System and method for determining catalytic properties of chemical compositions

Abstract

A system for determining catalytic properties of chemical compositions, the system comprising: a catalyst synthesis module for synthesizing chemical compositions, the catalyst synthesis module configured to synthesize the chemical compositions from one or more of a plurality of pre-set base materials and by processing the base materials under one or more of a plurality of processing conditions; a catalyst analysis module for analyzing catalytic properties of the synthesized chemical compositions under one or more of a plurality of test conditions and outputting analysis results; a control module for controlling the catalyst synthesis module, wherein the control module is configured to determine, based on the analysis results output by the catalyst test module for a previous chemical composition, a base material composition, a processing condition and / or a test condition for a next chemical composition to be synthesized.

Description

Technical Field

[0001] This disclosure relates to systems and methods for determining the catalytic properties of chemical compositions, determining processing conditions for forming chemical compositions with target catalytic properties, identifying chemical compositions with target catalytic properties, and / or producing chemical compositions with target catalytic properties. In various examples, the properties relate to hydrogen production, carbon dioxide conversion, or ammonia synthesis, and their respective reverse processes. Background Technology

[0002] The demand for more efficient materials is growing in applications such as photovoltaics, batteries, supercapacitors, and electrocatalysts. Electrocatalysts for hydrogen production or carbon dioxide conversion are of particular interest.

[0003] Using trial-and-error methods to study catalyst materials can be slow, expensive, and often results in biased and low-quality data. On the other hand, modeling catalyst structure and performance solely using first-principles calculations without experimental measurements to support these models has so far failed to yield the desired discovery of new catalyst candidates. These methods typically require a deep understanding of catalytic properties before predicting new characteristics, and knowledge of electrocatalytic performance and behavior at the atomic scale remains lacking.

[0004] Recently, methods for identifying potential catalysts through high-throughput experiments guided by expert analysis have also failed to yield substantial results in identifying innovative catalysts. These experiments are often biased by limited knowledge of catalytic performance, focusing on specific candidate groups while ignoring unexplored groups within the search domain that may have greater potential.

[0005] One objective of this public document is to at least partially address some of the aforementioned issues. Summary of the Invention

[0006] According to one aspect of this disclosure, a system for determining the catalytic properties of a chemical composition is provided, the system comprising: a catalyst synthesis module for synthesizing the chemical composition, the catalyst synthesis module being configured to synthesize the chemical composition from one or more of a plurality of preset base materials by treating the base materials under one or more of a plurality of processing conditions; a catalyst analysis module for analyzing the catalytic properties of the synthesized chemical composition under one or more of a plurality of test conditions and outputting analysis results; and a control module for controlling the catalyst synthesis module, wherein the control module is configured to determine the base material composition, processing conditions, and / or test conditions of a next chemical composition to be synthesized based on the analysis results output by the catalyst testing module for a previous chemical composition.

[0007] Optionally, the control module is configured to: refer to a predetermined target catalytic characteristic, and based on the basic materials, processing conditions and / or testing conditions that are predicted to improve the catalytic characteristics compared to the previous chemical composition, determine the basic materials, processing conditions and / or testing conditions for the next chemical composition to be synthesized.

[0008] Optionally, the control module is configured to execute a machine learning model to determine the base materials and processing and / or testing conditions of the next chemical composition, the machine learning model being configured to predict catalytic properties for the base material composition, processing and / or testing conditions.

[0009] Optionally, the base materials of the next chemical composition, as well as the processing and / or testing conditions, are determined based on a sampling function applied to the output of the machine learning model.

[0010] Optionally, the machine learning model is trained to output one or more target values ​​corresponding to catalytic properties based on training data, wherein the training data includes all or part of the basic material composition, the processing conditions and / or testing conditions for catalyst synthesis, and the relevant catalytic properties obtained from experimental measurements.

[0011] Optionally, the machine learning model is optimized based on the analysis results through active learning in a process space that includes material composition and processing conditions and / or testing conditions.

[0012] Optionally, the catalytic properties include one or more of the following: reaction product concentration, optionally corresponding to the target current and / or target voltage; reaction product selectivity; reactant conversion percentage; and catalyst stability.

[0013] Optionally, the catalytic properties are electrocatalytic and / or photocatalytic properties for hydrogen production or hydrogen oxidation, carbon dioxide conversion or reduction, and / or ammonia synthesis or cracking.

[0014] Optionally, the processing conditions include one or more of the following: different processing steps performed, the order in which different processing steps are performed, the processing temperature in each relevant processing step, the processing pH temperature in each relevant processing step, the processing time in each processing step, the liquid flow rate at which liquid is added to the sample, the storage time of the chemical components used in the synthesis, the deposition technique used, the deposition flow rate, the deposition time, the deposition cycle, the deposition spin coating rate, and the temperature applied during the deposition process.

[0015] Optionally, the processing conditions include one or more of the following: different processing steps performed, the order in which different processing steps are performed, the processing temperature in each relevant processing step, the processing pH value in each relevant processing step, the processing pressure in each relevant processing step, the revolutions per minute of the relevant mixing process, the processing atmosphere in each relevant processing step, the processing time in each processing step, the liquid flow rate of the liquid added to the sample, the storage time of the chemical components used in the synthesis, the deposition technique used, the deposition flow rate, the deposition time, the deposition cycle, the deposition spin coating rate, and the temperature applied during the deposition process.

[0016] Optionally, the base material includes a variety of single-metal or multi-metal materials.

[0017] Optionally, the base material is in powder form.

[0018] Optionally, the catalyst synthesis module is configured to mix the one or more base materials with one or more solvents to form a liquid catalyst precursor.

[0019] Optionally, the catalyst synthesis unit is configured to deposit the liquid catalyst precursor onto a conductive substrate to form a test sample.

[0020] Optionally, the deposition of the liquid catalyst precursor is carried out by spraying, blade coating, electrochemical deposition, dip coating, chemical bath and / or spin coating.

[0021] Optionally, the deposition of the liquid catalyst precursor is carried out by spraying, blade coating, electrochemical deposition, dip coating, chemical bath, spray pyrolysis, drop casting, sol-gel, chemical vapor deposition and / or spin coating.

[0022] Optionally, the catalyst synthesis module is configured to transfer the chemical composition to the catalyst analysis module.

