure, a molecular model can 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.

[0016] In general, if the digital representation of a polymer does not directly include the physicochemical parameters of the polymer, the physicochemical parameters of the polymer are preferably determined by determining subgroups of the polymer. For example, the respective subgroups of the polymer may be determined using known methods. However, the determination of the subgroups of the polymer is preferably performed according to the embodiment of the invention described below. In particular, the subgroups are preferably determined such that the polarisation of the bonds between the atoms of the different subgroups in the polymer is as small as possible, preferably such that the bond order is as small as possible (e.g., single C-C bonds). In addition, the subgroup representing the polymer preferably contains the same number of active non-hydrogen atoms as the polymer. The subgroup may also contain, besides the active atoms, further atoms that may be ignored when calculating the subgroup's descriptor. Furthermore, the subgroups are preferably determined such that polymers containing moieties built using different polymerization techniques are sufficiently covered and satisfy the above-mentioned conditions. Polyethers used as components of polyurethanes are an example. In general, a database or archive may be created in which multiple reactions between polymer moieties are recorded, and the subgroups may be derived from the respective structures of the reactions. For example, specific chemical languages ​​such as SMILES and SMARTS notations can be utilized to easily derive subgroups of polymers. For example, a database of reaction SMARTS can be created and then 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 be directly derived and, for example, RDkit can be used to determine the SMILES of the subgroups, i.e., the number of atoms and bonds, from the SMILES of the monomers.

[0017] The determined subgroups of the polymer are associated with physicochemical parameters of the subgroups that quantify the physicochemical properties of the subgroups in the polymer, which preferably also refer to the subgroup descriptors. In particular, if the physicochemical parameters of the polymer are not directly provided by the digital representation, the physicochemical parameters of the polymer are preferably determined for each of the subgroups by determining the physicochemical parameters of the respective subgroups and determining the physicochemical parameters of the polymer for the subgroups based on the physicochemical parameters of the subgroups, for example by averaging. Thus, the method preferably comprises first providing or determining the subgroups for the polymer from the digital representation of the polymer, then determining or providing the physicochemical parameters of the subgroups, i.e. the values ​​of the parameters that quantify the physicochemical properties of the subgroups, and then determining the physicochemical parameters of the polymer based on the physicochemical parameters of the subgroups of each polymer.

[0018] Preferably, the physicochemical parameters of the polymer refer to polymer descriptors that refer to at least one of structural, counting, structural fragment lists, fingerprints, graph invariants, 3D and / or high-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 the molar mass in particular describes the biodegradability of the 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, so that the physicochemical parameters of the subgroups can also refer to the same descriptors as above. However, the physicochemical parameters can also be derived without the use of 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.

[0019] The structural descriptors 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, protic, polar, and dispersive contributions, Abraham parameters, retention index, TPSA, receptor binding constant, Michaelis-Menten constant, inhibition constant, mutagenicity, LD50, bioconcentration rate, toxicity, biodegradation profile, and viscosity.

[0020] The counting descriptor may refer to any of the following: sum of atomic electronegativities, sum of atomic polarizabilities, amount of components, ratio of amount of components, number of atoms and non-H atoms, number of H, B, C, N, O, P, S, halogen elements, and heavy atoms, number of H donor and H acceptor atoms, number of bonds, number of non-H bonds or multiple bonds, 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, fraction of rotatable bonds, and number of conformers.

[0021] The polymer descriptor, which refers to a list of structural fragment descriptors, may refer to at least one of a list of molecular fractions, a list of functional groups, a list of bonds, and a list of atoms. The fingerprint descriptor preferably includes at least one of MACCS keys (preferably in bit or total form), Morgan fingerprints and other circular fingerprints (preferably in bit or total form), topological torsion, atom pairs, infrared and related spectra, fingerprint numbers, PubChem fingerprints, substructure fingerprints, and Klekota-Roth fingerprints. The graph invariant / topological index descriptor preferably includes at least one of topostructural index and topochemical index.

[0022] 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, protic, 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 orders, atomic radii. In particular, the physicochemical parameters of the polymer preferably refer to 3D descriptors including at least one of the following: total atomic volume sum, average volume per atom, total atomic area sum, 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, protic, polar and non-polar surface area, excitation energy and intensity, infrared and / or UV absorption bands, reactivity measurements, particle charge, and / or charge surface area. The high-dimensional descriptors preferably used 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, protic, polar, and / or non-polar surface area, charge distribution, conformational dipole moment, and molecular refraction. Preferably, high-dimensional descriptors including at least one of the following are used: solubility, vapor pressure and activity coefficient, surface activity, conformer-weighted H donor, H acceptor, protic, polar, and non-polar surface area, and charge distribution.

[0023] The method further includes providing a biodegradable habitat, the biodegradable habitat indicating a habitat descriptor value of a habitat descriptor that affects the biodegradability of the polymer in the respective habitat. In particular, providing may refer to receiving a biodegradable habitat from a user's input, for example using a respective input unit. Furthermore, providing may also refer to accessing a storage unit in which the biodegradable habitat is already stored. Furthermore, providing may also refer to pre-setting a biodegradable habitat. For example, if the method is used in a very specific situation where only one biodegradable habitat is appropriate, the respective biodegradable habitat may be pre-set and thus does not need to be provided as a specific input. Furthermore, providing may also include receiving a habitat descriptor value of a habitat descriptor directly from another source, for example via a network connection, and providing the received habitat descriptor value of the habitat descriptor as the biodegradable habitat. The provided biodegradation habitat can refer to a general habitat, for example a wastewater habitat, and the respective habitat descriptor values ​​of the habitat descriptors of this habitat are already stored in the respective storage, which can be accessed. However, the provided biodegradation habitat can also directly include the respective habitat descriptor values ​​of the biodegradation habitat to provide further specification of the biodegradation habitat, for example the deep ocean floor. Furthermore, providing the biodegradation habitat can include providing a digital representation of the biodegradation habitat, and the digital representation can indicate the respective habitat descriptor values ​​of the habitat descriptors that affect the biodegradability of the polymer in the respective habitat.

[0024] In general, the habitat descriptors indicate the environmental characteristics of the habitat. In particular, the environmental characteristics of the biodegradation habitat may affect the biological activity in the respective habitat, for example, the presence, growth, or absence of certain microorganisms. Thus, the environmental characteristics defined by the habitat descriptors also indirectly affect the biodegradability of the polymer in the respective habitat. For example, if a polymer is biodegraded by a specific microorganism that requires a certain salinity, the polymer will biodegrade quickly in a habitat that provides such a salinity, such as a marine habitat, but will biodegrade much slower in a habitat that does not provide the appropriate salinity, such as wastewater. Again, indicating a habitat descriptor for a habitat or being associated with a habitat descriptor for a habitat is defined as making the information of the habitat descriptor accessible. For example, a habitat may directly include a habitat descriptor, for example in the form of a respective quantity value. However, a habitat can also be a link to the respective habitat descriptor through which the habitat descriptor can be accessed, or a habitat can refer to an identifier that is associated with a habitat descriptor and allows the respective lookup storage to be used to access the habitat descriptor. Furthermore, a habitat can also refer to information that allows the habitat descriptor to be derived using one or more known relationships. For example, the geolocation of an environment can be used together with a habitat to allow the respective habitat descriptor to be derived using knowledge of the respective geolocation.

[0025] Preferably, the biodegradation habitat refers to any one of marine habitat, wastewater habitat, freshwater lake habitat, anaerobic habitat, compost habitat, or soil habitat. In a preferred embodiment, the biodegradation habitat refers to marine habitat, and the habitat descriptor refers to at least one of salinity, 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 freshwater lake habitat, and the habitat descriptor refers to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to wastewater, and the habitat descriptor refers to at least one of water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test time, solid content, and enzyme environment. Furthermore, in this habitat, sludge can also be a separate 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 descriptor refers to at least one of solid content, pH, nutrient content, heavy metal content, and microbial community. In a further preferred embodiment, the biodegradation habitat refers to soil, and the habitat descriptor refers to at least one of temperature, composition (e.g., sand content and / or clay content), pH value, water 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 descriptor refers to at least one of temperature, compost activity, pH value, moisture content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, solid content, water holding capacity, and enzyme environment. In general, the habitat can also refer to the habitat of the standard test used to determine the biodegradation degree of the polymer. For example, the standard tests defined by ISO13432, ISO14852, ISO14855, ISO17556, and OECD301 also define the specific habitat in which biodegradation takes place.Thus, providing a biodegradable habitat may also include providing one of the standard tests, e.g., selecting via user input, and the habitat descriptors refer to specific characteristics of the test, i.e., the test environment and thus the test habitat. Additionally, a habitat may also be defined by the biodegradability of a reference polymer or other reference chemical. In this case, the habitat may be provided by providing a reference material and its biodegradability, where the reference material and its biodegradability indicate the habitat descriptors.

[0026] 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 being trained for one biodegradation habitat, in particular for different values ​​or ranges of the habitat descriptor values ​​of the biodegradation habitat. Then, based on the provided biodegradation habitats exhibiting habitat descriptor values, a respective suitable biodegradation model may be selected from the plurality of biodegradation models. For example, if the indicated habitat descriptor value falls within the range of the habitat descriptor values ​​on which the biodegradation model was trained, the biodegradation model is suitable. For example, a respective look-up table may be provided that facilitates a comparison between the indicated habitat descriptor value and the descriptor range on which the biodegradation model stored in the storage was 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 able to select the respective biodegradation models to be utilized. In general, the possible stored biodegradation models refer to biodegradation models that have already been parameterized based on the respective training data sets of one or more habitats. Since the training data sets utilized to parameterize the biodegradation models are historical data, as will be described in more detail below, the biodegradation models may be trained, and thus generated, at any time before and after determining the specific biodegradability for a specific polymer, and stored in the respective databases. However, it will be appreciated that the training, and thus the generation, of the biodegradation models may also be performed at the time when it is determined that, for example, a specific biodegradation model for a specific habitat is required.

