Method for planning an experiment series

Abstract

The invention refers to a method for providing test synthesis specifications for performing an experiment series. Synthesis parameters defining ingredient and / or process parameters are received, wherein different ingredient and / or process parameters are associated with different potential test polymers. Variation parameters defining possible synthesis parameter variation ranges are received. Limits for the synthesis parameters are derived based on the synthesis parameters and the variation parameters. From an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-dataset is selected based on the derived limits. Test synthesis specifications associated with respective test polymers are determined based on the derived limits and the explored sub-dataset. The test synthesis specifications comprising instructions for controlling and / or monitoring the synthesis of the respective one or more test polymers are provided.

Description

[0001] Method for planning an experiment series

[0002] FIELD OF THE INVENTION

[0003] The invention relates to a method, an apparatus and a computer program product for providing one or more test synthesis specifications each associated with a test polymer for performing an experiment series. Further, the invention refers to a method, apparatus and computer program product for generating a machine learning based determination model. Moreover, the invention refers to a method, apparatus and computer program product for automated performing of an experiment series for finding a target polymer comprising a predetermined target technical application property. Further, the invention refers to interface methods, interface apparatuses, and interface computer program products providing an interface for the above methods, apparatuses and computer program products.

[0004] BACKGROUND OF THE INVENTION

[0005] Generally, polymers are widely used in industrial and / or daily use products due to their broad range of application properties. For developing new polymers or for utilizing known polymers in new application contexts, often polymers have to be found that comprise a specific technical application property, for instance, a specific heat insulating factor, a specific hardness, or a specific reflectivity. Today, the search for such new polymers is often performed by defining a search range for the polymers to be searched and then designing a plurality of experiments based on expert knowledge or a statistical design of experiment (DoE) in order to successively try to find within the plurality of experiments a polymer that fulfils the respectively searched property. However, the number of experiments, necessary to find a respective target polymer, is very large, for example, it can be necessary to synthesize a few hundred polymers during the search. Particularly, if many different chemical substances and process conditions can be varied, statistical design of experiment results in a vast number of necessary experiments. Further the process is mainly based on the experience, instincts and knowledge of the experts designing the experiment series. Thus, the success of such an experiment series is often difficult to predict and a lot of resources can be wasted during the experimental search process. Thus, it would be advantageous if a method could be found that allows for a more objective and efficient selection of the respective experiments of the experiment series, in particular, that allows to reduce the number of experiments that have to be performed.

[0006] SUMMARY OF THE INVENTION

[0007] It is an object of the present invention to provide methods, apparatuses and computer program products that allow to perform an experiment series more objectively and more efficiently, i.e. with less performed experiments and thus with less resources, preferably, for applications referring to a determining of a training dataset for training a polymer property determination model or a finding of a target polymer comprising a target value for a target technical application property.

[0008] In a first aspect of the present invention, a computer implemented method is presented for providing one or more test synthesis specifications each associated with a test polymer, for performing an experiment series, wherein a test synthesis specification comprises instructions for synthesizing the respective test polymer for measuring one or more technical application properties of the test polymer in the experiment series, wherein the method comprises a) receiving synthesis parameters defining ingredient and / or process parameters for which one or more test polymers for the experiment series are to be determined, wherein different ingredient and / or process parameters are associated with different potential test polymers, b) receiving variation parameters defining possible synthesis parameter variation ranges, c) deriving one or more limits for the synthesis parameters based on the synthesis parameters and the variation parameters, wherein the limits of the test synthesis specifications define which synthesis specifications are utilizable as test synthesis specifications, d) selecting from an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-dataset based on the derived limits, wherein the explored polymers and the explored sub-dataset comprise polymers for which one or more predetermined technical application properties are known, e) determine one or more test synthesis specifications associated with respective test polymers based on the derived limits and the explored sub-dataset, and f) providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring the synthesis of the respective one or more test polymers. Since limits are derived for the one or more test synthesis specifications based on the synthesis parameters and the variation parameters defining which synthesis specifications are utilizable as test synthesis specifications, it can be clearly defined which test synthesis specifications are expected to be suitable for the experiment series and which ones cannot be utilized. For example, it can be ensured that the generated test synthesis specification can be synthesized with the available means, for instance, synthesis or chemical processing equipment, and also that the ingredients are available. Further, since the explored dataset is received based on the derived limits, polymers for which a technical application property is already known and that are as close as possible, in particular, with the derived limits, are identified and the pre-knowledge provided by these explored polymers can be taken into account for determining the one or more test synthesis specifications. In particular, since the one or more test synthesis specifications are determined based on the derived limits and the explored sub-dataset, it can firstly be ensured that polymers for which a respective technical application property is already known are not determined as test synthesis specifications and tested again and further the functional relations represented by the explored polymers between the synthesis specification and the respective technical application property can be taken into account when determining the test synthesis specifications. Moreover, based on the explored sub-dataset and the limits, it can be determined which test synthesis specifications would provide the most benefit for completing the objective of the respective experimental series. Thus, not only the amount of the performed experiments, i.e. the amount of test polymers, can be reduced, but also the respective test polymers can be selected according to an objective criterion. Thus, the method allows for a more objective and efficient performing of the experiment series in particular for applications like determining a training dataset for a property determination model or finding a target polymer with the respective target technical application property.

[0009] The method refers to a computer implemented method and thus can be performed by a general or dedicated computer, or network of computers, adapted to perform the method, for instance, by executing a respective computer program. The method is configured for providing one or more test synthesis specifications each associated with a test polymer for performing of an experiment series. Generally, a synthesis specification is defined as an instruction on how a polymer can be synthesized. In particular, the synthesis specification indicates the starting ingredients and the respective process parameters for polymerization, like an amount of ingredients and feed profiles for the ingredients, and thus comprises the synthesis parameters defining the process and / or ingredient parameters for a specific polymer. The process parameters can also cover all aspects regarding the apparatus used for polymerization, like a temperature profile, a reactor type and size, or stirring power. The performing of the experimental series can comprise the planning, actual synthesising and experimenting, and the processing of the measurement results. The experiment series can referto any experiment series comprising one or more technical objectives with respect to a polymer or a material comprising a polymer. Preferably, it is an object of the experiment series to determine a training dataset that allows to efficiently train a property determination model that can determine a technical application property of a polymer based on one or more computed characteristics of the polymer after the training. A further preferred object of the experiment series refers to finding a target polymer that comprises the predetermined target value for a target technical application property, for instance, in the context of developing a new polymer or finding existing polymers for specific applications. However, the experiment series can also refer to any other objective associated with measuring one or more technical application properties of a polymer. Preferably, the experiment series refers to a design of experiment (DoE) process and the test polymers provided by the method refer to the experiments in the DoE process.

[0010] A technical application property can generally refer to any property of a polymer and / or a substance consisting at least partly of the polymer, for example, a formulation or mixture comprising the polymer, that allows to assess a technical applicability of the respective polymer as provided after its synthesis. The technical application property is a property of the polymer that defines the technical applicability of the polymer for a predetermined technical application. Preferably, the technical application property comprises at least one of mechanical properties, optical properties, physicochemical properties, chemical properties and biological properties. Generally, mechanical properties can refer to any of adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, split tear, tear-strength, rebound, compressibility, abrasion, spillage, morphology, haptic properties, stress at break, elongation at break, granulometry and a degree of filling. An optical property can generally comprise any of coloration, turbidity, opaqueness, lucidity, reflection, appearance, absorption, scattering, color strength, colour hue, colour saturation, colour intensity, cloud point, matting degree, optical density, spectra, refractive index. Moreover, a physicochemical property can referto any of density, viscosity, K-value, molar weight, dispersity, molar mass distribution, particle size distribution, solubility, partition coefficients, interfacial properties, surface tension, dispersibility, storage stability, odor, segregation, coagulation, electric conductivity, electric capacity, surface area, flow time, vapor pressure, VOC, solid content, hygroscopicity, magnetism, miscibility, thixotropy, phase transition properties, glass transition temperature, corrosion inhibition, solvent separation, aggregation, self-heating ability, impact sensitivity, loss on drying, angle of response, electrostatic charge, minimum filmforming temperature, and charge density. The chemical property can comprise any of chemical resistance, reaction timing, demolding time, growing, hard / soft segment content, crystallinity, reaction temperature, reaction pressure, decomposition, thermal decomposition, photodegradation, acidity, pKa, pH, moisture / water content, flammability, burning rate, self ignition, flash point, formation of flammable gases, reaction to fire, deflagration rate, residual monomer count, side product formation, degree of polymerization, salt content, temperature tolerance, oxidizing properties, reduction properties, reactivity, ash content, non-volatile matter content, stability, chelating ability, calorific value, saponification value. Further, the biological property can comprise any of biodegradability, biological resistance, in particular, resistance against a pathogenic virus, bacterium, fungus, plant or animal or developmental stage of said pathogen, tolerance against environmental parameters, e.g. drought tolerance, resistance against enzymatic degradation, e.g. protease resistance, lipase resistance, amylase resistance, hydrolase resistance, pesticide resistance, toxicity, biotransformation, ecotoxicology, sensitization, in particular, allergenicity, bacterial count, enzyme activity, substrate specificity, cofactor dependence, product specificity, substrate and / or product inhibition, dissociation constant, Michaelis-Menten-kinetics values, activ- ity / stability at or in different: pH, temperature, pressure, organic solvent concentration, carrier formulations, encapsulation formulations; distribution in environment, compartmentalization, bioaccumulation, biological exposure LD50, mutagenicity.