[0023] Optionally, the catalyst synthesis module is configured to operate autonomously under the control of the control unit.

[0024] Optionally, the catalyst analysis module is configured to electrolyze a test sample containing a synthesized chemical composition deposited on a conductive substrate.

[0025] Optionally, the catalyst analysis module is configured to electrolyze multiple test samples containing the same chemical composition in parallel under their respective different test conditions. Alternatively or additionally, the catalyst analysis module may be configured to electrolyze multiple test samples containing different chemical compositions in parallel, optionally under their respective different test conditions or under the same test conditions.

[0026] Optionally, the test conditions include one or more of the following: electrolytic cell components, electrolyte composition, gas concentration in the electrolyte, applied voltage, applied current, electrolyte pH value, input gas humidity, pressure, and temperature.

[0027] Optionally, the test conditions include one or more of the following: electrolytic cell components, electrolyte composition, electrolyte concentration, electrolyte flow rate, gas concentration in the electrolyte, gas flow rate, gas concentration, applied voltage, applied current, electrolyte pH value, input gas humidity, output gas humidity, pressure, and temperature.

[0028] Optionally, the catalyst analysis module is further configured to analyze additional properties of the synthesized chemical composition, base material, and / or intermediate chemical composition.

[0029] Optionally, the catalyst analysis module is configured to measure the product composition and / or concentration during and / or after electrolysis.

[0030] Optionally, the catalyst analysis module includes a gas chromatography-mass spectrometry (GC-MS) instrument to measure the product composition and / or concentration.

[0031] Optionally, the catalyst analysis module includes an inductively coupled plasma spectrometer to measure the catalyst concentration in the electrolyte before, during, and / or after the experiment.

[0032] Optionally, the catalyst analysis module is configured to operate autonomously under the control of the control unit.

[0033] Optionally, the catalyst synthesis module includes a plurality of synthesis workstations, each of which is configured to perform one or more processing steps to synthesize the chemical composition, at least a subset of the plurality of synthesis workstations performing optional and / or alternative processing steps, and the control module is configured to control which optional and / or alternative synthesis workstations are used to synthesize each chemical composition.

[0034] Optionally, the control unit is further configured to control the sequence of synthesizing the chemical composition using a synthesis workstation.

[0035] Optionally, the catalyst analysis module includes a plurality of analysis workstations, each of which is configured to perform one or more analysis steps to analyze the chemical composition, at least a subset of the plurality of analysis workstations performing optional and / or alternative analysis steps, and the control module is configured to control which optional and / or alternative analysis workstations are used to analyze each chemical composition.

[0036] Optionally, the control unit is further configured to control the sequence of analyzing the chemical composition using an analysis workstation.

[0037] Optionally, the control unit is configured to determine a workflow, which includes a series of processing and / or analytical steps performed for each chemical composition by one or more synthesis workstations and / or analysis workstations.

[0038] According to a second aspect of this disclosure, a system for determining the catalytic properties of a chemical composition is provided, the system comprising: a modular catalyst synthesis module for synthesizing the chemical composition from one or more of a plurality of preset base materials and by treating the base materials under one or more of a plurality of processing conditions, the modular catalyst synthesis module including a plurality of synthesis workstations, each of the plurality of synthesis workstations being configured to perform one or more processing steps to synthesize the chemical composition, at least a subset of the plurality of synthesis workstations performing optional and / or alternative processing steps; a catalyst analysis module for analyzing the catalytic properties of the synthesized chemical composition under one or more of a plurality of test conditions and outputting analytical results; and a control module for controlling the catalyst synthesis module, wherein the control module is configured to determine the base material composition and processing conditions for synthesizing the chemical composition, and to determine a workflow comprising a series of processing steps performed by one or more of the synthesis workstations, the workflow defining which of the optional and / or alternative synthesis workstations are used to synthesize the chemical composition, and / or the order in which the synthesis workstations are used to synthesize the chemical composition.

[0039] Optionally, the control module is configured to determine the workflow for synthesizing the next chemical composition based on the output analysis results of the catalyst testing module on the previous chemical composition.

[0040] Optionally, the catalyst analysis module is modular and configured to analyze the properties of synthesized chemical compositions, base materials, and / or intermediate chemical compositions. The modular catalyst analysis module includes multiple analysis workstations, each configured to perform one or more analytical steps. At least a subset of the multiple analysis workstations performs optional and / or alternative analytical steps. The workflow determined by the control module further defines which optional and / or alternative analysis workstations are used to analyze the properties of the synthesized chemical compositions, base materials, and / or intermediate chemical compositions, and / or the order in which the analysis workstations are used to analyze the properties of the synthesized chemical compositions, base materials, and / or intermediate chemical compositions.

[0041] Optionally, the system is configured to execute multiple workflows in parallel to synthesize and analyze a variety of different chemical compositions.

[0042] Optionally, the chemical composition comprises inorganic nanoparticles.

[0043] Optionally, the system includes a transfer module for transferring the base material, the chemical composition, and / or intermediate chemical composition between workstations.

[0044] According to a third aspect of this disclosure, a method for autonomously determining the catalytic properties of a chemical composition is provided, the method comprising: autonomously synthesizing a chemical composition from one or more of a plurality of preset base materials and synthesizing the base materials by treating the base materials under one or more of a plurality of processing conditions; autonomously analyzing the catalytic properties of the synthesized chemical composition under one or more of a plurality of testing conditions and outputting analytical results; and controlling the synthesis using a control module 31 configured to determine the base material composition, processing conditions and / or testing conditions of the next chemical composition to be synthesized based on the analytical results output by the catalyst testing module for the previous chemical composition.