[0027] The provided biodegradation model is then adapted to determine the biodegradation degree of the polymer in each biodegradation habitat. In particular, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitat so as to determine the biodegradation degree of the polymer based on the physicochemical properties, preferably based on the physicochemical parameters of the polymer that quantify the physicochemical properties represented by the digital representation. In this specification, the term "such that" should be interpreted as the parameterization adapting and thus enabling the biodegradation model to provide a biodegradation degree for the habitat when the physicochemical parameters of the polymer are provided as input. 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 the biodegradation degree. This allows that the digital representation of the synthetic specification can be determined based on the target biodegradation degree. In this specification, the term "data-driven" is used to emphasize that the model is mainly based on the respective input data 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. In this context, regression models based on linear regression, random forests, boosting trees, Lasso, ridge regression, and MARS algorithms, among others, have been found to be suitable for most applications, while random forests, logistic regression, and SVM algorithms, among others, have been found to be suitable for classification models. Preferably, the biodegradation model is based on a neural network algorithm. In general, the biodegradation model is parameterized during a training process in which polymer descriptors derived from parameters that quantify physicochemical properties, preferably physicochemical properties of the polymer, are utilized together with the biodegradation degree corresponding to a specific biodegradation habitat.Based on such a training data set specific to a biodegradation habitat, e.g., a particular habitat descriptor range and / or value, the respective parameters of the data-driven model can be determined using known training methods such that the biodegradation model can also determine the biodegradability of polymers that are not part of the training data set.

[0028] 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 provided as input with the habitat descriptor values ​​in addition to the physicochemical parameters of the polymer, and the biodegradation model may be trained such that the biodegradation model determines the biodegradability further based on the habitat descriptor values. This has the advantage that the biodegradability may be determined even more accurately, especially in cases where the biodegradability is strongly dependent on the specific habitat descriptor values ​​of the habitat. For example, in marine habitats, the temperature or salinity may vary greatly in different regions of the world, which may lead to different biodegradation rates for some polymers. Thus, in such cases, it may be advantageous to directly provide the habitat descriptor values ​​as inputs to the biodegradation model. However, instead of providing the habitat descriptor values ​​as inputs to the biodegradation model, it is also possible to train two different biodegradation models and indirectly treat different regions as different habitats.

[0029] The method further comprises determining the biodegradability of the polymer based on the provided biodegradation model and the digital representation of the polymer. In particular, if the digital representation of the polymer comprises physicochemical parameters of the polymer, the physicochemical parameters of the polymer are provided as input to the biodegradation model, and the biodegradation model provides the determined biodegradability as output. If the digital representation does not directly comprise the physicochemical parameters of the polymer, determining the biodegradability may also comprise first determining the physicochemical parameters of the polymer, for example as described above. The physicochemical parameters of the polymer thus determined may then be provided as input to the biodegradation model. The determined biodegradability application property may then be provided, for example, to an output unit or to a computing unit for further processing. Preferably, the determined biodegradability is provided such that the biodegradability can be used for validation of the polymer, particularly with respect to the intended application. Preferably, the determined biodegradability is provided such that it can be verified whether the biodegradability of the polymer meets a predetermined target. Even more preferably, providing comprises validating the polymer based on the determined biodegradability. In one embodiment, based on the validation, a specification of the polymer may be provided together with the biodegradability.

[0030] Preferably, providing the biodegradability leads to a further processing that utilizes the determined biodegradability, in such a case providing as a separate step may be omitted and replaced by the processing of the determined biodegradability.

[0031] The determination of the degree of biodegradation using the biodegradation model can be considered as a virtual measurement of the degree of biodegradation. In particular, the biodegradation model is based on measurement data, e.g., the measured biodegradation of the polymer used to train the biodegradation model. Thus, the biodegradation model includes the information provided by these previous measurements. Furthermore, the physicochemical parameters may also refer to the measured properties of the polymer in some cases. Thus, the determined biodegradation of a new polymer determined using the biodegradation model can also be considered as being based at least in part on the measurement results.

[0032] Preferably, the processing of the biodegradability includes determining a control signal for controlling a manufacturing process based on the determined estimated biodegradability. The manufacturing process can refer to the manufacturing process of the polymer or the biodegradation process of the product in which the polymer is utilized. For example, if the determined biodegradability indicates that the polymer will biodegrade at an appropriate rate in a particular environment, i.e., habitat, the generation of the control signal can include generating a control signal for controlling a waste management facility to provide this habitat, for example by providing a respective temperature. In a preferred embodiment, the control signal indicates a machine-executable synthesis specification for the polymer, particularly if the comparison indicates that the determined biodegradability of the polymer is within a predefined range around the provided target biodegradability.

[0033] Furthermore, the process of processing the biodegradation may also refer to a step of selecting one or more polymers based on the respective determined biodegradation. For example, if the respective biodegradation degrees have been determined for a plurality of possible polymers, selecting may include comparing the biodegradation degrees of the different polymers with predefined selection criteria and selecting the polymer whose determined biodegradation degree meets these criteria. In particular, in one embodiment, the method includes receiving a target biodegradation degree of the polymer, comparing the received target biodegradation degree with the determined biodegradation degree, and providing a control signal in response to the comparison. The control signal may refer to any signal that allows further control of the technical system. For example, the control signal may be adapted to control an interface to provide the result of the comparison on the interface.

[0034] Preferably, the biodegradability treatment is a biodegradability verification. In a preferred embodiment, the comparison refers to a verification of a target biodegradability, and the verification is positive if the determined biodegradability is within a predefined range around the target biodegradability. In this case, the control signal may be adapted to simply control a user interface to provide an indication that the verification result is positive or negative. However, preferably, the control signal refers to a recipe, i.e., a synthesis specification, of one or more polymers that meet a specified target biodegradability, i.e., that have been verified as positive. A recipe, i.e., a synthesis specification, is generally defined as an instruction of how a polymer can be synthesized. In particular, the recipe comprises starting materials and respective parameters for polymerization from the starting materials. Preferably, the control signal comprises the recipe in a form that directly enables the automatic control of a respective industrial system or work equipment for producing the polymer. In particular, if the result of the comparison refers to the determined biodegradability being within a predefined range around the target biodegradability, it is preferred that the control signal indicates a machine-executable synthesis specification of the polymer.

[0035] In one embodiment, the method includes validating the polymer based on the determined biodegradability. In particular, the validation may be performed as described above. Furthermore, in a preferred embodiment, the validation includes determining a biodegradation classification of the polymer based on the determined biodegradability. For example, the classification may refer to classifying whether the polymer is biodegradable or not. Furthermore, the classification may refer to determining which of a plurality of (e.g., three) biodegradation classifications the polymer belongs to. Preferably, the classification is based on one or more predefined biodegradation thresholds that determine the classification of the polymer. For example, a polymer with 0% to 25% biodegradation determined in a habitat within a predefined time period may be classified as non-biodegradable, a polymer with more than 25% and less than 60% biodegradation determined in a habitat within a predefined time period may be classified as a moderately biodegradable polymer, and a polymer with more than 60% biodegradation determined in a habitat within a predefined time period may be classified as highly biodegradable.

[0036] In a preferred embodiment, the method further comprises providing a synthesis specification as a digital representation of the polymer and determining physicochemical properties from the synthesis specification, for example in the form of physicochemical parameters of the polymer, preferably as polymer descriptors. In particular, the synthesis specification, i.e. recipe, comprises information on the polymer synthesis of the polymer, for example on the starting materials and the process by which the respective starting materials are covalently linked to form the polymer chain or network of the polymer. The method then comprises determining physicochemical properties, for example polymer descriptors, from the synthesis specification. Optionally, from the synthesis specification, subgroups can be determined and then the physicochemical parameters of the polymer can be determined based on the physicochemical parameters of the subgroups, for example from a database or using known physicochemical parameter determination algorithms. In a preferred embodiment, further from the synthesis specification, the catalyst and / or non-reactive process components used are determined. In this case, this information is preferably also used by the biodegradation model to determine the degree of biodegradation together with the physicochemical properties, in particular the physicochemical parameters of the polymer. Preferably, physicochemical parameters are also determined for the catalyst and / or non-reactive process components, and the respective physicochemical parameters are also used to determine the polymer physicochemical parameters. Preferably, the physicochemical parameters of the catalyst and / or non-reactive process components refer to the amount (e.g. molar mass, molar percentage, etc.) of the respective components and are taken into account to determine the polymer physicochemical parameters for the polymer.