[0011] In a first step, the method comprises receiving synthesis parameters for which one or more test polymers for the experiment series are to be determined. In particular, synthesis parameters can refer to any parameter that allows to define the limits of a search space in which the respective experiment series should be performed, for example, can comprise ingredient and / or process parameters at the end of the respective range. The synthesis parameters can be regarded as defining ingredient and / or process parameters which indicate the core of the search polymer space in a polymer space defined by the process parameters and ingredient parameters as coordinates, in which respective test synthesis specifications of test polymers are to be searched. The ingredient parameters generally quantify the production of the polymer itself, in particular, the substances utilized for producing the polymer. Such substances can refer to starting substances like initiators, monomers, or prepolymers from which the polymer is produced. However, the substances can also refer to auxiliary substances, like catalysts or surfactants. The substances can refer to mixtures of different ingredients as well, like catalysts dissolved in a solvent. In this context, the ingredient parameters quantify the influence of these substances on the produced polymer and thus also characterize the polymer itself. For example, the ingredient parameters can refer to any of an amount and / or feet profile of specific monomers, an amount and / or feed profile of specific additives, a mixing ration between different substances, or a used solvent. The process parameters can, in this context, refer to variables that can be set during the synthesis of a test polymer. Thus, process parameters quantify the production process of the respective polymer and can be part of a synthesis specification. For example, the process parameters can refer to any of a temperature profile, a pressure, a stirring power, a reactor type, etc.

[0012] The ingredient parameters may comprise the amount of a specific ingredient substance. Further, the ingredient parameters might refer to a type of ingredient instead of specifying the ingredient itself. For example, the ingredient parameters can be indicative of a specific type of monomers instead of specifying the monomers in detail. For indicating the ingredient substances, the ingredient parameters can comprise digital identifications of the respective ingredient substances. Moreover, the ingredient parameters can provide a categorization of the ingredient substances into predetermined categories. For example, substances that are catalysts can be provided in a catalyst category, initiator substances can be provided in an initiator category, surfactants can be provided in a surfactant category, etc. These categories are preferably functional categories that indicate the function of the respective substances in this category. This allows to directly provide additional information not only about the substances themselves but also about their function during the reaction process. However, the categories can also be other categories that refer to other additional information of the respective ingredient parameters.

[0013] The process parameters can comprise at least one of a reaction temperature, a reaction temperature profile, a dosage profile of one or more ingredients, a reaction pressure, a reaction pressure profile, a reaction vessel characteristic, a total amount of a substance and a total amount of functional groups. Preferably, the process parameters comprise at least the temperature or temperature profile of the reaction. Also the process parameters can be categorized to provide additional categorical information. For example, process parameters that refer to total amounts of parameters or process parameters that refer to profiles of a parameter can be provided in a respective category. Generally, the categorization allows to provide additional information with respect to the parameters and at the same time the processing of the categorized parameters, for instance, for determining the limits in the next step, is simplified.

[0014] In a further step, variation parameters defining possible ingredient and / or process parameter variation ranges are received. Parameter variation ranges refer to any kind of variations that can occur in the synthesis parameters, in particular, in the ingredient and / or process parameters in the experiment series. The parameter variation ranges may result from statistical variations in the formation of polymers. However, the parameter variations can also be variations based on the hardware utilized for performing the experiment series. For example, a heater might only provide a predetermined temperature range. The variation parameters can be provided as average range of variations and / or as maximum range of variations. An average range of variations determines variations that can be found on average but does not exclude that higher or lower variations are generally possible. A maximal range of variations in contrast thereto indicates the maximal and minimal values of a parameter. The variation parameters can comprise, provided as average range of variations and / or as maximum range of variations, an amount of ingredients, an amount of ingredients from a certain type, a temperature, a temperature profile, a dosage profile, a pressure, a pressure profile, a total and / or relative amount of substances, a total and / or relative amount of functional groups and combinations hereof. Preferably, the variation parameters comprise an average range of variations or an maximum range of variations at least an amount of ingredients.

[0015] In a further step, one or more limits for the one or more test synthesis specifications are derived based on the synthesis parameters and the variation parameters. The limits of the test synthesis specifications define which synthesis specifications are utilizable as test synthesis specifications. Moreover, the one or more limits define which region of a test polymer space is covered by the experimental series and thus by the test polymers. A polymer space can be defined as a space spanned by synthesis parameters, wherein each polymer associated with specific synthesis parameters can be represented by a point in the polymer space. Thus the limits derived from the synthesis parameters and the variations represent the boundaries on the polymer space for which a test polymer can be determined in the respective experiment series. The limits can define parameter ranges for the synthesis parameters that are to be covered by the experimental series, for instance, a temperature range for a reaction temperature can be defined, a range for the amount of a specific ingredient can be defined etc. The limits can be derived as limits that allow for a continuous variation of the respective synthesis parameter within these limits, limits that define a discontinuous variation of the synthesis parameter within these limits and / or limits that define a categorical variation within the limits. For example, a continuous variation within these limits can refer to a reaction temperature range, wherein a test synthesis specification can comprise each reaction temperature within the reaction temperature range defined by the limits. An example for a discontinuous variation within the limits can be an amount of an ingredient that can only deviate in natural numbers or in specific ratios with respect to another ingredient or to a case in which a parameter range is cut out between the general limits. A categorical variation can refer, for example, to which types of catalysts can be utilized, wherein the catalysts can refer to any catalyst within the respective catalyst type. Thus, the respective determined limits can be provided with a respective information on whether or not the limits define a continuous variation, a discontinuous variation or a categorical variation and further comprise a definition of the respective variation, for instance, of the discontinuities in a discontinuous variation or of the respective categories in a categorical variation. Moreover, a limit can be derived as soft limit or as hard limit. A hard limit can be defined as a limit that for the test synthesis specifications cannot be crossed. For example, a hard limit can define the maximal amount of a certain ingredient. A soft limit can refer to a limit that can, under predetermined criteria, be crossed. For example, a temperature range might be considered optimal, but if the test series does not fulfil the respective objective within this temperature range, also test synthesis specifications using a temperature outside the optimal range can be considered. In an example, variation parameters provided as average can be utilized to derive soft limits, and variation parameters provided as maximal range can be utilized to derive hard limits.

[0016] The limits can be derived based on the synthesis parameters and the variation parameters, for example, by setting the limits to the respective variation boundaries of a synthesis parameters based in the information provided by the synthesis parameters and the variation parameters. However, also more complex statistical methods can also be used, for instance, by utilizing the process parameters and the variation parameters to define a normal distribution and to define the limits based on the normal distribution.

[0017] In a next step, the method comprises selecting from an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-da- taset based on the derived limits, wherein the explored polymers and the explored subdataset comprise polymers for which one or more predetermined technical application properties are known. The explored dataset and the explored sub-dataset that is selected for the explored dataset can comprise a) a technical application property for a plurality of explored polymers and b) a synthesis specification for the plurality of explored polymers, wherein the synthesis specification is indicative of the ingredient and process parameters for synthesizing the respective polymer. Thus, an explored dataset and sub-dataset refers to a dataset comprising only polymers for which at least once at least one technical application property has been determined. Generally, an explored polymer refers to a polymer for which at least one technical application property has been determined. The technical application property provided with the explored dataset or sub-dataset is associated with a technical application property that is associated with the objective of the experiment series. For example, if the experiment series has the objective to find a target polymer comprising a target technical application property then the explored dataset and sub-dataset comprises explored polymers for which this technical application property is known, for instance, measured. In an example, in which the experiment series has the objective of generating training data fortraining a machine learning based model to determine one or more technical application properties, the explored dataset and sub-dataset comprises explored polymers for which these one or more technical application properties that the model should predict are known. The technical application property of an explored polymer can be determined in any known manner, for example, based on experimental data and measurements, based on simulations or theoretical considerations. Preferably, the technical application property refers to a measured technical application property. For example, for measuring the technical application property a respective polymer can be synthesized and the respective technical application property measured in a suitable measurement process. However, the technical application property can also be determined based on respective physical or chemical calculations or based on respective simulations of the polymer. The selected explored sub-dataset comprises all or less explored polymers than the explored dataset and thus defines a subset of the explored polymers provided by the explored dataset.

[0018] In an embodiment, the explored dataset and the sub-dataset can further comprise produc- ibility information for one or more of the plurality of polymers of the explored dataset, wherein the producibility information is indicative of which technical application property of the polymer can be measured. For example, during a synthesis of a polymer it can be determined that the polymer is generally not producible with a known synthesis processes, orthat the synthesized polymer is not suitable for certain measurements processes leading to respective technical application properties. Such information can be provided as part of the producibility information. Generally, the producibility information can refer to categorical information that indicates whether the polymer refers to one or more categories. For example, a category can refer to whether the polymer is producible, or whether or not a certain application property can be measured. Providing at least some explored polymers with such information in addition to or instead of technical application properties as part of the explored dataset and sub-dataset, allows to further take this information into account when determining the test synthesis specifications. Moreover, if the objective refers to training a model for determining a technical application property the training can be further based on these explored polymers to at least note when a polymer might not be suitable for an application, for instance, due to synthesis problems. Thus, in this embodiment, the explored dataset and sub-dataset can comprise a) a technical application property and / or a producibility information for a plurality of explored polymers and b) a synthesis specification for the plurality of explored polymers, wherein the synthesis specification is indicative of the ingredient and process parameters for synthesizing the respective polymer. For this embodiment it is preferred that more explored polymers are provided with technical application properties than explored molecules provided with producibility information instead of technical application properties. The explored dataset can be provided on a respective storage or a library and can comprise a plurality of explored polymers, for instance, a few thousand or more explored polymers. The explored sub-dataset is the selected from the explored dataset based on the derived limits. For example, the selection can be based on predetermined criteria associated with the derive limits. The explored sub-dataset can be selected by selecting explored polymers from the explored dataset that fall within the derived limits. Additionally or alternatively, the explored sub-dataset can also be selected by selecting explored polymers that fall within at least some of the derived limits. For example, if for a preferred ingredient also alternative ingredients are known, it can be determined that the selected explored polymers do not have to comprise the respective ingredient but can also comprise the alternative ingredients. In particular, if the experimental series explores a completely new ingredient, the explored dataset might not even comprise an explored polymer with the respective new ingredient, wherein in this case the explored sub-dataset can be selected without taking the limitation to the ingredient into account. Additionally or alternatively, the explored sub-da- taset can also be selected by selecting explored polymers that fulfill predetermined conditions with respect to the limits, for instance, fulfill a predetermined similarity measure. This allows to also utilize explored polymers that do not fall within the limits but are near to the limits, in the sense provided by the respective similarity measure. This can be advantageous in cases in which no or only a predetermined lower number, for instance only 10, explored polymers of the explored dataset fall within the limits. The similarity measure can be defined based on the synthesis parameters. The similarity measure can be a measure quantifying a comparison between the synthesis parameters of an explored polymer and the respective limits of the synthesis parameters. For example, a difference between one or more synthesis parameters of an explored polymer and the respective limits can be determined and the similarity measure can be determined based on the one or more differences. However, explored sub-dataset can be selected by selecting only explored polymers from the explored dataset that fall within the derived limits. This is in particular useful if enough explored polymers are present within the derive limits.