[0045] According to a fourth aspect of this disclosure, a method for determining the catalytic properties of a chemical composition is provided. The system includes a system comprising: autonomously synthesizing the chemical composition using a modular catalyst synthesis module, the modular catalyst synthesis module being configured to synthesize the chemical composition from one or more of a plurality of pre-defined base materials and by treating the base materials under one or more processing conditions among a plurality of processing conditions; the modular catalyst synthesis module including a plurality of synthesis workstations, each of the plurality of synthesis workstations being configured to perform one or more processing steps to synthesize the chemical composition, at least a subset of the plurality of synthesis workstations performing optional and / or alternative processing steps; and autonomously analyzing the chemical composition using a catalyst analysis module. The catalytic properties of the synthesized chemical composition are analyzed by a catalyst analysis module configured to analyze the catalytic properties of the synthesized chemical composition under one or more test conditions and output analytical results. The synthesis is controlled using a control module configured to control the catalyst synthesis module, wherein the control module is configured to determine the basic material composition and processing conditions used to synthesize the chemical composition, and to determine a workflow comprising a series of processing steps performed by one or more of the synthesis workstations. The workflow defines which optional and / or alternative synthesis workstations are used to synthesize the chemical composition, and / or the order in which the synthesis workstations are used to synthesize the chemical composition.

[0046] According to a fifth aspect of this disclosure, a method for identifying a chemical composition having a target catalytic property is provided, comprising the method according to a third or fourth aspect.

[0047] According to a sixth aspect of this disclosure, a method for producing a chemical composition having a target catalytic property is provided, comprising the method described according to a third or fourth aspect.

[0048] According to a seventh aspect of this disclosure, a method for determining processing conditions for forming a chemical composition having target catalytic properties is provided, comprising the method according to a third or fourth aspect. Attached Figure Description

[0049] Further features of this disclosure will now be described by way of non-limiting examples and with reference to the accompanying drawings, wherein:

[0050] Figure 1 An example system according to this disclosure is illustrated schematically;

[0051] Figure 2 This is a flowchart illustrating an example process according to this disclosure;

[0052] Figure 3 Another example system according to this disclosure is illustrated schematically;

[0053] Figure 4 This is a flowchart illustrating another example process according to this disclosure. Detailed Implementation

[0054] Figure 1 An example system 1, according to this disclosure, is schematically illustrated for determining the catalytic properties of a chemical composition, such as a chemical composition comprising inorganic nanoparticles. As shown, the system includes an experimental subsystem 2 and a computer subsystem 3. The experimental subsystem 2 includes a catalyst synthesis module 21 and a catalyst analysis module 23. The computer subsystem 1 includes a control module 31.

[0055] The catalyst synthesis module 21 is configured to synthesize a chemical composition. The catalyst synthesis module 21 can be configured to synthesize a chemical composition from one or more preset base materials. The catalyst synthesis module 21 can be configured to treat the base materials under one or more processing conditions to synthesize the chemical composition.

[0056] Catalyst synthesis module 21 is configured to mix the one or more base materials with one or more solvents to form a liquid catalyst precursor. The base materials may include various single-metal or multi-metal materials. The base materials may be formed from metallic elements or combinations of metallic elements, including transition metals and non-transition metals. For example, the base materials may be in powder form. The solvents may include one or more of the following: aqueous solvents (e.g., water, hydrochloric acid, sulfuric acid, sodium / potassium hydroxide, sodium / potassium carbonate) and organic solvents (e.g., ethanol, acetone, isopropanol, methanol).

[0057] The catalyst synthesis module 21 may include a solid processing unit 23, which is configured to extract a base material sample, measure the amount of the sample (e.g., by weight or volume) and load it into a sample container.

[0058] Solid processing unit 23 may include one or more containers holding the base material to be extracted. Solid processing unit 23 may include one or more robotic arms for manipulating the sample. Solid processing unit 23 may include one or more weighing balances for weighing the sample. Solid processing unit 23 may include a mixing unit for mixing two or more samples of different base materials together.

[0059] Solids handling unit 23 may include one or more dispensing units for dispensing samples from a base material container, and / or one or more extraction units for extracting samples from the base material container. For example, the dispensing and / or extraction units may include one or more pumps. For example, the dispensing unit may be attached to the base material container. For example, the extraction unit may be attached to one or more of the robotic arms.

[0060] The catalyst synthesis module 21 may include a liquid processing unit 24 configured to add liquid to a sample, mix the sample, and heat and / or cool the sample to form a liquid catalyst precursor. The liquid processing unit 24 may be configured to add a liquid solvent to the sample, and optionally add other liquids such as chemical buffers, acids, bases, or reducing agents.

[0061] The liquid handling unit 24 may include one or more liquid containers containing liquid. The liquid handling unit 24 may include one or more robotic arms for manipulating samples. The liquid handling unit 24 may include heating devices for heating and / or cooling samples, such as one or more thermocouple temperature devices. The liquid handling unit 24 may include stirring devices for stirring samples, such as magnetic stirrers or shakers.

[0062] Liquid handling unit 24 may include one or more dispensing units for dispensing liquid from a liquid container, and / or one or more extraction units for extracting liquid from the liquid container. For example, the dispensing and / or extraction units may include one or more pumps. For example, the dispensing unit may be attached to the liquid container. The extraction unit may also extract a solid catalyst (i.e., a solute) via a vacuum or gravity filtration process. For example, the extraction unit may be attached to one or more of the aforementioned robotic arms.

[0063] The catalyst synthesis module 21 can be further configured to deposit a liquid catalyst precursor onto a conductive substrate to form a test sample. For example, the conductive substrate can be a gas diffusion electrode or an anion-cation bipolar film. The conductive substrate can be made of an electrode material comprising one or more of the following materials: indium-doped tin oxide, fluorine-doped tin oxide, glassy carbon, nickel, gold, and silver. The liquid catalyst precursor can be deposited in thin film form.

[0064] The catalyst synthesis module 21 may include a deposition unit 25 configured to deposit a liquid catalyst precursor onto a conductive substrate to form a test sample. The deposition of the liquid catalyst precursor can be performed by spraying, spray pyrolysis, blade coating, electrochemical deposition, dip coating, chemical bath, drop casting, sputtering, sol-gel method, chemical vapor deposition, and / or spin coating. Accordingly, the deposition unit 25 may include equipment for spraying, spray pyrolysis, blade coating, electrochemical deposition, dip coating, chemical bath, drop casting, sputtering, sol-gel method, chemical vapor deposition, and / or spin coating. These various devices for different deposition methods may include the workstations described below. The deposition unit 25 may include one or more robotic arms for manipulating the substrate. The deposition unit 25 may include one or more robotic arms for manipulating the liquid catalyst precursor.