[0037] In a preferred embodiment, determining the physicochemical parameters of the polymer from the synthesis specification includes identifying the type and amount of subgroups based on the synthesis specification, for example as the physicochemical parameters of the subgroups, and determining the physicochemical parameters of the polymer based on the identified type and amount of the subgroups. In general, the type of subgroup may refer to a given type or classification associated with a specific physicochemical property, i.e., the physicochemical parameters of the subgroups and thus the specific physicochemical properties of the polymer containing these subgroups. However, the general physicochemical properties of the polymer and thus the physicochemical parameters of the polymer may also depend on the amount of the subgroups present in the polymer, so this amount may also be taken into account. In a preferred embodiment, the determination of the type and amount of the subgroups takes into account the information provided by the synthesis specification indicating the type of polymerization. The information on the type of polymerization that may be utilized may refer, for example, to whether the polymerization refers to polycondensation, polyaddition, radical polymerization, cationic polymerization, anionic polymerization, or coordination chain polymerization. Preferably, for each type of polymerization, a rule is predefined that may be applied to determine the subgroups of the polymer. For example, rules may be predefined that determine which functional groups of monomers in a synthesis specification react with which functional groups of a synthesized polymer, using which prioritization. The rules may be based, for example, on reaction kinetic considerations. Based on the number and type of functional groups to be polymerized, subgroups may be determined, and the number and type of subgroups may be calculated.

[0038] In one embodiment, determining the amount of the subgroups includes determining the amount of at least one of amide, ester, thioester, carbonate, ether, amine, urea, urethane, thiourethane, isocyanurate, biuret, allophanate, acetal, Michael adduct, radical polymerized double bond, siloxane, silane, silazane, phosphazene groups, and residual amine, aldehyde, ketone, epoxide, aziridine, isocyanate, alcohol, thiol, carboxylic acid, acyl halide, α,β-unsaturated carbonyl group, α,β-unsaturated carboxyl, and double bond groups in the polymer based on a synthesis specification.

[0039] In one embodiment, the method further comprises providing a biodegradation test method, the provided biodegradation test method indicating a standardized biodegradation test method for experimentally determining the biodegradability of a chemical substance, and a biodegradation model is further provided based on the provided biodegradation test method. Generally, there are a number of standardized biodegradation test methods for testing the biodegradability of a chemical substance. For example, such test methods can be found in DIN or ISO standards. Furthermore, by providing a biodegradation test method and providing a biodegradation model trained by the provided biodegradation test method, it is possible to determine a biodegradation that is easily comparable to, for example, the respective measured biodegradation by utilizing the respective test method. In particular, in this embodiment, the biodegradation model is preferably trained based on a data set, and the test method for which the biodegradation was determined is clearly specified, so that the biodegradation model can be specifically trained for one or more test methods.

[0040] In one embodiment, a target application of the polymer is further provided, which refers to the intended application of the polymer, and the biodegradation habitat is provided based on the target application. The target application of the polymer may refer to the intended application context of the polymer, for example, if the polymer is intended to be used as a coating, in personal care products, in detergents, in lubricants, in agriculture, or in product packaging. Such a target application may indicate 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 personal care products, it is very likely that the polymer will be found in an aqueous environment sooner or later. In this way, each target application may indicate a respective biodegradation habitat. In this regard, a predefined list may be provided in the storage in which each target application and the corresponding biodegradation habitat are stored. The target application for the polymer may then be provided, for example, by providing a list of target applications to the user and allowing the user to select each target application, each target application being linked to one or more biodegradation habitats. The degree of biodegradation may then be determined for each of the biodegradation habitats associated with the target application, or again, the user may select each of the biodegradation habitats associated with the target application. Additionally or alternatively, information may be provided that indicates the intended end-of-life treatment of the polymer. For example, the end-of-life treatment may indicate whether the polymer is intended to be biodegraded in a particular environment or should undergo a particular treatment, for example, in a bioreactor. Thus, as described above, the intended end-of-life treatment information may also be utilized to determine the biodegradation habitat for the polymer.

[0041] In one embodiment, further information is provided indicating the accessible surface area of ​​the polymer in its intended form, and the biodegradation model is further trained to determine the biodegradation degree based on the accessible surface area, and the method further comprises further determining the biodegradation degree based on the accessible surface area. For example, the information may indicate whether the intended product is provided in a solid, crushed, foamed, pelletized, or any other form. Preferably, the information indicates the surface area of ​​the product per mass or the geometric shape of the smallest independent part of the product. Generally, the biodegradation degree of a polymer is an intrinsic property of the polymer, but the exact timing of the biodegradation of a product containing the polymer may also depend, for example, on the surface area that can be accessed by the microbial components of the habitat responsible for the biodegradation. Thus, further determining the biodegradation degree based on the surface area of ​​the product containing the polymer can increase the accuracy in predicting the biodegradation degree of the final product.

[0042] In one embodiment, the habitat descriptor values ​​of the habitat descriptors are stored in association with the respective geolocations, and providing the biodegradation habitat refers to providing the geolocations of the habitats and retrieving the habitat descriptor values ​​of the geolocations from the storage. The geolocations may refer, for example, to coordinates or other locality identification information. For example, the geolocations may refer to city names, country names, country region names, ocean names, geographical features, etc. Based on such geolocations, the respective habitat descriptors may be stored, for example, average values ​​or minimum and maximum values ​​of the habitat descriptors. Thus, by providing a geolocation, the respective habitat descriptor values ​​for this geolocation may be provided. This has the advantage that the user does not need to know the exact habitats or the exact habitat descriptor values ​​in a certain area. Thus, the user simply provides the locations where it is expected that the polymer may biodegrade in this area.

[0043] In one embodiment, the physicochemical parameters of the polymer indicated by the digital representation of the polymer refer to at least one of recipe parameters from the polymer synthesis, structural descriptors, counting descriptors, lists of structural fragments, fingerprints, graph invariants, 3D descriptors, and / or high-dimensional descriptors that indicate the chemical nature of the polymer. The respective associations of the digital representations with polymer descriptors (e.g., previously calculated) or further information about the polymer may already be stored and associated 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 polymers corresponding to the brand name may already be stored, for example, in the storage of the owner of the brand name.

[0044] In a preferred embodiment, the polymer belongs to at least one of the following polymer types: polyalkoxylates, polycondensates, addition polymers, vinyl-based polymers, natural polymers, polymer dispersions, polymer foils, biopolymers, polysilicones, resins, rubbers, and polyketones, and the biodegradation model is specifically trained for each polymer type to which the polymer belongs. 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 from multiple polymer types.

[0045] In a preferred embodiment, the polymer belongs to polyalcoholates and the habitat is a wastewater habitat, particularly a sludge habitat.Furthermore, in this embodiment, it is preferred that the physicochemical property comprises at least one of molar mass, composition, chemical moiety, solubility in water, and partition coefficient, more preferably, the physicochemical property comprises molar mass, even more preferably, the molar mass, composition, and even more preferably, the molar mass, composition, and partition coefficient.

[0046] In a preferred embodiment, the polymer belongs to polycondensates, preferably polyesters, polyamides and phenolic resins, 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, composition, chemical moiety, solubility in water, partition coefficient, measure of stability to hydrolysis 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.

[0047] In a preferred embodiment, the polymer belongs to addition polymers, preferably polyurethanes and polyureas, and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical property comprises at least one of composition, chemical moiety, ratio of chemical moieties, ratio of composition, and crystallinity, more preferably, the physicochemical property comprises composition, even more preferably, the composition and ratio of chemical moieties, even more preferably, the composition, ratio of chemical moieties, ratio of composition.

[0048] In a preferred embodiment, the polymer belongs to vinyl polymers, preferably polyvinyl, polyacrylate, polystyrene, polyvinyl ether and 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, solubility in water, 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.

[0049] In a preferred embodiment, the polymer belongs to natural polymers, preferably polysaccharides, polynucleotides, lignin, suberin, cutin, cutan, melanin, natural rubber, and polypeptides, 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 solubility in water, and partition coefficient, more preferably, the physicochemical properties include chemical moiety, and even more preferably, the chemical moiety and molar mass.

[0050] In a preferred embodiment, the polymer belongs to a polymer dispersion and the habitat is a marine habitat or a soil habitat.Furthermore, in this embodiment, it is preferred that the physicochemical properties include at least one of composition, chemical moiety, solubility in water, and particle size, more preferably, the physicochemical properties include composition and particle size, and even more preferably, the composition, chemical moiety, and particle size.

[0051] In a preferred embodiment, the polymer belongs to a polymer foil, and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical properties preferably include at least one of the following: composition, molar mass, chemical moiety, solubility in water, crystallinity, and surface area / volume ratio, more preferably, the physicochemical properties include composition and surface area / volume ratio, even more preferably, the composition, chemical moiety, and surface area / volume ratio, even more preferably, the composition, chemical moiety, crystallinity, and surface area / volume ratio.

[0052] In a preferred embodiment, the polymer belongs to polysilicone, and the habitat is soil habitat, wastewater habitat, especially sludge habitat, or marine habitat.Furthermore, in this embodiment, the physicochemical property preferably includes at least one of composition, molar mass, chemical moiety, solubility in water, partition coefficient, and surface area / volume ratio, more preferably, the physicochemical property includes molar mass, even more preferably, includes molar mass and composition, and even more preferably, includes molar mass, composition, and partition coefficient.

[0053] In a preferred embodiment, the polymer belongs to resin and the habitat is soil habitat or marine habitat.Furthermore, in this embodiment, it is preferred that the physicochemical property includes at least one of composition, molar mass, chemical moiety, solubility in water, crystallinity, and surface area / volume ratio, more preferably, the physicochemical property includes molar mass, even more preferably, the molar mass and composition, even more preferably, the molar mass, composition, and surface area / volume ratio.

[0054] In a preferred embodiment, the polymer belongs to rubber and the habitat is a soil habitat or a marine habitat.Furthermore, in this embodiment, the physicochemical property preferably includes at least one of composition, chemical moiety, solubility in water, crystallinity, and surface area / volume ratio, more preferably, the physicochemical property includes composition, even more preferably, the composition and surface area / volume ratio, even more preferably, the composition, chemical moiety, crystallinity, and surface area / volume ratio.