[0019] In a next step, one or more test synthesis specifications associated with respective test polymers are determined based on the derived limits for the test polymers and the explored sub-dataset. For example, based on the explored sub-dataset, functional relations between explored polymers and corresponding technical application properties can be determined and taking the respective derived limits into account one or more test synthesis specifications that allow to further specify the respective functional relations can be determined. In another example, the limits can be utilized to define the region in the polymer space that should be explored by the experiment series referred to as test polymer space. The explored sub-dataset can be utilized to determine regions in the test polymer space for which respective synthesis specifications and associated technical application properties are missing. The determining of the test synthesis specifications is performed in the polymer space spanned by the synthesis parameters. In particular, the test synthesis specifications are determined based on potential synthesis parameters defined by the derived limits. Thus, the test synthesis specifications are derived in the polymer space spanned by the synthesis parameters. Potential synthesis parameters are synthesis parameters that lie within the test polymer space defined by the limits in the polymer space. Thus potential synthesis parameters defined all potential synthesis specifications that can be determined as test synthesis specifications.

[0020] Predetermined rules and / or statistical methods can be utilized to identify test synthesis specifications based on the derived limits and the explored sub-dataset test synthesis specifications that facilitate the objective of the experimental series. For example, the respective rules and statistical method utilized can depend on the respective objectives of the experiment series. In particular, the objective of the experimental series can define one or more criteria for an optimization of the test synthesis specifications. The statistical analysis for determining the test synthesis specifications can then be performed based on these one or more criteria taking the limits and the explored sub-dataset into account. In particular, space-filling methods and multivariate statistics, for instance, dimension reduction methods, like a PCA analysis, can be utilized to determine based on the limits and the explored sub-dataset one or more test synthesis specifications. For example, the test synthesis specifications can be determined such that a predetermined average distance, like a Euclidian or Manhattan distance, between the respective test synthesis specifications and / or between test synthesis specifications and synthesis specifications of the explored sub-da- taset in the polymer space are optimized. Moreover, for the selection of the test synthesis specifications also general linear models and general linear mixed models in connection with optimality criteria like A-optimality, D-optimality, G-optimality, l-optimality and V-opti- mality can be utilized. Additionally, also a Space-Filling-Design method can be used, in particular, D-optimal designs can be augmented with space filling designs. Further details on the methods that can be utilized are provided by the further embodiments and the detailed description.

[0021] In a last step of the method, the one or more test synthesis specifications comprising instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers are then provided. For example, the one or more test synthesis specifications can be provided to a user, for example, via an output unit like a display. However, the one or more test synthesis specifications can also be provided to a storage for storing the one or more test synthesis specifications. Generally, synthesis specification are instructions for a laboratory operator to control, monitor and perform an experiment and as such go beyond mere presentation of information. Preferably, the providing of the one or more test synthesis specification comprises providing control data based on the one or more test synthesis specifications, wherein the control data is configured for controlling and / or monitoring a synthesis system to perform a synthesis of the one or more test polymers based on the one or more test synthesis specifications. The method can comprise relaying the one or more test synthesis specifications and / or the control data to a synthesis system for controlling and / or monitoring the synthesis system for synthesizing the one or more test synthesis specifications. For example, the control data can be provided as part of the synthesis specification. An example, of such a synthesis specification is a synthesis specification provided as a json file. Generally, the control signals, i.e. control data, can be provided in any format that allows to directly or indirectly control a synthesis system for synthesizing a test polymer in accordance with the test synthesis specification. For example, the control data can be provided in a format that allows a synthesis system management application to interpret the control signals and then to control the synthesis system, for example, a synthesis robot, accordingly to produce the test polymer. However, the control data can also be provided in a format that directly allows a control of the respective synthesis system to produce a test polymer, for instance, that directly allows to control components, for example, to start or stop a heating unit, open or close valves, start or stop a mixer, etc. Generally, the synthesis system can refer to any fully or partly automated synthesis system provided in a laboratory or industrial environment that is generally configured to produce polymers based on synthesis specifications. Further, it is preferred, that the control data is further configured for controlling and / or monitoring an automatic measurement system for performing a test procedure for measuring the one or more technical application properties of the synthesised one or more test polymers. In this case the method can comprise relaying the control data to the automatic measurement system for controlling and / or monitoring the automatic measurement system for performing a test procedure for measuring the one or more technical application properties of the synthesised one or more test polymers. Providing the control data such that it further can control an automated measurement system to perform a test procedure for measuring the one or more technical application properties of the synthesized one or more test polymers allows for a complete automation of the experiment series while minimizing human intervention. Respective controllable and automatable synthesis and measurement systems are generally already available and can be utilized to perform the synthesis and the measurements. However, in some applications the controlling and / or monitoring of the synthesis system and automatic measurement system can also refer to a machine guided human machine interaction process in which, for example, a human verifies synthesis steps or measurement steps performed by the synthesis system and the measurement system, respectively, or in which the control data initiates, for instance, via a notification, the user to perform one or more tasks in the synthesis process or measurement process that cannot be performed by the respective system itself, for example, moving a probe from one position to another, adding one or more substances, etc.

[0022] In an embodiment, the method further comprises a) receiving one or more measure technical application properties for the one or more test polymers, b) adding the measured test polymers to the explored sub-dataset, and c) determining further test synthesis specifications based on the limits and the new explored sub-dataset. Generally, a measured quantity is defined as a quantity that is directly measured or derived from direct measurements utilizing known functional relations or physical laws. For example, in a completely automated process, as described above, the control data can also be configured to initiate that the measurements of the one or more technical application properties of the automatic measurement system are again made available for further processing, for instance, by providing them to a storage, or making them available in a respective network, for instance, in a cloud environment. Generally, the determining of the further test synthesis specifications based on the limits and the new explored sub-dataset can be further performed in accordance with predetermined rules that depend on the respective objective of the experiment series. In particular, if the respective objective of the experiment series refers to providing a training dataset for training a property determination model, the determining can be based on a result of the training of the property determination model based on the training data comprising the previously determined test polymers. For instance, an accuracy of the trained property determination model can be determined and if the accuracy does not meet a predetermined criterion, based on the deviation from the predetermined criterion the new test synthesis specifications can be determined and the above described method can be repeated until the property determination model fulfils the respective predetermined criterion. In another preferred example, if the objective of the experiment series refers to determining a target polymer comprising a target value for a target technical application property, before determining new test synthesis specifications the method can comprise comparing the measured technical application properties, i.e. the measured values of the technical application properties forthe one or more test polymers, with the target value. If one of the test polymers meets the target value, it can be determined to abort the experiment series and to determine the respective test polymer as the target polymer. However, if none of the test polymers meets the target value, the method can comprise determining the test polymers which comprise a measured value for the technical application property coming next to fulfilling the target value based on a distance measure like Euclidean distances in the polymer space. For example, the next ten test polymers can be determined and new test synthesis specifications can then be determined further based on the next ten test polymers. In particular, starting from the next test polymers new test synthesis specifications can be generated by varying the process and ingredient parameters of the next ten test polymers within predetermined limits taking into account the derived limits and the explored sub-dataset. In this way, the search for the test polymer can be successively narrowed down with each experiment series until the test polymer fulfilling the target value is found or an abortion criterion determines that in the respective search area it is unlikely to find a target polymer. In another example, the measured technical application properties can indicate that the starting test synthesis specifications are all too far, i.e. more than a predetermined limit, from the respective target technical application property. In this case the new test synthesis specifications can be determined such that they cover other overlapping or completely different areas of the polymer space of test synthesis specifications than the previously determined test synthesis specifications. For example, parameters of the previously used test synthesis specifications can be amended according to predetermined rules such that the newly generated test synthesis specifications strongly differ in at least one parameter from the previous test synthesis specifications. In this case the search space is not narrowed down but widened or displaced. Generally, during the search for one or more goals for the test synthesis specifications all the above changes in the search space can occur depending on the measurement results, for example, in a case the new test synthesis specifications can be determined such that the search space is displaced and then after a further measurement narrowed down. This optimization procedure allows, in particular, together with providing control data for automatically controlling and / or monitoring the synthesis and measurement of the test polymers, a nearly complete automation of an experiment series with different objectives while minimizing the influence of human experts.