[0065] The catalyst synthesis module 21 can be configured to process the base material under one or more preset treatment conditions. The treatment conditions may involve the synthesis of catalyst precursors or the formation of test samples from catalyst precursors.

[0066] For example, the processing conditions associated with the synthesis of catalyst precursors may include one or more of the following: the processing steps performed, the order in which the different processing steps are performed, the processing temperature in each relevant processing step, the processing pH value in each relevant processing step, the processing pressure in each relevant processing step, the revolutions per minute of the relevant mixing process, the processing atmosphere in each relevant processing step, the processing time in each processing step, the liquid flow rate at which liquid is added to the sample, the storage time of the chemical components used in the synthesis, and the synthesis technique used.

[0067] For example, the processing conditions associated with forming test samples from catalyst precursors may include one or more of the following: the deposition technique used, deposition flow rate, deposition time, deposition cycle, deposition spin coating rate, deposition height, deposition location (e.g., Cartesian coordinates), and the temperature applied during deposition.

[0068] The catalyst synthesis module 21 can be configured to transfer a chemical composition (now part of a test sample) to the catalyst analysis module 22. This can be achieved, for example, by one or more robotic arms.

[0069] The catalyst analysis module 22 can be configured to electrolyze a test sample containing a synthesized chemical composition deposited on a conductive substrate, and analyze the catalytic properties of the synthesized chemical composition under one or more test conditions and output analytical results. For example, the catalyst synthesis module 22 can be configured to measure the product composition and / or concentration. These measurements can be performed before, during, and / or after electrolysis.

[0070] The catalyst analysis module 22 can be configured to perform one or more of electrocatalytic hydrogen production, carbon dioxide reduction, and photovoltaic conversion, and / or the reverse process. Accordingly, the catalytic characteristics can correspond to one or more of these catalytic processes.

[0071] The catalyst analysis module 22 may include an electrolytic cell unit 26 for performing electrolysis. The electrolytic cell unit 26 may be configured to receive a test sample and form an electrolytic cell using the test sample (e.g., as electrodes within the electrolytic cell). The electrolytic cell may include electrodes and an electrolyte. The electrolytic cell unit 26 may include a potentiostat for controlling the electrode voltage. The electrolytic cell unit 26 may further include a temperature control device, such as a heater, for controlling the temperature of the electrolytic cell.

[0072] The catalyst analysis module 22 can be configured to electrolyze multiple test samples in parallel. The multiple test samples may include the same chemical composition, but with different test conditions. Alternatively or additionally, the multiple test samples may include different chemical compositions, but with the same test conditions.

[0073] Electrolyte unit 26 can be configured to control the test conditions for each test sample. For example, test conditions may include one or more of the following: electrolyte components, electrolyte stack, electrolyte composition, electrolyte concentration, electrolyte flow rate, gas concentration in electrolyte, gas flow rate, gas concentration, applied voltage, applied current (e.g., DC or AC), electrolyte pH, input gas humidity, output gas humidity, pressure, and temperature.

[0074] The catalyst analysis module 22 may further include a measurement unit 27 for measuring catalytic performance. The measurement unit 27 may include, for example, a gas chromatography-mass spectrometry (GC-MS) instrument to measure product composition and / or concentration, and / or an inductively coupled plasma atomic emission spectrometer (ICP-AES) instrument to measure catalyst decomposition.

[0075] The measurement results obtained by the catalyst analysis module 22 can be provided to the control module 31 for further analysis. Catalytic characteristics can be determined based on the obtained measurement results. Catalytic characteristics may include one or more of the following: reaction product concentration, optionally corresponding to the target current and / or target voltage; reaction product selectivity; reactant conversion percentage; and catalyst stability.

[0076] The control module 31 can be configured to control the catalyst synthesis module 21 and / or the catalyst analysis module 22. The catalyst synthesis module 21 and / or the catalyst analysis module 22 can be configured to operate autonomously under the control of the control module 31. The control module 31 may include one or more experimental subsystem control units 33 for controlling the modules forming the experimental subsystem. The experimental subsystem control units 33 can be configured to execute one or more machine learning algorithms, which are configured to control the modules forming the experimental subsystem based on the basic material composition of the test sample, processing conditions, and / or testing conditions determined by the control module 31.

[0077] Furthermore, the one or more experimental subsystem control units 33 can receive metadata related to the modules constituting the experimental subsystem as input. This metadata enables the experimental subsystem control unit 33 to perform feedback control based on the metadata. Therefore, the metadata may include metadata corresponding to processing or testing conditions. Regarding physical manipulation (e.g., via a robotic arm), data collected using cameras, position sensors, and / or motion sensors can be used.

[0078] The control module 31 may include a prediction unit 32, used to determine one or both of the basic material composition, processing conditions, and testing conditions of the next chemical composition to be synthesized, based on the results output by the catalyst testing module for the previous chemical composition. The prediction unit 32 may be configured to: determine the basic materials, processing conditions, and / or testing conditions of the next chemical composition to be synthesized, based on the predicted basic materials and processing and / or testing conditions that can improve the catalytic properties compared to the previous chemical composition, with reference to a predetermined target catalytic characteristic.

[0079] The prediction unit 32 can be configured to execute a machine learning model to determine the base materials and processing conditions of the next chemical composition, said machine learning model being configured to predict catalytic properties for the base material composition, processing conditions, and / or test conditions. The machine learning model can be, for example, a random forest regression model or a Bayesian neural network.

[0080] The machine learning model can be trained to output one or more target values ​​corresponding to catalytic properties based on training data, wherein the training data includes all or part of the basic material composition, the processing conditions and / or testing conditions for catalyst synthesis, and the relevant catalytic properties obtained from experimental measurements.

[0081] The machine learning model can be periodically optimized based on the results of the catalyst analysis module 22, for example through active learning in a process space that includes material composition and processing and / or testing conditions. The results may originate from the measurement unit and / or electrolyzer unit and may be related to base materials, chemical compositions, and / or intermediate chemical compositions.

[0082] The initial basic material composition, processing conditions, and testing conditions can be randomly selected or determined based on the initial prediction of the control module 31 (e.g., prediction unit 32).