[0055] 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 area / volume ratio. More preferably, the physicochemical properties include chemical moiety, and even more preferably, molar mass, chemical moiety, and solubility in water. For polymers belonging to polyalkoxylates, polycondensates, vinyl polymers, or polysilicones, the physicochemical properties preferably further include at least one of partition coefficient and moiety. For polymers belonging to polycondensates, addition polymers, polymer foils, resins, rubbers, or polyketones, the physicochemical properties preferably further include at least one of crystallinity and a measure of stability to hydrolysis. For polymers belonging to resins, rubbers, addition polymers, or polysilicones, the physicochemical properties preferably further include surface area / volume ratio.

[0056] In a further aspect, an interface method for providing an interface is presented, the interface method comprising: i) receiving as input a digital representation and a habitat via a user interface and providing the received digital representation and habitat to a processor executing the above method; and ii) providing as a result a determined biodegradability of the polymer to a user via the user interface, the result being received from the processor executing the above method.

[0057] In a further aspect, a computer-implemented training method for training a data-driven based biodegradation model for parameterization of a biodegradation model is provided, the training method comprising: i) providing training data associated with a given biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers, each of the training polymers being indicative of physicochemical properties; and b) a degree of biodegradation of each biodegradation habitat associated with each training polymer; ii) providing a data-driven based trainable biodegradation model; iii) 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 digital representations of the polymers, in particular based on the physicochemical properties of the polymers, preferably based on the physicochemical parameters, more preferably polymer descriptors, of the polymers represented in the digital representations; and iv) providing a trained biodegradation model.

[0058] In a further aspect, an apparatus for determining a biodegradation degree of a given polymer is presented, the apparatus comprising: i) a digital representation providing unit for providing a digital representation of the polymer indicative of physicochemical properties of the polymer or associated with the physicochemical properties of the polymer; ii) a habitat providing unit for providing biodegradation habitats, the biodegradation habitats indicative of habitat descriptor values ​​of habitat descriptors influencing the biodegradation of the polymer in the respective habitats, the habitat descriptors indicative of environmental properties of the habitats; iii) a model providing unit for providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine a biodegradation degree of the polymer in the respective biodegradation habitats, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats to determine a biodegradation degree of the polymer based on the physicochemical properties; and iv) a determining unit for determining a biodegradation degree of the polymer based on the selected biodegradation model and the digital representation of the polymer.

[0059] In a further aspect, an interface device for providing an interface is presented, the interface device comprising: i) an input interface unit for receiving as input a digital representation and a habitat via a user interface and providing the received digital representation and habitat to said device; and ii) a result interface for providing as a result the determined biodegradability of the polymer to a user via the user interface, the result being received from said device.

[0060] In a further aspect, a training device for training a data-driven based biodegradation model for parameterization of a biodegradation model is presented, the training device comprising: i) a training data providing unit for providing training data associated with a given biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers, each of the training polymers being indicative of physicochemical properties; and b) a biodegradability of each biodegradation habitat associated with each training polymer; ii) a trainable model providing unit for providing a data-driven based trainable biodegradation model; iii) 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 digital representations of the polymers, in particular based on the physicochemical properties, preferably the physicochemical parameters of the polymers represented by the digital representations; and iii) a trained model providing unit for providing a trained biodegradation model.

[0061] In a further aspect of the present invention, the use of the above method is presented, which is used to determine the biodegradability of a given polymer, either i) polyesters, particularly those referred to as polymers used in multi-film and packaging applications (e.g. aromatic-aliphatic copolyesters), ii) polyalkoxylates, particularly those used in home and personal care applications, iii) polymers referred to as 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 as polyurethanes used in adhesives.

[0062] In a further aspect of the invention, a system is provided comprising: i) a control signal comprising a polymer synthesis specification indicating one or more components for producing a polymer, the control signal being generated according to the method described above; and ii) one or more components indicated by the synthesis specification in the control signal.

[0063] In a further aspect of the invention, there is provided the use of control signals generated according to the above method for controlling manufacturing processes, particularly manufacturing processes involving the production of polymers.

[0064] In a further aspect of the invention, a control signal is provided, the control signal being generated in accordance with the above method. Preferably, the control signal comprises a machine executable synthesis specification for producing a target polymer.

[0065] In a further aspect, a computer program product for determining the biodegradability of a given polymer is provided, the computer program product comprising program code means for causing an apparatus as described above to carry out the method as described above.

[0066] In a further aspect, a computer program product for training a biodegradation model is presented, said computer program product comprising program code means for causing said training device to execute said training method.

[0067] It is to be understood that the above method, the above device and the above computer program product have similar and / or identical preferred embodiments, in particular as defined in the dependent claims, and furthermore, the above training method, the above training device and the above training computer program product also have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0068] It shall be understood that a preferred embodiment of the invention may also be any combination of the dependent claims or the above embodiments with the respective independent claims.

[0069] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. [Brief description of the drawings]

[0070] [Figure 1]1 illustrates, in a schematic and exemplary manner, one embodiment of a system comprising an apparatus for determining the biodegradation rate of a polymer. [Diagram 2] 1 shows, in a schematic and exemplary manner, a flow chart of a method for determining the biodegradability of a polymer. [Diagram 3] 1 shows, generally and exemplarily, a flow chart of a method for training a biodegradation model for determining the biodegradation degree of a polymer. [Figure 4] 1 shows, generally and exemplarily, a flow chart of one embodiment of a method for determining the biodegradability of a polymer. [Diagram 5] 1 shows, diagrammatically and exemplarily, an optional extension of the method for determining the biodegradability of a polymer. [Figure 6] 1 shows, in a schematic and exemplary manner, a block diagram of the system architecture of a system and device for determining the biodegradability of a polymer. [Figure 7] 1 shows, in a schematic and exemplary manner, a block diagram of the system architecture of a system and device for determining the biodegradability of a polymer. [Figure 8] 1 shows, in a schematic and exemplary manner, a block diagram of the system architecture of a system and device for determining the biodegradability of a polymer. [Figure 9] 1 shows schematic and exemplary output and input screens of an exemplary user interface; [Figure 10] 2 shows, diagrammatically and exemplarily, a further flow chart of a preferred and more detailed embodiment of a method for determining the degree of biodegradation. [Figure 11] 2 shows, diagrammatically and exemplarily, a further flow chart of a preferred and more detailed embodiment of a method for determining the degree of biodegradation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0071] 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 the biodegradation degree of a polymer based on a digital representation of the polymer and a provided biodegradation habitat. Furthermore, the system 100 comprises a training device 130 for training a biodegradation model used in the apparatus 110, a database 140 in which the results of the determination of the biodegradation degree of the polymer can be stored, and a manufacturing system 120 for manufacturing products, in particular products comprising the polymer, that can be controlled using the determined biodegradation degree.

[0072] The apparatus 110 comprises a digital representation providing unit 111, a habitat providing unit 112, a model providing unit 113, a determination unit 114 and, optionally, an output and / or control unit 115 which may be adapted to output the determined biodegradability and / or to provide a control signal for controlling a manufacturing process of the manufacturing system 120 based on the determined biodegradability.

[0073] The digital representation providing unit 111 is adapted to provide a digital representation of the polymer for which the biodegradation is to be determined, which indicates the physicochemical parameters of the polymer, in particular the polymer descriptors. The digital representation providing unit 111 may for example refer to an input unit, through which a user may input the respective digital representation. Furthermore, the digital representation providing unit 111 may refer to or be part of a user interface, which allows a user to interact with the device 110 and / or the database 140. However, the digital representation providing unit 111 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 physicochemical parameters of the polymer, which indicate parameters quantifying the physicochemical properties of the respective polymer. However, instead of directly providing the physicochemical parameters of the polymer, it is also possible to provide a synthesis specification of the polymer. In this case, the digital representation providing unit 111 is preferably further adapted to determine the physicochemical parameters of the polymer from the synthesis specification. In particular, the digital representation providing unit 111 is preferably adapted to identify the type and amount of polymer subgroups from the synthesis specification and to determine the physicochemical parameters of the polymers based on the identified type and amount of the subgroups. In particular, the digital representation providing unit 111 may be adapted to determine the physicochemical parameters of the respective subgroups for each identified subgroup, for example by accessing a database in which the respective physicochemical parameters are stored for a plurality of most relevant subgroups. The physicochemical parameters of the polymer may then be determined for the subgroups based on the physicochemical parameters of the subgroups, preferably also based on the determined amount and type of the subgroups, for example by taking a weighted average of the physicochemical parameters of the subgroups. The digital representation providing unit 111 is then adapted to provide a digital representation comprising the physicochemical parameters of the polymer to the determining unit 114 or the like.

[0074] The habitat providing unit 112 is adapted to provide biodegradable habitats. The habitat providing unit 112 may for example refer to an input unit, where a user may input the respective biodegradable habitat. For example, a user interface may be provided that allows the user to select from several predefined biodegradable habitats. In a preferred embodiment, the habitat providing unit may be communicatively coupled to or refer to a user interface that allows the user to indicate a geolocation, for example by marking a location on a map, by indicating coordinates, or by providing a name of an area, for example a political or geological area. The habitat providing unit may then be adapted to provide a biodegradable habitat based on the geolocation. For example, if the geolocation 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 a biodegradable habitat.