[0023] In an embodiment, the method further comprises receiving constraint information indicative of technical and / or productional constraints in the synthesis of a polymer and deriving the limits further based on the constraint information and / or determining one or more test synthesis specifications further based on the interdependency information. For example, the limits can be determined such that test synthesis specification that do not meet the received constraint information are not determined. In an example, constraint information can be directly used as limit. Moreover a determined test synthesis specification can be checked with respect to the constraint information whether or not the respective test synthesis specification fulfils the respective constraints and can then only be provided as test synthesis specification if it fulfils the respective constraints. However, the respective constraint information can also already be taken into account when generating the potential test synthesis specifications, for example, as limits that lead to directly avoiding process and / or ingredient parameters that do not fulfil the technical and / or production constraints. The constraint information can comprise at least one of a maximal number of test synthesis specifications to be tested in the experiment series, a total or relative amount of crosslinking monomers, a relation between specific monomers and functional groups in the polymer, a total amount of specific monomers, a total amount of salts, a total amount of certain atomic elements, an amount of volatile ingredients, a total amount of different ingredients, a carbon footprint of ingredients, a molar mass of a resulting polymer, an amount of hydro- philic / hydrophobic monomers and a free-rise density of a polymer foam. In an example the constraint information comprises a maximum chloride content. A to high chloride content could lead to a higher erosion of the processing and production hardware. In an example, the constraint information comprises a total or relative amount of cross-linking monomers. Moreover, the provided constraints can also be categorized into predetermined categories that are associated with the technical and / or productional aspects of the synthesis of the polymer. Such categories can be, for instance, environmental impact, safety, production capacity, application-related and processing-related. For example, limiting the amount of certain elements can be categorized into a safety category if the respective atomic elements reduce a burning or explosion risk and lead to a flame retardancy, whereas a constraint in the carbon footprint of ingredients can be categorized into the environmental category. Moreover, the different categories can be provided with respective priorities and can be prioritized accordingly in the determination of the limits. In particular, constraints with a high priority can be utilized to derive hard limits that are not to be exceeded, whereas constraints and categories with a lower prioritization, for instance, a prioritization below a certain threshold, can be utilized to derive soft limits that generally should not be exceeded but can be exceeded in case of predetermined circumstances.

[0024] In an embodiment, the method further comprises receiving interdependency information indicative of interdependency constraints associated with interdependencies of one or more synthesis parameters and deriving the limits further based on the interdependency information and / or determining one or more test synthesis specifications further based on the interdependency information. In many cases, synthesis parameters can be interdependent in the sense that a specific value of one synthesis parameter leads to a specific value or value range of another synthesis parameter in order to achieve a meaningful synthesis specification. For example, the presence of one ingredient might lead to a specific temperature range during the reaction in order to not destroy the respective ingredient. Moreover, in many cases, if a specific ingredient is utilized, it is advantageous to also add another ingredient. Another example is, if in the synthesis specification an acid or base is utilized, the interdependency information can relate to additionally adding a buffer so that the final polymer has a pH value in a certain pH value range. A further example, refer to interactions between catalysts or even the information that some catalysts have to be used without other catalysts. Thus, the interdependency information allows to further fine-tune the limits to take the respective interdependencies into account. Moreover, the interdependency information can also be utilized for validating determined test synthesis specifications with respect to whether or not they fulfil the respective interdependency constraints. The interdependency information can comprise any of interdependency between an acid, and a base to reach a predetermined pH value, interdependency of a reaction temperature and one or more substances, interaction between catalysts, interdependency of substances during the reaction, etc.

[0025] In an embodiment, a plurality of potential test synthesis specifications associated with respective potential test polymers are generated based on the synthesis parameters and the derived limits, wherein the determining of the test synthesis specification refers to selecting the test synthesis specifications from the generated potential test synthesis specifications. In particular, the plurality of potential test synthesis specifications are derived such that they fall within the limits for the synthesis parameters. Generally, the potential test synthesis specifications can be generated and provided, for example, by an expert. However, the plurality of potential test synthesis specifications can also automatically be generated and provided, for example, by selecting from a plurality of potential test synthesis specifications already generated and, for example, stored on a respective storage, the plurality of potential test synthesis specifications that fall within the limits. Moreover, potential test synthesis specifications can also be generated by varying from a starting synthesis specification one or more parameters of the starting synthesis specification, for instance, process parameters and / or ingredient parameters, while taking the limits into account or by selecting after the generations the potential test synthesis specifications that fall within the limits. In particular, one or more synthesis specifications of the explored sub-dataset can be utilized as starting point for varying the synthesis parameters to generate the potential test synthesis specifications. The generating of the potential test synthesis specification can be performed in an arbitrary manner, for instance, by arbitrarily varying one or more parameters of the potential test synthesis specification or can be generated in accordance with predetermined rules, for instance, based on a predetermined scheme for the variation of the parameters.

[0026] It is preferred that the plurality of potential test synthesis specifications is derived such that it covers the polymer space within the limits in accordance with a predetermined criterion. For example, such a criterion can refer to a predetermined distribution of the plurality of potential test synthesis specifications over the polymer space defined by the limits. In particular, statistical criterions, like defined average distances between potential test synthesis specifications, a number of potential test synthesis specifications, etc. can be utilized as respective criterion. However, the plurality of potential test synthesis specifications can also be derived without utilizing such a criterion, wherein in this case, it is preferred that a respective larger amount of potential test synthesis specifications is derived. For example, the number of potential test synthesis specifications for the plurality of potential test synthesis specifications can be predetermined based on the size of an ingredient and / or process parameter range defined by the limits. Since generally, the deriving of a plurality of potential test synthesis specifications is an easy and resource-preserving task, deriving a huge amount of potential test synthesis specifications provides no disadvantages.

[0027] For determining the test synthesis specifications, the test synthesis specifications can then be selected from the generated potential test synthesis specifications based on the explored sub-dataset and the limits and further optionally any other criterion described already above. For example, a similarity measure can be utilized to determine a similarity between the synthesis specifications of the explored sub-dataset with the plurality of potential test synthesis specifications, wherein potential test synthesis specifications that are, according to the similarity criterion, too similar to test synthesis specifications of the explored subdataset, are ruled out as being selected as test synthesis specifications. In this way, it can be easily ensured that only potential test synthesis specifications are selected as test synthesis specifications that provide new knowledge, for instance, explore a new part of the polymer space. Moreover, also statistical methods can be utilized, as described above, without the potential test synthesis specifications, wherein the statistical methods can then be utilized for selecting the respective test synthesis specification from the potential test synthesis specifications. In particular, potential test synthesis specifications can be identified that lie in regions of the polymer space that are not covered by the synthesis specifications of the explored sub-dataset. From the potential test synthesis specifications lying in these regions, the test synthesis specifications can then be selected based on statistical rules but even arbitrarily. Utilizing the generated potential test synthesis specifications makes defining the regions in which the explored sub-dataset does not well cover the polymer space easier and also the selection is an easier process than directly generating a synthesis specification after the region has been identified.

[0028] In a further aspect of the invention, an apparatus is presented for providing one or more test synthesis specifications, each associated with a test polymer, for performing an experiment series, wherein a test synthesis specification comprises instructions for synthesizing the respective test polymer for measuring one or more technical application properties of the test polymer in the experiment series, wherein the apparatus comprises one or more processors configured to a) receiving synthesis parameters defining ingredient and / or process parameters for which one or more test polymers for the experiment series are to be determined, wherein different ingredient and / or process parameters are associated with different potential test polymers, b) receiving variation parameters defining synthesis parameter variation ranges, c) deriving one or more limits for the synthesis parameters based on the synthesis parameters and the variation parameters, wherein the limits of the test synthesis specifications define which synthesis specifications are utilizable as test synthesis specifications, d) selecting from an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-dataset based on the derived limits, wherein the explored polymers and the explored sub-dataset comprise polymers for which one or more predetermined technical application properties are known,, e) determining one or more test synthesis specifications associated with respective test polymers based on the derived limits and the explored sub-dataset, and f) providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring the synthesis of the respective one or more test polymers.

[0029] In a further aspect of the invention, a computer program product is presented for providing one or more test synthesis specifications, wherein the computer program product comprises program code means for causing an apparatus as described above to execute the method as described above.

[0030] In a further aspect of the invention, an interface method for providing one or more test synthesis specifications each associated with a test polymer for performing of an experiment series is presented, wherein the interface method comprises receiving, via an input unit, as input synthesis parameters, variation parameters and an explored dataset b) interfacing, via an interface unit, with a processor performing the method as described above based on the input for providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers, and c) providing, via an output unit, the one or more test synthesis specifications.

[0031] In a further aspect of the invention, an interface apparatus for providing one or more test synthesis specifications each associated with a test polymer for performing of an experiment series is presented, wherein the interface apparatus comprises a) an input unit configured to receive as input synthesis parameters, variation parameters and an explored dataset b) an interface unit configured to interface with a processor performing the method as described above based on the input for providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers, and c) an output unit configured for providing the one or more test synthesis specifications. In a further aspect of the invention, a computer implemented method is presented for generating a machine learning based determination model, wherein the trained determination model is adapted to determine a technical application property of a polymer based on one or more polymer computed characterizing parameters, wherein a polymer computed characterizing parameter is indicative of a characteristic of a polymer and / or is derivable from one or more characteristics of the polymer, wherein the method comprises a) performing the method as described above to determine one or more test synthesis specifications, b) receiving for the determined one or more test synthesis specifications the measured technical application property, c) training the machine learning based determination model by parameterizing the determination model based on computed characterizing parameters and the measured technical application property of the test polymers, and d) providing the trained determination model.

[0032] The determined test synthesis specifications and the associated test polymers are part of the training data utilized fortraining the determination model. Further, the training data can comprise at least parts of the explored sub-dataset. Known methods can be utilized to select from the explored sub-dataset and the test polymers a respective training dataset for training the determination model. Generally, the determination model is a data driven model, wherein the term “data driven” is used to emphasize that the model is mainly based on respective data input and not, for instance, on intuition, personal experience, or knowledge. Preferably, the determination model is based on known machine learning algorithms, like neural networks, regression models, classification algorithms, etc. It has been found that for most applications in this context, in particular, regression models based on Linear Regression, Random Forests, Lasso, Boosted Trees, Ridge Regression and MARS algorithms are suitable, whereas for classification models, in particular, Random Forests, Logistic Regression, and SVM algorithms are suitable. Generally, the determination model is parameterized during the training process, wherein in the training process the determined training dataset that is based on the test polymers and the explored sub-dataset is utilized for the training of the determination model. Generally, any known and suitable training method can then be utilized to train the trainable property determination model based on the training dataset, for instance, Newton algorithms, like steepest gradient methods, can be used.