[0083] Control module 31 (e.g., prediction unit 32) can be configured to iteratively change one or more of the base material composition, processing conditions, and testing conditions in subsequent experiments. The system can be configured to operate in an iterative loop of synthesis and analysis until preset conditions are met. These preset conditions may be that the catalytic properties of the chemical composition converge to a set of preset catalytic properties. The base materials, processing conditions, and / or testing conditions of the next chemical composition are determined based on an acquisition function applied to the output of the machine learning model. The acquisition function can determine the exploration of the process space, including material composition, processing conditions, and / or testing conditions.

[0084] Figure 2 This is a flowchart illustrating an example process according to this disclosure. In step S1, initial base material composition, processing conditions, and test conditions are selected. In step S2, a liquid catalyst precursor is synthesized from base materials, solvent, and additives using the solid-state processing unit 23 and liquid-state processing unit 24 of the synthesis module. In step S3, the liquid catalyst precursor is deposited as a thin film onto a conductive substrate using the deposition unit 25 of the synthesis module to form a test sample. In step S4, the test sample is integrated into an electrolytic cell using the electrolytic cell unit 26 of the analysis module, and electrolysis is performed. In step S5, the catalytic performance is measured using the measurement unit 27 of the analysis module. In step S6, the measurement results, along with data related to the base material composition, processing, and test conditions, are provided to the control module 31. In step S7, the machine learning model is updated based on the received measurement results and data. In step S8, the control unit determines whether the catalytic characteristics correspond to preset desired characteristics. If yes, the process ends. If not, the process continues to step S9. In step S9, the control unit determines the subsequent basic material composition, processing conditions, and testing conditions. The process then returns to step S2.

[0085] Experimental subsystem 2 (e.g., one or both of catalyst synthesis module 21 and catalyst analysis module 22) may include multiple workstations, such as a synthesis workstation and an analysis workstation, respectively. Each workstation is a modular hardware unit configured to perform specific functions within experimental subsystem 2, as well as within catalyst synthesis module 21 and catalyst analysis module 22.

[0086] A synthesis workstation can be configured to perform one or more processing steps to synthesize a chemical composition. An analytical workstation can be configured to perform one or more analytical steps to analyze a chemical composition, a base material, and / or an intermediate chemical composition.

[0087] For example, the hardware units and / or sub-units forming the aforementioned solid processing unit 23, liquid processing unit 24, deposition unit 25, electrolytic cell unit 26, and measurement unit 27 may form one or more workstations. Hardware units and / or sub-units configured as described above to transfer base materials, intermediate chemical compositions, or chemical compositions from one workstation to another may form sub-modules of a transfer module. The transfer module may be configured to transfer base materials, intermediate chemical compositions, and / or chemical compositions between workstations.

[0088] At least some workstations can perform optional and / or alternative functions. At least a subset of the plurality of synthesis workstations can perform optional and / or alternative processing steps. At least a subset of the plurality of analysis workstations can perform optional and / or alternative analysis steps.

[0089] The control module may be configured to control which optional and / or alternative synthesis workstations are used to synthesize each chemical composition. Alternatively or additionally, the control module may be configured to control which optional and / or alternative analytical workstations are used to analyze each chemical composition. The control unit may be further configured to control the order in which the chemical compositions are synthesized or analyzed using the synthesis workstations and / or analytical workstations.

[0090] The control unit can be configured to determine a workflow comprising a series of processing and / or analytical steps performed for each chemical composition by one or more synthesis and / or analysis workstations. The control unit can also be configured to control the operating parameters of each workstation. Therefore, appropriate workstations can be selected as needed to synthesize chemical compositions under desired processing conditions and to analyze chemical compositions, base materials, and / or intermediate chemical compositions as required.

[0091] Therefore, optional and / or alternative workstations and / or the operating parameters of each workstation (whether optional and / or alternative) can define the parameter space for synthesizing chemical compositions. Thus, the system is capable of dynamically synthesizing chemical compositions based on the parameter space that needs to be explored to synthesize chemical compositions that satisfy one or more target properties.

[0092] Figure 3 Another example system 1 for determining the catalytic properties of a chemical composition according to this disclosure is schematically illustrated. As shown, the system includes an experimental subsystem 2 and a computer subsystem 3. The experimental subsystem 2 includes a catalyst synthesis module 21, which comprises multiple workstations.

[0093] As shown in the figure, multiple workstations may include one or more of the following: automatic feeding workstation 41, microwave synthesis workstation 42, spray pyrolysis workstation 43, and electrochemical deposition workstation 47.

[0094] The automated feeding workstation 41 automates the precise measurement and mixing of base materials to prepare chemical compositions for synthesis or deposition. It involves using an automated system to precisely measure the required amount of each base material (liquid or solid). These materials are then mixed under controlled conditions to ensure homogeneity. The prepared chemical compositions are consistent and ready for direct use in further processing, whether synthesized in a microwave workstation or deposited in a spray pyrolysis or electrochemical deposition workstation.

[0095] Microwave synthesis workstation 42 utilizes microwave radiation to heat and synthesize compounds. An intermediate chemical composition (partially treated material) is subjected to microwave energy. The microwaves cause rapid heating of the material, initiating a chemical reaction that ultimately completes the preparation of the composition. The result is a homogeneous final chemical composition that can be directly used in the next step. Depending on the next step, this preparation may include processes such as crimping / de-crimping, capping / recapping, filtration, centrifugation, or mixing with other liquid compounds.

[0096] The spray pyrolysis workstation 43 is used to deposit a thin film of a chemical composition onto a conductive substrate. The chemical composition is first dissolved or suspended in a solution (typically a solvent). The solution is then atomized into fine droplets and sprayed onto a heated conductive substrate. When the droplets come into contact with the high-temperature surface, a pyrolysis reaction occurs, transforming the precursor solution into a solid thin film. A uniform thin film of the target chemical composition is ultimately formed on the substrate, ready for direct testing.

[0097] Electrochemical deposition workstation 47 utilizes an electrochemical process to deposit materials onto a conductive substrate. The substrate is immersed in an electrolyte containing the material to be deposited. By applying an electric current, material ions are reduced and deposited on the substrate surface. This ultimately forms a material layer electrochemically bonded to the substrate. This final chemical composition can then be used directly for testing.