[0075] In general, the biodegradation habitats indicate habitat descriptor values ​​of the habitat descriptors that affect the biodegradability of the polymer in the respective habitat. In particular, the habitat descriptors indicate the environmental characteristics of the habitat, for example, in marine habitats, salinity may strongly affect the biodegradability of the polymer in the marine habitat. In general, the chemical effect of the habitat descriptors on the polymer is not important in this application, since the biodegradability is determined. Thus, it is the effect of the habitat descriptors on the ecology of the habitat, in particular the microbial community of the habitat, that indirectly affects the biodegradability.

[0076] The model providing unit 113 is adapted to provide a biodegradation model based on the provided biodegradation habitat. In particular, the model providing unit 113 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 in terms of habitat descriptor values ​​or ranges that define for which biodegradation habitats the respective biodegradation model is suitable. For example, a look-up table may be provided that allows the model providing unit to select which biodegradation model is suitable based on the biodegradation habitat, for example based on the habitat descriptor values ​​of the biodegradation habitat. However, the model providing unit 113 may also comprise or point to an input unit that can receive a biodegradation model, for example by user selection or user input indicating which biodegradation model should be used.

[0077] The biodegradation model is a data-driven model that is parameterized to determine the degree of biodegradation of a polymer based on a digital representation, in particular based on physicochemical parameters of the polymer that indicate the physicochemical properties associated with the polymer. 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 may be based on any of the following algorithms: a neural network algorithm, a linear regression algorithm, a LASSO algorithm, a ridge regression algorithm, a MARS algorithm, a random forest algorithm, and a boosting tree algorithm. The classifier-based model algorithm may be based on any of the following algorithms: a random forest algorithm, a logistic regression algorithm, and an SVM algorithm. The inventors have found that linear regression, random forest, neural network, and MARS-based algorithms are particularly suitable for most applications.

[0078] The biodegradation model may be trained, for example, using 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 the polymers for a plurality of training polymers, and b) a biodegradation degree associated with each training polymer in one or more different habitats. Preferably, the biodegradation degree provided for each training polymer in the training data refers to a biodegradation degree measured according to the same measurement method. In general, the training data may be designed to cover a given 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 given polymer type for a given habitat. Known methods for designing and optimizing training data for a given habitat space may be utilized such that the habitat space is sufficiently covered by the training data and random outliers are avoided.

[0079] 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 in a training process to train the biodegradation model. For example, the trainable biodegradation model may already be stored in a storage unit that the model providing unit 132 can access to provide it. 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 biodegradation degree of the polymer based on the digital representation. In general, any known training algorithm for training a data-driven, in particular machine learning based, model may be utilized. Preferably, during the training of the biodegradation model, the physicochemical parameters of the polymer that have the greatest influence on the biodegradation degree in each habitat are also determined, and then the model is trained based on these most influential physicochemical parameters. To determine these most influential physicochemical parameters, for example, a cluster analysis or PCA analysis tool 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 for optimizing the training data in the application space can then be applied, for example to cover the application space with as little training data as possible.

[0080] The training device 130 then comprises a trained model providing unit 134 adapted for providing 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, respectively, are stored. However, the trained model providing unit 134 may also be adapted for providing the trained biodegradation model directly, for example to the biodegradation model providing unit 113 of the device 110.

[0081] In all cases, the biodegradation model providing unit 113 is adapted to subsequently provide the appropriate trained biodegradation model to the characterization unit 114. The determination unit 114 may then utilize the biodegradation model and the provided digital representation to determine the biodegradation degree. In particular, the determination unit 114 may be adapted to utilize the physicochemical parameters of the polymer indicated by the digital representation as input to the trained biodegradation model already described above and then provide a decision on the trained biodegradation degree as output. An output unit, for example a display, may then be adapted to output the determined biodegradation degree. However, the output unit may additionally or alternatively be adapted to provide the determined biodegradation degree to a database 140 for storing the polymer in association with the determined biodegradation degree for future use. In particular, the output unit may be adapted to select the respective polymer based on a predefined criterion regarding the biodegradation degree, when the biodegradation degree for different polymers has already been determined and stored, for example, in a storage unit, i.e., in the database 140. The output unit may then be adapted to provide and / or output the selected polymer and its biodegradation degree. This is particularly suitable when a user is looking for a polymer from a number of candidate polymers that has a particular degree of biodegradation in one or more habitats.

[0082] Optionally, the apparatus 110 may comprise a control unit 115 adapted to provide a control signal for controlling a manufacturing process of the manufacturing system 120 based on the determined biodegradation degree. In particular, the control unit 115 is preferably adapted to receive a target biodegradation degree of the polymer, compare the received target biodegradation degree with the determined biodegradation degree and provide a control signal in response to the comparison, preferably to provide a control signal indicative of the use or production of the polymer whose biodegradation degree has been determined. Furthermore, the control signal may indicate a machine-feasible synthesis specification of the polymer whose biodegradation degree has been determined if the result of the comparison indicates that the determined biodegradation degree is within a predefined range around the target biodegradation degree. However, the control unit 115 may also be adapted to control a manufacturing process of another product based on the determined biodegradation degree. For example, it may be adapted to provide a control signal indicative of a machine-feasible synthesis specification of another product that utilizes or includes the respective polymer. Furthermore, the control unit 115 may provide a control signal for controlling a habitat for biodegrading the polymer, for example in a waste treatment facility. For example, target biodegradability may be met for certain habitat descriptors, and the control unit 115 may be adapted to provide control signals that control the facility such that these habitat descriptor values ​​are met.

[0083] Fig. 2 shows a schematic and exemplary flow chart of a method for determining the biodegradation degree of a polymer. The method 200 comprises a first step 210 of providing a digital representation of the polymer, which represents the physicochemical parameters of the polymer. In particular, the provision of the digital representation in this step may follow the principles described above with respect to the digital representation providing unit 111. Furthermore, in a step 220, biodegradation habitats are provided, which represent the habitat descriptor values ​​of the habitat descriptors influencing the biodegradation of the polymer in the respective habitat. For this step 220, the principles described above with respect to the habitat providing unit 112 may also be applied. Furthermore, in a step 230, a biodegradation model is provided, which is adapted to determine the biodegradation degree of the polymer based on the digital representation. As already described in more detail above, providing a biodegradation model may also refer to selecting a biodegradation model based on the provided biodegradation habitat. Furthermore, the biodegradation model is a data-driven model parameterized with respect to the biodegradation habitats, so as to be able to determine the biodegradation degree of the polymer based on the physicochemical parameters of the polymer. Generally, steps 210, 220 and 230 may be performed in any order or even simultaneously. In a next step 240, the biodegradation degree is determined based on the provided digital representation of the polymer and the biodegradation model. In an optional step 250, the biodegradation degree may then be provided to a user interface, for example such that the determined biodegradation degree of the polymer is displayed on a display. However, in step 250, the method may additionally or alternatively comprise generating a control signal that allows control of a manufacturing process of a product, for example a polymer or a product comprising a polymer, as already described in detail above.

[0084] 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 discussed with respect to FIG. 2. In general, the method 300 may be executed by each unit of the training device 130 described with respect 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) polymer physicochemical parameters of a plurality of training polymers, and b) biodegradation degrees associated with each training polymer in each biodegradation habitat, for example for a particular habitat descriptor value. In particular, the training data may be provided according to the principles described above with respect to the training data providing unit 131 described with respect 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 even simultaneously. Next, the method 300 further comprises a step 330 of training the provided data-driven based biodegradation model based on the provided training data, e.g. by varying parameters of the data-driven based trainable biodegradation model such that the trained biodegradation model is adapted to determine a degree of biodegradation of a polymer based on a 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 described with respect to FIG.

[0085] In the following, a more detailed preferred example of the above method and the corresponding device is described. An exemplary embodiment of the method may consist of the steps described below. With FIG. 4, a schematic and exemplary flow chart of an exemplary embodiment of the method is provided. In this exemplary embodiment, the method starts with requesting a digital representation of a new polymer, for example via a user interface. Furthermore, a target application of the new polymer may be requested, for example also via a user interface. The target application may refer, for example, to how the new polymer, for which the digital representation is provided, should be utilized in a product, or which waste treatment is expected for the polymer. Each target application then indicates, for example, a respective biodegradation habitat. For example, a list may be presented to the user via a user interface, from which the user may select the respective target application, and based on the respective selection, further a selection of the biodegradation habitat associated with the target application may also be provided for the user to select. Based on the target application, in particular based on the biodegradation habitat indicated by the target application, a respective biodegradation model, i.e. a biodegradation model, may be selected. Optionally, further preselected conditions indicated by the selected biodegradation model may be requested. For example, the biodegradation model may be adapted to utilize additional descriptors, such as optional habitat descriptors, application constraints, etc., which may be required as needed and enable the biodegradation model to determine the biodegradability with more accuracy or specifically for the constraints. Furthermore, physicochemical parameter values ​​of the polymer may be derived from the provided digital representation, and the physicochemical parameters of the polymer may also depend on the provided target application. Further details on the possibility of deriving the physicochemical parameter values ​​of the polymer are described below with respect to FIG. 5. By utilizing the selected biodegradation model and the derived physicochemical parameter values, a respective determined biodegradation degree, i.e. a respective target performance, may be provided.FIG. 10 shows a schematic and exemplary illustration of the same methodology, but with a specific target technical application property being biodegradation.