[0033] Generally, in a preferred embodiment, further the trained determination model can be validated, for example, by applying the determination model to polymers that have not been part of the training dataset and for which respective measured technical application property values are known. If the trained determination model does not fulfil a predetermined criterion, for example, a predetermined accuracy, a plurality of new test synthesis specifications can be provided and the method of training the determination model can be repeated based on the plurality of new test synthesis specifications. This iterative training method allows for an optimization of the training dataset utilized for training the determination model and thus also leads to a more accurate and reliable determination model.

[0034] In a further aspect of the invention, a training apparatus for generating a machine learning based determination model, wherein the trained determination model is adapted to determine a technical application property of a polymer based on one or more polymer computed characterizing parameters, wherein a polymer computed characterizing parameter is indicative of a characteristic of a polymer and / or is derivable from one or more characteristics of the polymer, wherein the apparatus comprises one or more processors configured to a) performing the method as described above to determine one or more test synthesis specifications, b) receiving for the determined one or more test synthesis specifications the measured technical application property, c) training the machine learning based determination model by parameterizing the determination model based on computed characterizing parameters and the measured technical application property of the test polymers, and d) providing the trained determination model.

[0035] In a further aspect, a computer program product for generating a machine learning based determination model is presented, wherein the computer program product comprises program code means for causing a computing system, in particular, an apparatus as described above, to execute a method as described above.

[0036] In a further aspect of the invention, a computer-implemented method is presented for controlling and / or monitoring of an experiment series for determining a target polymer comprising a predetermined target technical application property, wherein the method comprises a) receiving a target value for the target technical application property, b) performing the method as described above to determine one or more test synthesis specifications, c) generating control data for controlling and / or monitoring the experiment series based on the one or more test synthesis specifications, wherein the control data comprise instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers and the performing of the respective measurement of the target technical application property, d) receiving measured values for the target technical application properties for the one or more test polymers, e) comparing the measured values of the target technical application property of the one or more test polymers with the target value and, based on the comparison, either I) determining a test polymer of the one or more test polymers as the target polymer and the respective test synthesis specification as the target synthesis specification, or II) determining a plurality of new test synthesis specifications according to the method as described above, and repeating the previous steps with the new test polymers, and f) providing the determined target polymer and the target synthesis specification.

[0037] In particular, the determining of a target polymer comprising a predetermined target technical application property only refers to a specific application of the experiment series and the determined test polymers utilizing a method as described above. In particular, the comparison of the measured technical application property value with the target value allows to determine whether the measured technical application property fulfils a predetermined criterion, for example, that the measured technical application property meets the target value of the target technical application property within predetermined limits. If such a criterion is fulfilled, the respective test polymer is determined as the target polymer and the respective test synthesis specification as the target synthesis specification and the method proceeds to the next step. However, if the comparison indicates that the measured technical application property value does not meet the target value within the predetermined limits, a next iteration step utilizing new test synthesis specifications has to be processed. In particular, for each iteration step of the iteration new test synthesis specifications are determined, preferably based on the previous test synthesis specifications, for instance, by amending one or more features of the previous test synthesis specification. For example, a predetermined number of test synthesis specifications can be determined for which the measured technical application property value is next to the target value, and the new synthesis specifications can be determined based on these test synthesis specifications, for example, by utilizing these as starting synthesis specifications. In particular, the test synthesis specification can in following iteration be utilized as part of the explored sub-dataset. Based on the new test synthesis specification, in each iteration step again test polymers are determined, a technical application property value is measured and the measured technical application property value is again compared with the target value such that the comparison can again lead to a further iteration step or if the respective criterion is fulfilled a respective new test polymer can be selected as the target polymer. Moreover, also an additional termination criterion for the iteration can be selected, for instance, a number of iteration steps can be determined before the iteration is terminated with a notification to a user that no target polymer could be found for the respective technical application property. However, alternatively, after a predetermined amount of iteration steps the method can further comprise amending the target technical application property, for instance, by increasing the predetermined limits around the technical application property and to repeat the iteration while utilizing the increased limits during the comparison. Further, additional or alternatively to amending the target technical application property, the variation parameters can be amended, for example, the ranges can be increased in order to allow for more potential test polymers to fall within the limits. This can allow to find a target polymer that meets the technical application property as much as possible, even if a meeting of the original goal might not be possible. After the target polymer has been determined as described above, the target polymer and the target synthesis specification can be provided, for instance, to a user via an output unit.

[0038] Generally, the receiving of measurement values for technical application properties as performed by the methods above, i.e. the method for training a determination model and the method for determining a target polymer, can preferably refer to providing control data for initiating a controlling and / or monitoring of a synthesis of one or more test or training polymers, respectively, based on the one or more test or training synthesis specifications, respectively, and further for initiating a testing process for measuring the technical application properties for the respective application for the one or more respective test or training polymers. In a preferred embodiment, the control data not only initializes but directly controls the synthesis process and the measurement process such that a complete automation of the generating of a determination model or of a determining of a target polymer can be achieved.

[0039] In a further aspect of the invention, an apparatus is presented for controlling and / or monitoring of an experiment series for determining a target polymer comprising a predetermined target technical application property, wherein the apparatus comprises one or more processors configured to a) receiving a target value for the target technical application property, b) performing the method as described above to determine one or more test synthesis specifications, c) generating control data for controlling and / or monitoring the experiment series based on the one or more test synthesis specifications, wherein the control data comprise instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers and the performing of the respective measurement of the target technical application property, d) receiving measured values for the target technical application properties for the one or more test polymers, e) comparing the measured values of the target technical application property of the one or more test polymers with the target value and, based on the comparison, either I) determining a test polymer of the one or more test polymers as the target polymer and the respective test synthesis specification as the target synthesis specification, or II) determining a plurality of new test synthesis specifications according to the method as described above, and repeating the previous steps with the new test polymers, and f) providing the determined target polymer and the target synthesis specification. In a further aspect, a computer program product for determining a target polymer is presented, wherein the computer program product comprises program code means for causing a computing system, in particular, a determining apparatus as described above, to execute the method as described above.

[0040] In a further aspect, control data for controlling and / or monitoring an experiment generated utilizing any of the methods described above is presented. Preferably, the control data is structured to instruct a machine to perform a synthesis of a polymer based on a synthesis specification as part of the experiment.

[0041] In a further aspect, a test, training and / or target synthesis specification comprising instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers provided by any of the methods described above, respectively, is presented. Preferably, the synthesis specification comprises control data for controlling and / or monitoring the synthesis.

[0042] In a further aspect, use of control data generated utilizing any of the methods described above for controlling and / or monitoring a synthesis system, in particular, laboratory equipment, for producing the one or more test polymers in accordance with the one or more test synthesis specifications is presented.

[0043] It shall be understood that the methods as described above, the apparatuses as described above and the computer program products as described above have similar and / or identical preferred embodiments, in particular, as defined in the dependent claims.

[0044] It shall be understood that a preferred embodiment of the present invention can also be any combination of the dependent claims or above embodiments with respective independent claims.

[0045] These and other aspects of the present invention will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Fig. 1 shows schematically and exemplarily a flowchart of a method for providing one or more test synthesis specifications for performing an experiment series according to the invention,

[0047] Fig. 2 shows schematically and exemplarily a flowchart of a method for generating a machine learning based determination model according to the invention,

[0048] Fig. 3 shows schematically and exemplarily a flowchart of a method for determining a target polymer according to the invention,

[0049] Fig. 4 shows schematically and exemplarily differences of an exemplary method according to the invention with respect to the conventional design of experiment method,

[0050] Figs. 5 and 6 show schematically and exemplarily block diagrams of exemplary system architectures of systems utilizing the invention.

[0051] DETAILED DESCRIPTION OF THE DRAWINGS

[0052] Fig. 1 shows schematically and exemplarily a flowchart of a computer implemented method for providing one or more test synthesis specifications each associated with a test polymer for performing of an experiment series. The method can be performed by any dedicated or general computing device, in particular, the method can also be performed not only by one processor of a computing device but by a plurality of processors, for example, in a distributed computing, like cloud computing or network computing.

[0053] In a first step, the method comprises receiving synthesis parameters defining ingredient and / or process parameter. Generally, ingredient parameters can refer to parameters determining amount, feed profile and type of substances utilized for synthesizing the polymer, for example, to an amount, feed profile, and type of monomers, prepolymers, but also of catalysts and other additives. Process parameters referto parameters indicative of the synthesis process of the polymer itself, for instance, to a temperature profile, a reactor type, a pressure profile, a stirring power, etc. utilized during the synthesis of the polymer. Preferably, the synthesis parameters are provided in accordance with an intended application of the results of the experiment series. However, it is further preferred that constraint information, for example, with respect to technical or physical constraints, of the synthesis system that is utilized for the synthesis of a test polymer, are also provided. Moreover, also interdependency information can also be provided that defined interdependencies between synthesis parameters. The defined ingredient and / or process parameter thus indicate the core of the search polymer space in a polymer space defined by the process parameters and ingredient parameters as coordinates, in which respective test synthesis specifications of test polymers are to be searched.

[0054] Further, variation parameters defining synthesis parameter variation ranges are received. The variation parameters can comprise average ranges of variations and / or maximum ranges of variations of the process parameters. Thus, where the synthesis parameters define the core of the polymer space, the variation parameters define how far the search space should reach in any of the dimensions of the polymer space. Based on the synthesis parameters and the variation parameters, thus respective limits of the test polymer space in the polymer space can be determined. The respectively derived limits can be soft limits or hard limits and can thus refer to limits that cannot be exceeded or that can be exceeded under predetermined criteria. Moreover, if constraint information or interdependency information is also provided, these can also be taken into account when determining the limits, for instance, for defining soft or hard limits. Thus, the limits define the synthesis specifications that can generally be determined as the test synthesis specifications since all test synthesis specifications that are determined must fall within the limits.