[0098] Experimental subsystem 2 further includes a catalyst analysis module 22, which comprises multiple workstations. As shown in the figure, these workstations may include a UV-Vis spectroscopy workstation 44, an X-ray fluorescence workstation 45, and a parallel electrolyzer workstation 46.

[0099] Experimental subsystem 2 may further include a transfer module (not shown), the function of which is provided by Figure 3 Arrows between workstations indicate this.

[0100] The computer subsystem includes a control module 31, which comprises a prediction unit 32 and an experimental workflow control unit 34. The experimental workflow control unit is configured to determine a workflow comprising a series of processing and / or analytical steps performed for each chemical composition by one or more synthesis and / or analysis workstations. The workflow control unit 34 may further determine the operating parameters of each workstation in the workflow. The workflow is determined based on the output of the prediction unit 32, such as the underlying materials and processing conditions.

[0101] The catalyst synthesis module 21 can be configured to synthesize chemical compositions by treating the base material under one or more of a variety of processing conditions corresponding to the parameter space defined by the workstation in the defined workflow.

[0102] For example, catalyst synthesis module 21 may be configured to mix one or more base materials with one or more solvents using one or more preparation workstations (such as automatic feeding module 41) to form a liquid catalyst precursor. The preparation workstation is a workstation used to prepare the liquid catalyst precursor, such as a component forming solid processing unit 23 and liquid processing unit 24.

[0103] The catalyst synthesis module 21 can be further configured to deposit a liquid catalyst precursor onto a conductive substrate using a deposition workstation (e.g., a coupled spray pyrolysis workstation 42 and an electrochemical deposition workstation 47) to form a test sample. The deposition workstations are used to form test samples from the liquid catalyst, such as those constituting deposition unit 25.

[0104] The processing conditions associated with the synthesis of catalyst precursors at the preparation workstation may include one or more of the following: the different processing steps performed, the order in which the different processing steps are performed, the processing temperature in each relevant processing step, the processing pH and temperature in each relevant processing step, the processing pressure in each relevant processing step, the revolutions per minute of the relevant mixing process, the processing atmosphere in each relevant processing step, the processing time in each processing step, the liquid flow rate at which liquid is added to the sample, the storage time of the chemical components used in the synthesis, the synthesis technique used, the deposition technique used, the deposition flow rate, the deposition time, the deposition cycle, the deposition spin coating rate, and the temperature applied during the deposition process.

[0105] For example, the processing conditions associated with forming test samples from catalyst precursors using a deposition workstation may include one or more of the following: the deposition technique used, deposition flow rate, deposition time, deposition cycle, deposition spin coating rate, deposition height, deposition location (e.g., Cartesian coordinates), and the temperature applied during the deposition process.

[0106] Catalyst analysis module 2 can be configured to: analyze the intrinsic catalytic properties of synthetic chemicals and output analytical results (e.g., using UV-Vis spectroscopy workstation 44 or X-ray fluorescence workstation 45), and analyze the performance of the catalytic properties of test samples (e.g., using parallel electrolyzer workstation 46).

[0107] Transfer module (its functions are provided by) Figure 3 The arrows between workstations (indicated by the arrows) may include one or more of the following: a robotic arm, a robotic arm on a linear stroke extender, a mobile robot, or a human-robot collaborative system. The main function of the transfer module is to transfer the input materials required by each workstation and collect its output products.

[0108] Figure 3 Two alternative example workflows are shown. The first workflow is represented by transfer module steps A, B, C, D, and E. Prediction unit 32 predicts the base material composition, processing conditions, and testing conditions. Experimental workflow control unit 34 then determines a workflow that first requires automatic feeding workstation 41, followed by microwave reactor workstation 5, then UV-Vis spectroscopy workstation 44, then spray pyrolysis workstation 43, and finally completed by parallel electrolyzer workstation 46.

[0109] The second workflow is illustrated by transfer module steps G, H, I, and J. Prediction unit 32 predicts different base material compositions, alternative processing conditions, and testing conditions. This second workflow is planned by experimental workflow control unit 34 and utilizes automatic feeding workstation 41, electrochemical deposition workstation 47, X-ray fluorescence workstation 45, followed by parallel electrolytic cell workstation 46.

[0110] Multiple workflows can be executed in parallel, that is, simultaneously. For example, the first and second workflows mentioned above can both be executed in parallel.

[0111] In step F, the electrolysis measurement results and / or other data from the catalyst analysis module 22 can be provided to the control module 31.

[0112] At least some workstations can be selectively coupled to the system, for example, interchangeable. In other words, workstations not needed for a particular experiment or experimental group can be decoupled and removed from the system, and recoupled to the system when needed. Alternatively, each different workstation can be permanently coupled within the system. For example, electrodeposition workstation 47 can be configured according to... Figure 3 The GHIJ workflow requires selective coupling.

[0113] Figure 4The flowchart, based on this disclosure, illustrates the advanced process. In step S21, an initial base material composition, processing conditions, and test conditions are selected. In step S22, the reaction path (corresponding to the processing conditions) required to achieve the selected composition is determined based on the available parameter space. Step S23 identifies available workstations for the reaction path (including uncoupled and coupled ones), which serve as input to step S22.

[0114] In step S24, the system determines whether an uncoupled workstation is needed to execute the synthesis and characterization route. If an uncoupled workstation is needed, the process proceeds to step S25. If not, the process continues to step S26, in which the preparation workstation of the catalyst synthesis module synthesizes a liquid catalyst precursor from base materials, solvents, and additives.

[0115] Step S27 assesses whether intrinsic analysis of the liquid precursor is required based on the test conditions determined by the control module prediction unit. If so, the process proceeds to step S28, where one or more workstations within the catalyst analysis module perform intrinsic analysis of the liquid precursor. If intrinsic analysis is not required, the process proceeds to step S29, where the liquid catalyst precursor is deposited onto a conductive substrate by one or more workstations within the catalyst synthesis module deposition unit to form a thin film, thereby preparing the test sample.

[0116] In step S30, intrinsic analysis of the nanomaterial coating (corresponding to the chemical composition) is evaluated. If the test conditions determined by the control module prediction unit require intrinsic analysis, the process proceeds to step S31, where the analysis is performed by a workstation within the catalyst analysis module. If not, the process proceeds to step S32, where the test sample is integrated into the electrolyzer workstation of the catalyst analysis module and electrolyzed.