[0086] Fig. 5 shows a schematic and exemplary preferred method 500 for deriving physicochemical parameter values ​​(referred to in this example as polymer descriptors) from a digital representation of a new polymer. In a first step 510, a digital representation of the polymer is provided. The digital representation may directly include the polymer descriptor, in which case the steps up to step 550 shown in Fig. 5 may be omitted. However, in many cases the polymer descriptor must first be determined based on the provided digital representation, in such a case the polymer descriptor refers for example to a recipe for the synthesis of the polymer or to a chemical representation of the polymer indicating the chemical components and bonds in the polymer. In this step 510, the digital representation may include any one or more of the following information: amounts of monomeric components, amounts of non-monomeric components like initiators, fillers, additives, reaction conditions like temperature, vessel, pressure, stirring speed, condition profile (e.g. temperature profile, pH value, solvent), feed profile, type of polymerization (e.g. radical, cationic, anionic, polycondensation, polyaddition, polyether formation), amounts of components, conditions, and post-treatment like temperature and feed profile, type of post-treatment (e.g. radical, cationic, anionic, polycondensation, polyaddition, polyether formation), chemical information about the components like mixture, combination of non-polymeric pure compounds, composition of polymeric pure compounds based on subgroups, combination of monomers related to subgroups in polymeric pure components, and for block copolymers, further information about which block each monomer and reactive prepolymer is included in, and for structured / layered materials and composites, further information about which phase / layer each component is included in. In optional step 520, reactive components and subgroups may also be derived from the digital representation (eg, from a recipe) if such information is not provided directly from the digital representation.

[0087] If the information provided indicates the presence of a mixture, in a next step the mixture is decomposed into its pure components and each polymer component is treated as an input polymer. Furthermore, the polymer composition may also be converted to mole %, weight %, volume %, or absolute moles, as appropriate.

[0088] In the next step 530, 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 binding information of the non-polymeric pure compounds using SMARTS, e.g. via a KNIME workflow. Also, the binding information of all possible subgroups can be derived from the binding information of the non-polymeric pure compounds by using reaction SMARTS, e.g. also via a KNIME workflow.

[0089] After the subgroups and their types are determined, in step 540, the type of the descriptor to be used may be provided. However, the descriptor may also be determined without first selecting the type of subgroup. In order to reduce the computational resources of the method, in step 541, it is preferably determined whether the subgroup descriptors associated with each type of subgroup are already stored in the database, for example, whether an entry for a subgroup with the same binding information is already present in the database. If the subgroup descriptors are already stored in the database, for example, in step 544, the respective associated subgroup descriptors may be directly downloaded. If the determined type of subgroup is not stored in the database, for example, in step 542, the subgroup descriptors associated with each type of subgroup may be determined. For example, a 3D structure of each type of subgroup may be derived based on the binding information, and an automatic calculation of the subgroup descriptor may be initiated, for example using a computer cluster, or an already existing machine learning determination may be used as the subgroup descriptor. In general, if a calculation for a new subgroup is required, in step 543, the results are preferably stored in the database after the calculation is completed. Optionally, further subgroup descriptors may be provided from subgroup topological analysis, quantum chemical calculations, molecular dynamics calculations, coarse-grained methods, finite element calculations, and reaction kinetic simulations. In particular, polymer reaction engineering methods may be used to derive subgroup descriptors that allow for the microstructure of the polymer to be taken into account.

[0090] In step 531, the amount of subgroups, i.e. the amount of each type of subgroup, is determined, for example based on the provided recipe information of the polymer, and provided in step 532. 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 obtained from the non-polymerizable components, and such determined amount can be added to the count of the number of optionally non-polymerized polymerizable groups of the subgroup of polymerizable components based on the composition of the polymerizable components to determine the resulting amount. Furthermore, the amount of polymerizable groups originating from agents used for post-treatment after polymerization is preferably excluded from the resulting amount.

[0091] However, although it is preferred that the polymer descriptors are derived from the polymer subgroups, in other embodiments of the invention the polymer descriptors may also be derived in other ways, for example by directly determining the polymer descriptor from the complete polymer. Furthermore, the polymer descriptors for the respective polymers may also already be stored in a storage unit that allows to access the database and to read the corresponding polymer descriptor, such that derivation of the polymer descriptor from the digital representation of the polymer may refer to determining the information of the polymer from the digital representation.

[0092] Optionally, the derived amounts of subgroups can be used to further interpret the polymer composition, for example, the total number of polymerized functional groups (e.g., double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups) and functional groups formed (e.g., amide groups, ester groups, thioester groups, urea groups, urethane groups, thiourethane groups, ether groups) can be determined. Also, the molar weighted total number of polymerized functional groups, the mass weighted total number of polymerized functional groups, the total number of residual functional groups (e.g., double bonds, amine groups, alcohol groups, thiol groups, carboxylic acid groups, isocyanate groups, epoxide groups), the molar weighted total number of residual functional groups, the mass weighted total number of residual functional groups, the sum of all residual functional groups, the ratio between functional groups after polymerization, the number of crosslinks in the polymer, the mole fraction of crosslinks in the polymer, optionally mass weighted, the average number of atoms per subgroup, optionally by weight, the average number of non-H atoms per subgroup, optionally by weight, the average number of bonds per subgroup, optionally by weight, the average number of bonds between non-H atoms per subgroup, optionally by weight, the average number of rotators per subgroup, optionally by weight, the average number of rotators between non-H atoms per subgroup, optionally by weight, the rings per subgroup, the average number of blocks, optionally by weight, the average polar surface area per subgroup, 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 the area weighted HLB value, the HLB value of the block with the smallest HLB value, optionally the area weighted HLB value, the HLB value of the block with the largest HLB value, optionally the area weighted HLB value, the HLB value of the first block, optionally the area weighted HLB value, the HLB value of the last block, optionally the area weighted HLB value, the mass of the first block, the mass of the last block, the area of ​​the block with the smallest HLB value, the area of ​​the block with the largest HLB value, the difference in the HLB values ​​of the blocks, optionally 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.

[0093] In step 550, the determined amount and type of subgroups and associated subgroup descriptors may be utilized to calculate a polymeric descriptor. For example, the polymeric descriptor may be determined by one or more of a molar-weighted (e.g., arithmetic, harmonic, or logarithmic) average, a mass-weighted (e.g., arithmetic, harmonic, or logarithmic) average, a volume-weighted (e.g., arithmetic, harmonic, or logarithmic) average, a surface-area-weighted (e.g., arithmetic, harmonic, or logarithmic) average of the associated descriptors of the subgroups. Additionally, the polymeric descriptor may be determined by determining one or more of a molar-weighted standard deviation, a mass-weighted standard deviation, a volume-weighted standard deviation, a surface-area-weighted standard deviation, a molar-weighted maximum, a mass-weighted maximum, a volume-weighted maximum, a surface-area-weighted maximum, a molar-weighted minimum, a mass-weighted minimum, a volume-weighted minimum, a surface-area-weighted minimum, a molar-weighted sum, a mass-weighted sum, a volume-weighted sum, a surface-area-weighted sum, and a maximum difference from the associated subgroup descriptors.

[0094] In step 560, the derived or provided polymer descriptors can then be provided to a trained biodegradation model for determining biodegradation, for example as described with respect to FIG. 4. The biodegradation model can be trained based on an automated statistical pre-processing of the training data, in particular the polymer descriptors to be trained, for example using feature engineering. For example, feature engineering can include first determining a number of different polymer descriptors for the polymer, for example based on subgroup descriptors of subgroups, and pre-selecting from this number of descriptors those that are relevant with a given probability to the biodegradation of the polymer in a particular habitat. Based on the relevant descriptors, a cluster analysis is preferably performed to identify groups of highly correlated descriptors. Such groups make it possible to select only one of the members of the group, i.e. only one of the descriptors of the group, to represent the entire group of descriptors. Thus, based on the cluster analysis, the number of relevant descriptors can be further reduced. The same process can be optionally performed on the habitat descriptors to determine the habitat descriptors that are most relevant to the determination of the biodegradation of the polymer in a particular habitat. Based on the remaining polymer descriptors, and optionally also on the habitat descriptors, an application space can be determined and optimized. Then, for example, the application of the trained biodegradation model to a particular habitat or polymer descriptor can be determined by the space spanned by the training data forming the application space. This space can be optimized, for example, by periodically correcting the training data so that it covers the application space, by removing extreme outliers, by adding training data to parts of the space that are not yet covered, etc. This can also maximize the applicability space. Then, a biodegradation model is trained based on the optimized training data. The biodegradation model can generally refer to sparse models (e.g., spline, LASSO regression, PLS) and non-sparse models (e.g., ridge regression, tree methods, kernel-based methods, statistical learning models) for relating polymer descriptors to biodegradation in a particular habitat. Furthermore, the biodegradation model can further provide a reliability estimate of the decision depending on the respective biodegradation model used.In step 570, the determined biodegradability, i.e. technical application property, can be provided to a user, for example via a user interface. Figure 11 shows the same method, in a schematic and exemplary manner, but specialized for the target technical application property being biodegradable.

[0095] FIG. 6 shows a block diagram of an exemplary system architecture of an automated laboratory system 1000 for synthesizing polymers using a laboratory equipment control device 1102, a network 1150, and a synthesis specification (i.e., recipe) module 1100 / 1110, and a client device 1108. The automated laboratory system includes a laboratory equipment control device layer 1152 as part of the laboratory equipment control device 1102, a synthesis specification module layer 1154 associated with the 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 hierarchical layers, namely, a hardware layer, a middleware layer, and an interface layer. The hardware layer relates to hardware resources, such as sensors and actuators, particularly for controlling the synthesis of polymers. The middleware relates to any of the known middleware for laboratory or plant synthesis operations. One example is LABS / QM, which provides various abstractions over hardware, networks, and operating systems, such as low-level device control and message passing. The communication layer concerns communication protocols, one of which may be REST, which may be implemented on top of different transmission protocols (i.e. UDP, TCP, Telemetry) that allow the exchange of messages between the laboratory equipment control device and the laboratory equipment devices. Such a software architecture makes it possible to control and monitor the laboratory equipment without the need to interact with the hardware.