[0055] Further, from an explored dataset an explored sub-dataset is selected based on the derived limits. The explored dataset and the explored sub-dataset comprise one or more explored polymers, preferably a plurality of explored polymers and associated synthesis specifications that fall within the derived limits. Explored polymers are generally polymers for which one or more predetermined technical application properties are known, wherein the known technical application properties are related to the objective of the experimental series. The selection based on the limits can comprise selecting explored polymers that are within the limits or within at least some limits. In cases in which the explored dataset does not comprise explored polymers within at least some of the limits, also explored polymers outside of the limits that fulfil a predetermined similarity measure to the limits can be selected. Selecting at least some explored polymers outside the limits can also be advantageous in some cases where explored polymer within the limits are known. For example, for case where only less than a predetermined minimal number of explored polymers lies within the limits or if explored polymers outside the limit are known to contribute to specify relations between polymers and technical application properties. The explored sub-dataset reflects a current knowledge on the test polymer space. In particular, the explored sub-dataset reflects functional relations between polymers and technical application properties and also determines which parts of the test polymer space have already been explored.

[0056] Optionally, a plurality of potential test synthesis specifications associated with respect potential test polymers are derived, in particular, generated, based on the synthesis parameters and the. In particular, from one or more starting points, i.e. starting potential test synthesis specifications, further potential test synthesis specifications can automatically be generated, for example, by varying ingredient and / or process parameters of the potential test synthesis specifications within the limits. The variation can be an arbitrary variation or can be based on predetermined rules like statistical design of experiment. Moreover, also a human expert can provide at least some of the potential test synthesis specifications, or can supervise the generating of the plurality of potential test synthesis specifications, for example, by providing new start potential test synthesis specifications for the generation, if necessary. Preferably, at least some of the explored polymers of the explored sub-dataset are utilized as starting points for the generation of the potential test polymers.

[0057] In a next step, one or more test synthesis specifications are determined based on the derived limits and the explored sub-dataset and optionally further based on at least one of the constrain information, the interdependency information and the plurality of potential test synthesis specifications. For example, predetermined or learned rules that depend on the respective objective of the experiment series can be utilized for determining the one or more test synthesis specifications. In particular, statistical analysis methods can be utilized to statistically analyse the explored sub-dataset in the polymer space defined by the limits of the synthesis parameters, for example, to determine correlations and / or similarity measures, wherein the test synthesis specifications can then be selected based on the statistical analysis, for example, based on the determined correlations and / or similarity measures. In particular, it is preferred that the test synthesis specifications are determined in “white spots” that define parts of the polymer space which are not statistically well covered by the explored sub-dataset.

[0058] For example, the determining of the test synthesis specifications in the polymer space defined by the limits and based on the explored sub-dataset can comprise performing a dimensional reduction with respect to the synthesis parameters to determine a synthesis parameter space that is relevant for the experiment series. In particular, a principle component analysis or similar known method can be utilized to identify a reduced parameter set that allows to cluster the test synthesis specifications of the explored sub-dataset with respect to the resulting reduced parameter set, for instance, by determining clusters along one or more of the members of the reduced parameter set. For example, if a plurality of clusters containing similarly polymers and associated synthesis specifications of the explored sub-dataset with similar technical application properties, test synthesis specification can be determined for outside of the clusters to allow for more knowledge of the behavior of polymers outside the clusters. Further, for example, also a variable clustering method can be utilized for determining the reduced parameter set and optionally, for determining clusters in the synthesis parameters that can be used for selecting the test synthesis specifications. Preferably, the test synthesis specifications are selected based on the reduced parameter set such that the test synthesis specifications together with the explored subdataset cover a space defined by the reduced parameter set and the limits according to a predetermined criterion, for example, a similarity criterion or distance criterion.

[0059] If a plurality of potential test synthesis specifications are generated, the determining of the test synthesis specification can refer to a selection of the test synthesis specifications from the potential test synthesis specifications. In this case, the synthesis specifications of the explored sub-dataset can be compared with the potential test synthesis specifications, for instance, utilizing a similarity measure. The similarity measure is determined based on the synthesis parameters of the respective synthesis specification of the explored sub-dataset and the respective potential test synthesis specifications and can refer, for instance, to an Euclidian measure. Based on the similarity measure, it can be determined which potential test synthesis specifications are similar to the already explored sub-dataset and the test synthesis specifications can then be selected from the potential test synthesis specifications that are non-similar to the explored sub-dataset. The selection of the test synthesis specifications from the non-similar potential test synthesis specifications can then be based on respective statistical measures to cover the remaining polymer space according to a predefined criteria. However, even a random selection can be advantageous.

[0060] Generally, this determining of the test synthesis specification can be performed automatically but also in a user interaction process, for instance, a first selection of test synthesis specifications can be provided for a user for validation and the user can then amend the suggested test synthesis specifications, for instance, by adding or removing further test synthesis specifications or by choosing, for example, another criterion or statistical measure for determining the test synthesis specifications. The such determined test synthesis specifications can then be provided, for instance, as final result of the method to a user. However, it is preferred that further based on the test synthesis specifications control data are generated that allow for a direct or indirect control of a synthesis system to initiate the synthesis of the respective test polymers based on the respective test synthesis specifications. In a preferred embodiment, the above described method is applied for determining a training dataset for a machine learning based determination model and training such a machine learning based determination model based on the training dataset. Fig. 2 shows schematically and exemplarily a flowchart of such a preferred application. In particular, the steps of receiving synthesis parameters, receiving variation parameters, derive limits and and receiving an explored sub-dataset can be performed in the same way as described with respect to Fig. 1. In particular, the training polymers comprise at least one polymer of the explored sub-dataset and at least one of the test polymers. Thus, the method for determining test polymers is only applied in the context of training a machine learning based determination model. In the method shown in Fig. 2 after the test polymers have been determined, in particular, in accordance with the method already described with respect to Fig. 1 , control data can be provided to control or to initiate a controlling and / or monitoring of respective laboratory equipment, in particular, of a synthesis system and a measurement system. Preferably, the control data are generated based on the determined test synthesis specifications of the respective test polymers in order to control the synthesis system such that the determined test polymers are synthesized. The synthesized test polymers can then be provided to a measurement system automatically or, for example, with the help of a respective user, and subjected to respective testing procedures for measuring the technical application property for which a machine learning based determination model should be trained. The respectively measured technical application properties are then provided from the measurement system again, for example, to an apparatus performing the method for generating a machine learning based determination model or to a dedicated apparatus performing the steps of training and evaluating the machine learning based determination model. The such received measured technical application properties are then utilized together with computed characterizing parameters of the respective test polymers in a training dataset for training the determination model. In particular, any known training method can be utilized, for instance, steepest decent methods or other respective Newton’s algorithms can be utilized. After the determination model has been trained such that it can determine, based on one or more characterising parameter values the technical application property of a polymer, the respectively trained determination model can be provided and the method can end at this step.

[0061] However, it is preferred that in a further step the trained determination model is evaluated. In particular, a determination accuracy of the determination model can be determined. Generally, if the evaluation of the determination model is positive, for example, if the accuracy lies within predetermined limits (in Fig. 2 “Yes”), the determination model has been successfully trained and can be provided, for instance, to a respective storage to be later used in other applications. However, if the validation indicates that the trained determination model does not meet predetermined criterions (“No” in Fig. 2), the above described method can be repeated iteratively. In particular, new test synthesis specifications can be provided. The test synthesis specification of the previous step can further be utilized as part of a new explored sub-dataset. The method for determining the training dataset with new test polymers and then for training the determination model is then repeated based on the new explored sub-dataset. This iteration and optimization of the determination model can then be repeated until either the determination model passes the validation or another abortion criterion is fulfilled referring, for example, to an amount of repeating steps or an amount of determined test polymers. If no respective determination model can be provided at the end of the method, the method can also be repeated by amending the synthesis parameter, for example, by changing the variation parameters for finding a respectively suitable training dataset.

[0062] In another application of the method described with respect to Fig. 1 , a target polymer is determined comprising a predetermined target technical application property, in particular, a predetermined target value of a target technical application property. A schematic and exemplary flowchart of this method is shown in Fig. 3. In a first step, the method comprises providing a target value for a target technical application property, for example, via a user interface. The following steps for the determining of the test synthesis specifications and thus of the test polymers are then also performed in accordance with the method and embodiments of the method described with respect to Fig. 1 . After the test synthesis specification has been determined accordingly, control data is generated that is configured for controlling and / or monitoring or for initiating a controlling and / or monitoring, for example, a laboratory system comprising a synthesis system and a measurement system based on the determined test synthesis specifications. In particular, the synthesis system, for instance, a synthesis robot, is controlled to synthesize respective test polymers based on the test synthesis specifications and to provide the synthesized test polymers to the measurement system. The measurement system then applies the test polymers to one or more testing procedures to determine a measurement value for the test polymers for the target technical application property. The respective measurement values can then be again provided to an apparatus performing the method or to a dedicated apparatus for validating the measurement results of the test polymers. Based on the received measured technical application property values of the test polymers, it is determined whether or not one of the test polymers refers to the target polymer. In particular, for each of the test polymers the measured value is compared with the target value. If one or more of the measured values meet the target value the respective test polymers are determined as target polymers and the respective target synthesis specifications are provided. For example, the target synthesis specifications can be provided to a production system of a production plant in order to start a production of the target polymer on a bigger scale. However, if none of the test polymer comprises a measurement value meeting the target value within predetermined limits, a further optimization step can be initiated. In particular, new test synthesis specifications can be provided. Preferably, the new test synthesis specifications are provided based on a new explored sub-dataset comprising the test synthesis specifications of the previous iteration steps. In particular, it can be determined which of the previously determined test synthesis specifications were closest to meeting the target value, e.g. by using a distance measure like Euclidean distances. For example, predetermined rules for defining such a closest test synthesis specification can be utilized, like determining the ten closest synthesis specifications or determining all synthesis specifications that lie within a predetermined limit around the target value. The new test synthesis specifications can then be determined based on these determined closest test synthesis specifications. For example, the determined closest test synthesis specifications can be utilized as starting test synthesis specifications for the generating of the new test synthesis specifications, wherein the variations are limited to a predetermined variation range. In particular, in this case new limits can be defined that represent a smaller variation of the synthesis parameters then the previous limits, in particular, only vary the synthesis parameters of the starting test synthesis specifications. The method is then performed based on the provided new test synthesis specifications. This iteration can then be regarded as narrowing down the search space in which the target polymer is searched successively and efficiently until the target polymer can be determined.