[0117] Step S33 involves measuring the catalytic performance of the sample in the catalyst analysis module. Step S34 then determines, based on the test conditions determined by the prediction unit of the control module, whether intrinsic analysis of the used nanomaterial coating is required. If yes, one or more workstations within the catalyst analysis module perform this intrinsic analysis in step S35. If not, the composition, processing conditions, test conditions, and performance measurement results are transmitted to the control module in step S36.

[0118] In step S37, the machine learning model of prediction unit 32 is updated based on the received measurement results and data. In step S38, the control module checks whether the catalytic characteristics have reached the preset target characteristics. If the desired characteristics are reached, the process ends. If not, the process continues to step S39, where the subsequent basic material composition, processing conditions, and testing conditions are determined. The process then returns to step S22 to begin a new iteration.

[0119] Machine learning enables more intelligent selection of experimental schemes based on predefined objectives (such as target characteristics). This makes the search for new catalysts less exhaustive and more economical. The machine learning module uses a random forest regression method to build a predictive model. This model learns from experimental data generated during the synthesis and testing of electrocatalysts, identifying correlations and patterns. Based on the random forest model, the machine learning module selects candidate compositions for synthesis and testing. This method ensures a diverse and unbiased exploration of the composition space, minimizing the risk of missing potential candidate materials. As the system iterates through synthesis, testing, and prediction, the machine learning module continuously optimizes its predictive model. This iterative process improves prediction accuracy, gradually increasing the efficiency of candidate material screening.

[0120] The experimental setup supports high-throughput, precise, and controllable electrocatalyst synthesis and testing. Solid processing unit 23 precisely loads, weighs, and mixes solid powder samples using a robotic arm, precision pump, and weighing balance. This ensures the consistency and reproducibility of catalyst formulations. Liquid processing unit 24, equipped with sample containers, thermocouple heating / cooling plates, a magnetic stirrer, and a liquid pump, precisely mixes, stirs, and heats / cools liquid precursor samples. This contributes to improved reproducibility of catalyst synthesis. Liquid precursors are uniformly deposited onto a conductive substrate using techniques such as spraying, blade coating, and spin coating. This yields catalyst films with precise composition and thickness. The electrolyzer module supports simultaneous testing of multiple catalysts. This parallel configuration accelerates experimental throughput. Gas chromatography-mass spectrometry (GC-MS) measures the composition and concentration of products after electrocatalysis, revealing reaction pathways and catalytic efficiency. Inductively coupled plasma atomic emission spectrometry (ICP-AES) measures the concentration of the catalyst in the electrolyte, indicating the catalyst decomposition during the electrolysis experiment.

[0121] The experimental setup supports high-throughput, precise, and controlled synthesis and testing of electrocatalysts. The workstation in the catalyst synthesis module consists of instruments performing processes such as cap crimping / uncrimping, cap sealing / opening, liquid handling, solid handling, centrifugation, vortex mixing, ultrasonic dispersion and emulsification, heating, sample manipulation, cooling, filtration, glass ampoule / syringe preparation, high-shear homogenization, evaporation, weighing, and oscillation to ensure the stability of the catalyst formulation. Liquid precursors are uniformly deposited onto conductive substrates using techniques such as spraying, spray pyrolysis, blade coating, electrochemical deposition, dip coating, chemical bath deposition, drop casting, sputtering, sol-gel, chemical vapor deposition, and / or spin coating to obtain catalyst films with precise composition and thickness. A parallel electrolyzer workstation can simultaneously test multiple catalysts, thereby accelerating experimental throughput. Gas chromatography-mass spectrometry (GC-MS) is used to measure the composition and concentration of products after electrocatalysis, while inductively coupled plasma spectrometry (ICP-MS) assesses the decomposition of the catalyst by measuring its concentration in the electrolyte.

[0122] The system disclosed in this paper addresses the challenges of existing automated systems, such as limited flexibility and parameter space—automated systems typically lack the adaptability of human researchers and are geographically fixed. This limits the scope of experiments and may result in overly narrow exploration of potential materials. Furthermore, the time-consuming process of developing and integrating new functionalities into autonomous systems can slow the discovery process and reduce responsiveness to new insights or unexpected results.

[0123] Data management and analysis capabilities facilitate data collection, organization, and interpretation. During synthesis and testing, data is captured and systematically organized. This structured database ensures systematic tracking and analysis of catalyst performance trends. Using the collected data, machine learning algorithms can perform refined analysis of catalytic performance. These algorithms compare experimental results with preset boundary conditions and target catalytic characteristics, supporting informed decision-making in subsequent experiments.

[0124] The iterative process of this invention promotes continuous improvement and optimization. The iterative cycle begins with a machine learning module selecting candidate compositions for synthesis. These candidate compositions undergo controlled synthesis, electrochemical testing, and analysis. The resulting data is used to optimize the module's predictive model, guiding the selection of subsequent candidate compositions. With each iteration, the accuracy of the machine learning module's predictive model gradually improves. Based on newly acquired data, the module fine-tunes the prediction results, progressively narrowing down the range of optimal electrocatalyst compositions.

[0125] Therefore, the integration of machine learning algorithms with advanced experimental techniques has the following advantages:

[0126] • Accelerated discovery: The parallel setup of the system, rapid synthesis, and data-driven prediction can significantly accelerate the discovery of electrocatalysts, enabling researchers to efficiently explore a wide range of components.

[0127] • Enhanced exploration: Unbiased candidate selection and comprehensive analysis ensure thorough exploration of catalyst composition, reducing the risk of missing high-performance materials.

[0128] • High-quality data: A systematic data acquisition and analysis mechanism can generate high-quality, structured data, thereby drawing reliable conclusions and profound insights about catalyst performance.

[0129] • Smart decision-making: The predictions from the machine learning module enable researchers to make smart decisions about the next set of experiments, optimizing resource utilization.

[0130] • Iterative Learning: The iterative nature of the system encourages continuous learning and improvement. The machine learning module optimizes its predictive model in each iteration, resulting in increasingly accurate predictions and higher-quality catalyst discovery.