[0096] The synthesis 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 the data-driven biodegradation model to provide a recipe for a polymer based on biodegradation, i.e., a synthesis specification, as detailed above. In particular, the functions performed by the device may be provided as program code means stored in the mass storage, as described above. Furthermore, synthesis specifications for 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 processes based on target properties. Such functions may include determining a digital representation of a target polymer based on a target biodegradation and a biodegradation model, generating a synthesis specification from the digital representation of the target polymer, and providing the synthesis specification as control data to a laboratory equipment control device.

[0097] The interface layer can implement web services, network interfaces as UDP or TCP, or web socket interfaces. A REST API is implemented for communication with laboratory equipment control devices.

[0098] The client layer 1156 provides an interface for an end user. For an end user, the client layer 1156 can execute 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 may be provided with a UI for selecting a target biodegradability and a biodegradable habitat for the target biodegradability, which may also include a range of biodegradability values. In another example, the user may be provided with a UI for selecting multiple target biodegradability values ​​and respective values. The application may be configured for the user to remotely monitor and control the laboratory equipment control device and operation. In another example, the client device layer and the composite specification module layer may be incorporated into one device. The alternatives described herein are merely illustrative and should not be construed as limiting.

[0099] 7 shows a block diagram of an exemplary system architecture of a system and device for generating a biodegradation model for determining a biodegradation degree, namely, a network 2150 and a model generation module 2100 / 2110 that may be regarded as 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.

[0100] The model generation module layer 2154 may include a mass storage layer, a computing layer, and an interface layer. The storage layer is configured to provide mass storage for the data-driven biodegradation model described above. Furthermore, the mass storage is configured to store the synthetic specifications of the polymer and the measured biodegradation of 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, or a NoSQL database, such as HBase, MongoDB, etc. The computing layer may include an application layer that allows customizing the functionality provided by standard cloud services to execute a computing process for generating a biodegradation model for determining the biodegradation of the polymer. Such functions may include: receiving, for at least two previously measured polymers, 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 biodegradation in at least one habitat; receiving, in the model generation module, a digital representation of at least one unmeasured polymer; training a model according to the above training principle based on the digital representations of the at least two previously measured polymers, the measurement data of biodegradation 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 digital representations associated with the synthetic specification of each of the at least two previously measured polymers and the digital representations associated with the synthetic specification of the at least one unmeasured polymer; and providing a biodegradation model of biodegradation via an output interface. The model generation module layer may be configured to deploy the generated model and the synthetic specification database to the synthetic specification module layer. This may include storing the generated model and the synthetic specification database in a mass storage device associated with the synthetic specification module.

[0101] The model generation module layer may further be configured to determine, from the synthesis specification, a digital representation of the polymer associated with 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 any other principle already mentioned. 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 device associated with the model generation module. In such a case, deploying the model includes providing the relationship.

[0102] The interface layer may implement a web service, a network interface as UDP or TCP, or a web socket interface. In this example, a REST API is implemented for communication with the client device. The client layer 2156 provides access to a mass storage device containing the polymer synthesis specifications and at least one biodegradability for at least two polymers. The client layer further provides an interface for an end user. For the end user, the client layer 2156 may execute a client-side web application that provides an interface to a mass storage device associated with the model generation module layer 2154 or the client layer. The user may be provided with a UI for selecting a 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 to upload the selected data to the model generation module layer and, optionally, to start the model generation.

[0103] FIG. 8 shows an exemplary system 700 for producing a chemical product based on a synthesis specification generated according to the invention. In this example, 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 principles described above, and 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 to receive control data, for example, generated according to the invention described above, and in particular, generated based on a synthesis specification of a polymer comprising a target biodegradability. In this example, the control data is provided from a database 730, but in other examples, the control data may also be provided from a server or any other computing unit for distributing data. The containers 750, 752 each contain a component of a chemical product, for example a prepolymer, a catalyst, etc. Typically, there are three or more containers, but in this example, only two are shown for illustration purposes. Valves 760, 762 are associated with the containers 750, 752. Valves 750 and 752 may be controlled to dose the appropriate amount of each component to reactor 770 according to the synthesis specification. Motor 800 of mixer 780 may also be controlled by the control unit according to the synthesis specification. Optional heater 790 may also be controlled according to the synthesis specification. Finally, outlet valve 810 in fluid communication with the reactor may be controlled by the control unit to deliver chemical products to a vessel or test system 820.

[0104] FIG. 9 exemplarily and diagrammatically shows a possible user interface for interfacing with a processor that executes, for example, the above-described method for determining the biodegradability of a polymer. In this example, an input screen is shown on the right. The input screen allows the definition of the polymer for which the biodegradability is to be determined. In this case, polyalkoxylate is defined as the polymer classification, for example by using a drop-down menu. Furthermore, further fields are provided for further optional specifications and inputs. In this example, the polymer is then further defined below by defining the components of the polymer in the form of component type and associated components, amounts and blocks. In FIG. 9, an exemplary further definition of polyalkoxylate is provided. Additionally, in this example, the input screen allows the input of which application properties are to be determined (in this case biodegradability and kinematic viscosity). The additional application properties may be determined, for example, based on respective prediction models for each additional application property or by any other known method. Furthermore, the input screen allows the input of the type of habitat. However, this can also be omitted if the habitat is derived from other information, such as, as in this case, from the measurement method selected for biodegradability, which applies only to wastewater. In general, the input may also refer to further information to define, for example, habitat descriptors, intended application, measurement methods, etc. In this example, it can be seen that a good prediction accuracy can be achieved when using, as polymer descriptor types, a descriptor referring to the molar 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 can then be determined according to the principles described above for a defined polymer. An exemplary output screen is shown on the right side of FIG. 9. In this case, the output screen provides the results of the determination of the degree of biodegradation for a defined polymer and habitat, using the polymer descriptors defined above on the input screen. In this example, the results show that for the defined polyalkoxylate, 75±8% is biodegraded after 28 days in wastewater, and the respective determined kinematic viscosity is also provided.

[0105] In general, the present invention refers to a method for determining the biodegradability of, for example, a new polymer. For example, in a first step, a digital representation of the polymer may be provided. The digital representation may be a recipe, a structural formula, a brand name, a CAS number, etc. In an optional step, a target application of the new polymer may be provided. In a second step, a habitat may be selected. In this context, the term "habitat" is the biological environment in which the biodegradability is to be evaluated. Depending on the habitat, other parameters may also be relevantly provided. In one embodiment, the habitat may be selected based on the target characteristic. For example, personal care products such as shampoos are generally desired to be degraded in wastewater. As a result, in this example, the automatic selection selects wastewater as the habitat.

[0106] In general, the biodegradation model may be based on habitat descriptors that are important for the habitat. As a result, different biodegradation models may be selected based on the input habitat. Thus, in a preferred workflow, the biodegradation model is selected based on the habitat. Upon selection of the model, the inputs of the model, the model may indicate that further inputs are required, such as further polymer physicochemical parameters, and further inputs may be requested. This may be done by providing a list of the required parameters to be provided. Values ​​are derived from the digital representation of the polymer physicochemical parameters, which form the inputs to the model. Based on the habitat descriptors and the polymer physicochemical parameters, a measure of biodegradation is determined and provided. In an optional step, a representation of biodegradation may be selected. In this case, the output is based on the selection. Possible representations of biodegradation may be one or more of: mineralization, which refers to information on whether the polymer is completely mineralized or not, or the time until mineralization is achieved; biodegradation, which refers to a change in the chemical structure that results in the loss of a particular property of the polymer (e.g., toxicity), 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 soils. In the case of oceans, parameters that may affect biodegradation are salinity, sediments, water temperature, bacterial cultures, etc. In some examples, the marine habitat descriptors may be stored in a database together with a geolocation. A geolocation may then be entered and the value of the parameter associated with this geolocation may be retrieved from the database. In the case of wastewater, parameters that may affect biodegradation are temperature, bacterial count, type of bacteria, enzyme concentration, enzymes. In the case of soil, parameters that may affect biodegradation are temperature, bacterial count, type of bacteria, enzyme concentration, enzymes. EXAMPLES

[0107] Below are provided some examples of the results of biodegradation determination using the biodegradation models trained and provided as above. For each example, the biodegradation model was trained for a particular class of polymers using a training data set containing 50-100 different polymers in the polymer class and their respective biodegradability in the respective habitats. Furthermore, each model was trained to determine the biodegradation 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 that all carbon is converted. Furthermore, the model applied for polyalkoxylates in wastewater was trained according to the standard OECD301-Test, and for better comparison, the model applied for polyesters in soil was trained according to ISO17556.

[0108] In a first example, a biodegradation model was trained for polyalkoxylates in wastewater and was based on a two-step approach. In the first step, a random forest model was trained and used to select polymer descriptors, which were then used in a trained linear regression model to determine the biodegradation degree in the second step. The trained biodegradation model, in this case a linear regression model, was then applied to Plurafac LF 221 using its respective physicochemical properties. The output of the biodegradation model resulted in a biodegradation of 91%, meaning that 91% of the polymeric material was converted to CO2 based on the calculated theoretical oxygen demand of the polymeric material assuming all carbon was converted, and the measured biodegradation of this polymer was reported to be more than 60%.