[0063] Fig. 4 shows schematically and exemplary a more detailed embodiment of a design of experiment process performed in accordance with the methods as described, for instance, with respect to Figs. 1 , 2 and 3. In a first step synthesis parameters comprising ingredient and / or process parameters defining the ingredient and / or process parameter ranges are provided, for example, which type of monomers, type of additives, which amounts and which temperatures can be utilized for synthesizing the polymers, and a respective variation parameters defining the range of variation for the parameters are provided. Further, interdependencies and constraints of these parameters can also be taken into account, for instance, technical constraints and chemical interdependencies between parameters. Based on these information the limits of the search space for the new test polymers can be defined. In a next step, an explored sub-dataset is selected from a explored dataset that comprises synthesis specifications and respectively known technical application properties associated with the synthesis specifications. Determining the limits and also the explored sub-dataset based on the limits can further comprise determining the synthesis parameters with the most influence on the technical application property and to reduce the parameter space that is searched for the test synthesis specifications to these most influential synthesis parameters. Further, it can be determined if synthesis parameters within the limits can be varied on a continuous scale or on a categorical scale or a discontinuous scale. Further, targets like target technical application properties, but also targets with respect to quality, quantity and performance of a target polymer can be defined and taken into account. The constraint information can also refer to information with regard to a maximum number of experiments that should be performed with respect to the experimental series and also to a maximum number of iterations for the design of experiment process for arriving at the respective target. Moreover, the interdependency information can provide information with respect to interdependent parameters that can also influence the limits but also the determined test synthesis specifications. The explored sub-dataset and the limits are then utilized to suggest a set of new experiments depending, for example, on the respective objective of the design of experiment process. For example, statistical tools can be utilized to optimally cover the design space provided by the synthesis parameters with the explored sub-dataset and the new experiments. For example, statistical methods can be utilized that allow to avoid to select experiments belonging to the same class, comprising strong similarities or correlations. Accordingly, a much smaller set of experiments can be suggested that still allows, from an objective point of view, to cover the space of possible experiments very well. Generally, different statistical methods can be utilized, for example, space filling methods and multivariant statistical method that determine and fill “white spots” of the explored sub-dataset within the limits. In a next step of the commonly utilized design of experiment process the suggested new experiments can be discussed with experts for the experiments and it can be decided which of the suggested new experiments can be performed. During this process, some suggested experience might be discarded, for example, since experimental results are already known or since the experts indicate that a performance of the experiments might not be possible. The new experiments are then performed and utilized for the respective objective of the experiment series. In this example, the objective refers to the training of a machine learning determination model as in the example described with respect to Fig. 2. Thus, in this example the newly performed experiments are utilized in the training data for training the machine learning determination model. If a validation of the machine learning based determination model indicates that the training of the determination model was not successful with respect to one or more predetermined criterion, some steps of the design of experiment process can be repeated in an optimization routine. This design of experiment process not only allows to reduce the number of experiments that have to be performed extensively but also allows to select the to be performed experiments more objectively by minimizing the influence of an expert on the process. This further allows for a complete or semi-complete automation of the respective process. Fig. 5 illustrates a block diagram of an exemplarily system architecture of an automated laboratory system 1000 for synthesizing a polymer with a laboratory equipment control device 1102, a network 1150 and the synthesis specification, i.e. experiment, 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 as well as 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 split into several hierarchical layers: the hardware, the middleware and the interface layer. The hardware layer relates to hardware resources such as sensors and actuators, in particular for controlling and / or monitoring a synthesis of a polymer. The middleware relates to any of the known middleware for laboratory or plant synthesis operations. One example is LABS / QM, providing different abstractions to hardware, network and operating system such as low-level device control and message passing. The communication layer relates to communication protocols, wherein the protocol may be REST, which may be implemented over different transport protocols (i.e. UDP, TCP, Telemetry) that allow the exchange of messages between the laboratory equipment control device and laboratory equipment devices. Such software architecture allows to control and monitor laboratory equipment without having to interact with the hardware.

[0064] The synthesis specification module layer 1 154 may include: a mass storage layer, the computing layer, the interface layer. The storage layer is configured to provide mass storage for the explored dataset, as described in detail above. In particular, the functions performed by the apparatus, as described above, can be provided as program code means stored on the mass storage. Furthermore, synthesis specifications for a plurality of polymers can be stored in the mass storage. Such data may be stored in structured databases such as SQL databases or in a distributed file system such as HDFS, NoSQL databases such as HBase, MongoDB. The computing layer may include an application layer that allows to customize the functionalities provided by standard cloud services to perform computing processes based on objectives for an experiment series. Such functionalities can include determining test polymers as described above, providing test synthesis specifications for the test polymers, and providing the test synthesis specifications as control data, i.e. control signal, to the laboratory equipment control device. The interface layer may implement web services, network interfaces as UDP or TCP or Websocket interfaces. For communication with the laboratory equipment control device a REST API is implemented.

[0065] The client layer 1156 provides interfaces for end-users. For end-users, the client layer 1 156 can run client side Web applications, which provide interfaces to the synthesis specification module layer 1154 or the laboratory equipment control device layer 1152. Users may be provided with a Ul for selecting an experiment series objective, for example, the determining of a target polymer comprising a specific target technical application property value, and further for synthesis and variation parameters for defining a search space for the experimental series. In other examples, the users may be provided with a Ul for selecting more than one objective and respective values. The applications may be configured for users to monitor and control the laboratory equipment control device and the operation remotely. In other examples, the client device layer and the synthesis specification module layer may be integrated into one device. The alternatives described here are only for illustration purposes and should not be considered limiting.

[0066] Fig. 15 illustrates a block diagram of an exemplarily system architecture of a system and apparatus for generating a determination model for determining a technical application property, a network 2150 and a model generating module 2100 / 2110 that can be regarded as or comprising a training apparatus, a synthesis specification module 1100 / 1110, and a client device 2108. The system for generating a determination model includes a model generating module layer 2154 as part of model generating module and a client layer 2156 associated with the client devices 2108.

[0067] The model generating module layer 2154 may include: a mass storage layer, a computing layer, an interface layer. The storage layer is configured to provide mass storage for the data-driven determination model as described above. Furthermore, the mass storage is configured for storing synthesis specifications for polymers and technical application properties. Such data may be stored in structured databases such as SQL databases or in a distributed file system such as HDFS, NoSQL databases such as HBase, MongoDB. The computing layer may include an application layerthat allows to customize the functionalities provided by standard cloud services to perform computing processes for generating a determination model for determining properties of polymers. Such functionalities may include receiving for at least two previously selected, in particular, in accordance with the above described method, and measured training polymers the measurement data of at least one technical application property for each of the at least two previously measured test polymers, training the model according to the above described training principles based on the at least two previously measured test polymers and the at least one technical application property for each of the at least two previously measured test polymers, and providing via an output interface the determination model for the technical application property. The model generating module layer may be configured for deploying the generated model and the synthesis specification database to the synthesis specification module layer. This may include storing the generated model and the synthesis specification database in the mass storage devices associated with the synthesis specification module.

[0068] The model generating module layer may further be configured for determining a digital representation of the polymer associated with the synthesis specification from the synthesis specification. The digital representation may include a set of polymer computed characterizing parameters and polymer computed characterizing parameter values associated with a synthesis specification of each measured polymer. One way of deriving these polymer computed characterizing parameters can be to apply the SMILES algorithm or any other already above described principle. In case, where the model is generated based on the synthesis specification, a relation between the synthesis specification and the computed characterizing parameters may be stored in the mass storage devices associated with the model generating module. In such cases, deploying the model comprises providing that relation.

[0069] The interface layer may implement web services, network interfaces as UDP or TCP or Websocket interfaces. For communication with the client device a REST API is implemented in this example. The client layer 2156 provides access to mass storage devices, that contain synthesis specifications for polymers, and for at least two polymers at least one technical application property. The client layer further provides an interface for endusers. For end-users, the client layer 2156 may run client side Web applications, which provide interfaces to the model generation module layer 2154 or the mass storage devices associated with the client layer. Users may be provided with a Ul for selecting a technical application property. The user may further be provided with a Ul for selection of the synthesis specification data and the technical application property data associated with the synthesis specification data. The user interface may also provide an option for uploading the selected data to the model generating module layer and optionally an option to initiate model generation.

[0070] 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.

[0071] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in differing order. Furthermore, the outlined operations are only provided as examples, and some of the operations may be optional, combined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without detracting from the essence of the disclosed embodiments.

[0072] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

[0073] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0074] Procedures like the providing of process and ingredient parameters, the generating of the potential test synthesis specifications, the providing of the computed characterizing parameters, the determining of the computed characterizing parameter value, the determining of the test synthesis specifications, etc. performed by one or several units or devices can be performed by any other number of units or devices. These procedures can be implemented as program code means of a computer program and / or as dedicated hardware.

[0075] A 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 telecommunication systems.