[0109] 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 in both examples. The biodegradation model was applied to Pluriol E 200 using its respective physicochemical properties. The output of the biodegradation model resulted in 96% biodegradation, meaning that 96% of the polymeric material was converted to CO2 based on the calculated theoretical oxygen demand of the polymeric material assuming all carbon was converted, and the measured biodegradation of this polymer was reported to be greater than 70%.

[0110] 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 in both examples. The biodegradation model was 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 calculated theoretical oxygen demand of the polymeric material assuming all carbon was converted, and the measured biodegradation of this polymer was reported to be 7.2%.

[0111] In the fourth example, a biodegradation model was trained for polyester in soil. In this example, the trained biodegradation model was based on a trained partial least squares model. The biodegradation model was applied to Lupraphen 1619 / 1 using its respective physicochemical properties. The output of the biodegradation model resulted in 49% biodegradation, meaning that 49% of the polymeric material was converted to CO2 based on the calculated theoretical oxygen demand of the polymeric material assuming all carbon was converted, and the measured biodegradation of this polymer was reported to be more than 60%.

[0112] The expected measurement error of the measurements is ±10% for wastewater and ±10% for soil, and the results of the trained biodegradation model are within the range of appropriate accuracy for the intended application. Thus, a training dataset with only 50 polymer data points already provides adequate accuracy. Higher accuracy may be obtained by utilizing a dataset containing more polymers. Thus, the biodegradation model can be trained accordingly according to the accuracy appropriate for the respective application.

[0113] 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.

[0114] For the processes 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.

[0115] 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.

[0116] A single unit or device may fulfill the functionality 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.

[0117] The procedures performed by one or more units or devices, such as providing the physicochemical parameters and biodegradation model of the polymer, determining the biodegradation degree, providing the biodegradation degree, etc., 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.

[0118] 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.

[0119] 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, or may include 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 a structure that may be software, hardware, or a combination thereof. For example, if implemented in software, one skilled in the art should understand that the structure of the executable components 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 stage or multiple stages. 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 processing units of a computing system. When such operations are implemented in software, one or more processors direct the operation of the computing system in response to executing the computer-executable instructions that make up the executable component. A computing system may also include communication channels that enable the computing system to communicate with other computing systems, for example, over a network. A "network" is defined as one or more data links that enable the transmission of electronic data between computing systems and / or modules and / or other electronic devices. When information is transferred or provided to a computing system over a network or another communications connection, for example, either wired, wireless, or a combination of wired and wireless, the computing system properly considers the connection to be a transmission medium. A transmission medium may include a network and / or data link that can be used to carry desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose 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.

[0120] 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 electronic devices, 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, through a network, either by wired data links, wireless data links, or a combination of wired and wireless data links, perform tasks together. In a distributed system environment, program modules may be located in both local and remote memory storage units.

[0121] Those skilled in the art will also appreciate that at least a portion of the present invention may also be implemented in a cloud computing environment. A cloud computing environment may be distributed, but this is not required. If distributed, a cloud computing environment may be distributed internationally within an organization and / or may have components held across multiple organizations. In this specification and in 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 on a distributed computing system that includes elements that reside in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems depicted in the figures may include more or fewer components than those depicted in the figures, and some of the components may be combined where circumstances warrant.

[0122] Any reference signs in the claims shall not be construed as limiting the scope.

[0123] The present invention relates to a method for determining the degree of biodegradation for a polymer. A digital representation of a polymer is provided, which indicates physicochemical properties of the polymer. Further, habitats are provided, which indicate habitat descriptor values ​​that influence the biodegradability of the polymer. The habitat descriptors indicate environmental properties of the habitats. A biodegradation model is provided based on the habitats, the biodegradation model is adapted to determine the degree of biodegradation of the polymer in each habitat, the biodegradation model being a data-driven model parameterized with respect to the habitats such that the degree of biodegradation of the polymer can be determined based on the physicochemical properties. The degree of biodegradation of the polymer is then determined based on the provided biodegradation model and the digital representation of the polymer.

Claims

1. 1. A computer-implemented method for determining a biodegradability that can be used to verify the biodegradability of a polymer, said method (200) comprising: providing a digital representation of the polymer indicative of or associated with physicochemical properties of the polymer (210); Providing (220) biodegradable habitats, each of which exhibits habitat descriptor values ​​for habitat descriptors that affect the biodegradability of a polymer in the habitat, the habitat descriptors indicating environmental characteristics of the habitat; providing a biodegradation model (230) based on the provided biodegradation habitats, the biodegradation model being adapted to determine the degree of biodegradation of a polymer in each biodegradation habitat, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats such that the biodegradation model can determine the degree of biodegradation of a polymer based on the physicochemical properties; determining (240) the degree of biodegradation of the polymer based on the provided biodegradation model and the digital representation of the polymer; A method (200) comprising:

2. 10. The method of claim 1, further comprising providing a biodegradation test method, wherein the provided biodegradation test method represents a standardized biodegradation test method for experimentally determining biodegradability of chemical substances, and wherein the biodegradation model is further provided based on the provided biodegradation test method.

3. 2. The method of claim 1, wherein the biodegradation habitat refers to any one of a marine habitat, a wastewater habitat, a freshwater lake habitat, a compost habitat, an anaerobic habitat, or a soil habitat.

4. 4. The method of claim 3, wherein the biodegradation habitat refers to a marine habitat and the habitat descriptors refer to at least one of salinity, sedimentation type, oxygen level, location, sample depth, water temperature, nutrient concentration, pH value, environment type, and microbial community.

5. 4. The method of claim 3, wherein the biodegradation habitat refers to wastewater and the habitat descriptors refer to at least one of water temperature, microbial community, sludge concentration, nutrient concentration, pH value, test time, and enzyme environment.

6. 4. The method of claim 3, wherein the biodegradation habitat refers to soil and the habitat descriptors refer to at least one of temperature, sand content, pH value, moisture content, nutrient concentration, microbial community, and enzyme environment.

7. 4. The method of claim 3, wherein the biodegradation habitat refers to compost and the habitat descriptors refer to at least one of temperature, compost activity, pH value, moisture content, humidity, compost maturity, compost composition, compost origin, nutrient concentration, microbial community, and enzyme environment.

8. 2. The method of claim 1, wherein habitat descriptor values ​​of the habitat descriptors are stored in association with respective geolocations, and wherein providing the biodegradable habitat refers to providing a geolocation of the habitat and retrieving the habitat descriptor value for the geolocation from storage.

9. 1. An interface method for providing an interface, the interface method comprising: receiving as input a digital representation and a habitat via a user interface and providing said received digital representation and said habitat to a processor executing the method of any one of claims 1 to 8; providing a result of the determined biodegradability of the polymer to a user via a user interface, the result being received from the processor executing the method of any one of claims 1 to 8; A method comprising:

10. 1. A computer-implemented training method for training a data-driven based biodegradation model for parameterization of said biodegradation model, said training method (300) comprising: Providing training data associated with predetermined biodegradation habitats (310), the training data including: a) digital representations of a plurality of training polymers indicating physicochemical properties for each of the training polymers; and b) a biodegradation rate for each biodegradation habitat associated with each training polymer; Providing a data-driven based trainable biodegradation model (320); training (330) the provided data-driven based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine biodegradability of the polymer based on physicochemical properties of the polymer; providing the trained biodegradation model (340); A training method (300) comprising:

11. An apparatus for determining the degree of biodegradation that can be used to verify the biodegradability of a given polymer, said apparatus (110) comprising: a digital representation providing unit (111) for providing a digital representation of said polymer indicative of or associated with physicochemical properties of said polymer; a habitat providing unit (112) for providing biodegradable habitats, the biodegradable habitats exhibiting habitat descriptor values ​​of habitat descriptors that affect the biodegradability of polymers in the respective habitats, the habitat descriptors indicating environmental characteristics of the habitats; a model providing unit (113) for providing a biodegradation model based on the provided biodegradation habitats, the biodegradation model being adapted to determine the biodegradation degree of a polymer in each biodegradation habitat, the biodegradation model being a data-driven model parameterized with respect to the biodegradation habitats to determine the biodegradation degree of a polymer based on the physicochemical properties; a determination unit (114) for determining the degree of biodegradation of the polymer based on the selected biodegradation model and the digital representation of the polymer; An apparatus (110) comprising:

12. 1. An interface device for providing an interface, said interface device comprising: an input interface unit for receiving as input a digital representation and a habitat via a user interface and for providing said received digital representation and said habitat to the apparatus of claim 11; a result interface for providing the determined biodegradability of the polymer as a result to a user via a user interface, the result being received from the device of claim 11; An interface device comprising:

13. A training device for training a data-driven based biodegradation model for parameterization of said biodegradation model, said training device (120) comprising: a training data providing unit (121) for providing training data associated with a predetermined biodegradation habitat, the training data including: a) digital representations of a plurality of training polymers, each of the training polymers representing physicochemical properties; and b) a biodegradation rate of each of the biodegradation habitats 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 based biodegradation model based on the provided training data, such that the trained biodegradation model is adapted to determine the biodegradability of the polymer based on the physicochemical properties of the polymer; a trained model providing unit (124) for providing said trained biodegradation model; A training device (120) comprising:

14. 12. A computer program for determining the degree of biodegradation of a given polymer, said computer program comprising program code means for causing an apparatus according to claim 11 to carry out the method according to any one of claims 1 to 8.

15. A computer program for training a biodegradation model, said computer program comprising program code means for causing an apparatus according to claim 13 to carry out the method according to claim 10.

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