[0076] Any units described herein may be processing units that are part of a classical computing system. Processing units may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may include any computer-readable storage media such as a non-volatile mass storage. If the computing system is distributed, the processing and / or memory capability may be distributed as well. The computing system may include multiple structures as “executable components”. The term “executable component” is a structure well understood in the field of computing as being a structure that can be software, hardware, or a combination thereof. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component may include software objects, routines, methods, and so forth, that may be executed on the computing system. This may include both an executable component in the heap of a computing system, or on computer- readable storage media. The structure of the executable component may exist on a computer-readable medium such that, when interpreted by one or more processors of a computing system, e.g., by a processor thread, the computing system is caused to perform a function. Such structure may be computer readable directly by the processors, for instance, as is the case if the executable component were binary, or it may be structured to be interpretable and / or compiled, for instance, whether in a single stage or in multiple stages, so as to generate such binary that is directly interpretable by the processors. In other instances, structures may be hard coded or hard wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Accordingly, the term “executable component” is a term for a structure that is well understood by those of ordinary skill in the art of computing, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to acts that are performed by one or more processing units of the computing system. If such acts are implemented in software, one or more processors direct the operation of the computing system in response to having executed computer-executable instructions that constitute an executable component. Computing system may also contain communication channels that allow the computing system to communicate with other computing systems over, for example, network. A “network” is defined as one or more data links that enable the transport of electronic data between computing systems and / or modules and / or other electronic devices. When information is transferred or provided over a network or another communications connection, for example, either hardwired, wireless, or a combination of hardwired or wireless, to a computing system, the computing system properly views the connection as a transmission medium. Transmission media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or specialpurpose computing system or combinations. While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for use in interfacing with a user. User interfaces act as input or output mechanism to users for instance via displays.

[0077] Those skilled in the art will appreciate that at least parts of the invention may be practiced in network computing environments with many types of computing system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, pagers, routers, switches, datacenters, wearables, such as glasses, and the like. The invention may also be practiced in distributed system environments where local and remote computing system, which are linked, for example, either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0078] Those skilled in the art will also appreciate that at least parts of the invention may be practiced in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and / or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling 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 other numerous advantages that can be obtained from such a model when deployed. The computing systems of the figures include various components or functional blocks that may implement the various embodiments disclosed herein as explained. The various components or functional blocks may be implemented on a local computing system or may be implemented on a distributed computing system that includes elements resident in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or less than the components illustrated in the figures and some of the components may be combined as circumstances warrant.

[0079] Any reference signs in the claims should not be construed as limiting the scope.

[0080] The invention refers to a method for providing test synthesis specifications for performing an experiment series. Synthesis parameters defining the ingredient and / or process parameter ranges in which test polymers for the experiment series are to be determined are provided. A plurality of potential test synthesis specifications are provided based on the synthesis parameters. Computed characterizing parameters are provided indicative of a computed characteristic of a polymer. A computed characterizing parameter value is determined for the provided computed characterizing parameters based on the plurality of potential test synthesis specifications. Test synthesis specifications are selected from the plurality of potential test synthesis specifications based on the determined computed characterizing parameter values and the test synthesis specifications are provided.

Claims

CLAIMS1 . A computer-implemented method for providing one or more test synthesis specifications, each associated with a test polymer, for performing an experiment series, wherein a test synthesis specification comprises instructions for synthesizing the respective test polymer for measuring one or more technical application properties of the test polymer in the experiment series, wherein the method comprises: receiving synthesis parameters defining ingredient and / or process parameters for which one or more test polymers for the experiment series are to be determined, wherein different ingredient and / or process parameters are associated with different potential test polymers, receiving variation parameters defining synthesis parameter variation ranges, deriving one or more limits for the synthesis parameters based on the synthesis parameters and the variation parameters, wherein the limits of the test synthesis specifications define which synthesis specifications are utilizable as test synthesis specifications, selecting from an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-dataset based on the derived limits, wherein the explored polymers and the explored sub-dataset comprise polymers for which one or more predetermined technical application properties are known, determining one or more test synthesis specifications associated with respective test polymers based on the derived limits and the explored sub-dataset, and providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring the synthesis of the respective one or more test polymers.

2. The method according to claim 1 , wherein the providing of the one or more test synthesis specifications comprises providing control data based on the one or more test synthesis specifications, wherein the control data is configured to control a synthesissystem to perform a synthesis of the one or more test polymers based on the one or more test synthesis specifications.

3. The method according to any of the proceeding claims, wherein the method further comprises a) receiving one or more measure technical application properties for the one or more test polymers, b) adding the measured test polymers to the explored sub-dataset, and c) determining further test synthesis specifications based on the limits and the new explored sub-dataset.

4. The method according to claim 3, wherein the further test synthesis specifications are further determined based on the received one or more measured technical application properties.

5. The method according to any of the preceding claims, wherein the test synthesis specifications are determined based on potential synthesis parameters defined by the derived limits.

6. The method according to any of the preceding claims, wherein the method further comprises receiving constraint information indicative of technical and / or productional constraints in the synthesis of a polymer and deriving the limits further based on the constraint information and / or determining one or more test synthesis specifications further based on the constrain information.

7. The method according to any of the preceding claims, wherein the method further comprises receiving interdependency information indicative of interdependencies of one or more synthesis parameters and deriving the limits further based on the interdependency information and / or determining one or more test synthesis specifications further based on the interdependency information.

8. The method according to any of the preceding claim, wherein a plurality of potential test synthesis specifications associated with respective potential test polymers are generated based on the synthesis parameters and the derived limits, wherein the determining of the test synthesis specification refers to selecting the test synthesis specifications from the generated potential test synthesis specifications.

9. The method according to claim 8, wherein a similarity measure is determined between the synthesis specifications of the explored sub-dataset and the potential testsynthesis specifications and the test synthesis specifications are determined based on the similarity measure, wherein the similarity measure is determined based on the synthesis parameters associated with the explored sub-dataset and the potential test synthesis specifications, respectively.

10. An apparatus for providing one or more test synthesis specifications, each associated with a test polymer, for performing an experiment series, wherein a test synthesis specification comprises instructions for synthesizing the respective test polymer for measuring one or more technical application properties of the test polymer in the experiment series, wherein the apparatus comprises one or more processors configured to: receiving synthesis parameters defining ingredient and / or process parameters for which one or more test polymers for the experiment series are to be determined, wherein different ingredient and / or process parameters are associated with different potential test polymers, receiving variation parameters defining synthesis parameter variation ranges, deriving one or more limits for the synthesis parameters based on the synthesis parameters and the variation parameters, wherein the limits of the test synthesis specifications define which synthesis specifications are utilizable as test synthesis specifications, selecting from an explored dataset comprising one or more explored polymers and associated synthesis specifications an explored sub-dataset based on the derived limits, wherein the explored polymers and the explored sub-dataset comprise polymers for which one or more predetermined technical application properties are known, determining one or more test synthesis specifications associated with respective test polymers based on the derived limits and the explored sub-dataset, and providing the one or more test synthesis specifications comprising instructions for controlling and / or monitoring the synthesis of the respective one or more test polymers.

11. A computer implemented method for generating a machine learning based determination model, wherein the trained determination model is adapted to determine a technical application property of a polymer based on one or more polymer computed characterizing parameters, wherein a polymer computed characterizing parameter is indicative of a characteristic of a polymer and / or is derivable from one or more characteristics of the polymer, wherein the method comprises: performing the method according to any of claims 1 to 9 to determine one or more test synthesis specifications, receiving for the determined one or more test synthesis specifications the measured technical application property, training the machine learning based determination model by parameterizing the determination model based on computed characterizing parameters and the measured technical application property of the test polymers, and providing the trained determination model.

12. A training apparatus for generating a machine learning based determination model, wherein the trained determination model is adapted to determine a technical application property of a polymer based on one or more polymer computed characterizing parameters, wherein a polymer computed characterizing parameter is indicative of a characteristic of a polymer and / or is derivable from one or more characteristics of the polymer, wherein the apparatus comprises one or more processors configured to: performing the method according to any of claims 1 to 9 to determine one or more test synthesis specifications, receiving for the determined one or more test synthesis specifications the measured technical application property, training the machine learning based determination model by parameterizing the determination model based on computed characterizing parameters and the measured technical application property of the test polymers, and providing the trained determination model.

13. A computer-implemented method for controlling and / or monitoring of an experiment series for determining a target polymer comprising a predetermined target technical application property, wherein the method comprises: receiving a target value for the target technical application property, performing the method according to any of claims 1 to 9 to determine one or more test synthesis specifications, generating control data for controlling and / or monitoring the experiment series based on the one or more test synthesis specifications, wherein the control data comprise instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers and the performing of the respective measurement of the target technical application property, receiving measured values for the target technical application properties for the one or more test polymers, comparing the measured values of the target technical application property of the one or more test polymers with the target value and, based on the comparison, either I) determining a test polymer of the one or more test polymers as the target polymer and the respective test synthesis specification as the target synthesis specification, or II) determining a plurality of new test synthesis specifications according to the method of any of claims 1 to 9, and repeating the previous steps with the new test polymers, and providing the determined target polymer and the target synthesis specification.

14. An apparatus for controlling and / or monitoring of an experiment series for determining a target polymer comprising a predetermined target technical application property, wherein the apparatus comprises one or more processors configured to: receiving a target value for the target technical application property, performing the method according to any of claims 1 to 9 to determine one or more test synthesis specifications,generating control data for controlling and / or monitoring the experiment series based on the one or more test synthesis specifications, wherein the control data comprise instructions for controlling and / or monitoring a synthesis of the respective one or more test polymers and the performing of the respective measurement of the target technical application property, receiving measured values for the target technical application properties for the one or more test polymers, comparing the measured values of the target technical application property of the one or more test polymers with the target value and, based on the comparison, either I) determining a test polymer of the one or more test polymers as the target polymer and the respective test synthesis specification as the target synthesis specification, or II) determining a plurality of new test synthesis specifications according to the method of any of claims 1 to 9, and repeating the previous steps with the new test polymers, and providing the determined target polymer and the target synthesis specification.

15. A computer program product is presented for providing one or more test synthesis specifications, wherein the computer program product comprises program code means for causing an apparatus according to claim 10 to execute the method according to any of claims 1 to 9.

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