Claims

1. A system for determining the catalytic properties of a chemical composition, the system comprising: A catalyst synthesis module for synthesizing a chemical composition, the catalyst synthesis module being configured to synthesize the chemical composition from one or more of a plurality of preset base materials and by treating the base materials under one or more of a plurality of processing conditions; The catalyst analysis module is used to analyze the catalytic properties of the synthesized chemical composition under one or more test conditions and output the analysis results. A control module is used to control the catalyst synthesis module, wherein the control module is configured to determine the basic material composition, processing conditions and / or testing conditions of the next chemical composition to be synthesized based on the analysis results output by the catalyst testing module on the previous chemical composition.

2. The system according to claim 1, wherein, The control module is configured to: determine the basic materials, processing conditions and / or testing conditions for the next chemical composition to be synthesized, based on the basic materials and processing and / or testing conditions that are predicted to improve the catalytic properties compared to the previous chemical composition, with reference to a predetermined target catalytic property.

3. The system according to any of the preceding claims, wherein, The control module is configured to execute a machine learning model to determine the base materials and processing and / or testing conditions of the next chemical composition, the machine learning model being configured to predict catalytic properties based on the base material composition, processing and / or testing conditions.

4. The system according to claim 3, wherein, The base materials, processing conditions, and / or testing conditions of the next chemical composition are determined based on an acquisition function applied to the output of the machine learning model.

5. The system according to claim 3 or 4, wherein, The machine learning model is trained to output one or more target values ​​corresponding to catalytic properties based on training data, which includes all or part of the basic material composition, the processing conditions and / or testing conditions for catalyst synthesis, and the relevant catalytic properties obtained from experimental measurements.

6. The system according to any one of claims 3 to 5, wherein, The machine learning model is optimized based on the analysis results through active learning in a process space that includes material composition, processing conditions and / or testing conditions.

7. The system according to any of the preceding claims, wherein, The catalytic properties include one or more of the following: reaction product concentration, optionally corresponding to the target current and / or target voltage; reaction product selectivity; Percentage of reactant conversion; and catalyst stability.

8. The system according to any of the preceding claims, wherein, The catalytic properties are electrocatalytic and / or photocatalytic properties used for hydrogen production or hydrogen oxidation, carbon dioxide conversion or reduction, and / or ammonia synthesis or cracking.

9. The system according to any of the preceding claims, wherein, The processing conditions include one or more of the following: different processing steps performed, the order in which different processing steps are performed, the processing temperature in each relevant processing step, the processing pH value in each relevant processing step, the processing pressure in each relevant processing step, the revolutions per minute of the relevant mixing process, the processing atmosphere in each relevant processing step, the processing time in each processing step, the liquid flow rate at which liquid is added to the sample, the storage time of the chemical components used in the synthesis, the deposition technique used, the deposition flow rate, the deposition time, the deposition cycle, the deposition spin coating rate, and the temperature applied during the deposition process.

10. The system according to any of the preceding claims, wherein, The base materials include a variety of single-metal or multi-metal materials.

11. The system according to any of the preceding claims, wherein, The base material is in powder form.

12. The system according to any of the preceding claims, wherein, The catalyst synthesis module is configured to mix the one or more base materials with one or more solvents to form a liquid catalyst precursor.

13. The system according to any of the preceding claims, wherein, The catalyst synthesis unit is configured to deposit the liquid catalyst precursor onto a conductive substrate to form a test sample.

14. The system of claim 13, wherein, The deposition of the liquid catalyst precursor is carried out by spraying, scraping, electrochemical deposition, dip coating, chemical bath, spray pyrolysis, drop casting, sol-gel, chemical vapor deposition and / or spin coating.

15. The system according to any of the preceding claims, wherein, The catalyst synthesis module is configured to transfer the chemical composition to the catalyst analysis module.

16. The system according to any of the preceding claims, wherein, The catalyst synthesis module is configured to operate autonomously under the control of the control unit.

17. The system according to any of the preceding claims, wherein, The catalyst analysis module is configured to electrolyze a test sample containing a synthesized chemical composition deposited on a conductive substrate.

18. The system according to any of the preceding claims, wherein, The catalyst analysis module is configured to: electrolyze multiple test samples containing the same chemical composition in parallel under their respective different test conditions; or electrolyze multiple test samples containing different chemical compositions in parallel, optionally under their respective different test conditions.

19. The system according to any of the preceding claims, wherein, The test conditions include one or more of the following: electrolytic cell components, electrolyte composition, electrolyte concentration, electrolyte flow rate, gas concentration in the electrolyte, gas flow rate, gas concentration, applied voltage, applied current, electrolyte pH value, input gas humidity, output gas humidity, pressure, and temperature.

20. The system according to any of the preceding claims, wherein, The catalyst analysis module is further configured to analyze additional properties of the synthesized chemical compositions, base materials, and / or intermediate chemical compositions.

21. The system according to any of the preceding claims, wherein, The catalyst analysis module is configured to measure the product composition and / or concentration during and / or after electrolysis.

22. The system according to any of the preceding claims, wherein, The catalyst analysis module includes a gas chromatography-mass spectrometry (GC-MS) instrument to measure product composition and / or concentration.

23. The system according to any of the preceding claims, wherein, The catalyst analysis module includes an inductively coupled plasma spectrometer to measure the catalyst concentration in the electrolyte before, during, and / or after the experiment.

24. The system according to any of the preceding claims, wherein, The catalyst analysis module is configured to operate autonomously under the control of the control unit.

25. The system according to any of the preceding claims, wherein: The catalyst synthesis module includes multiple synthesis workstations, each configured to perform one or more processing steps to synthesize the chemical composition. At least a subset of the multiple synthesis workstations performs optional and / or alternative processing steps. The control module is configured to control which optional and / or alternative synthesis workstations are used to synthesize each chemical composition.

26. The system according to claim 25, wherein, The control unit is further configured to control the sequence of synthesizing the chemical composition using a synthesis workstation.

27. The system according to any of the preceding claims, wherein: The catalyst analysis module includes multiple analysis workstations, each configured to perform one or more analytical steps to analyze the chemical composition, and at least a subset of the multiple analysis workstations performing optional and / or alternative analytical steps. The control module is configured to control which optional and / or alternative analysis workstations are used to analyze each chemical composition.

28. The system according to claim 27, wherein, The control unit is further configured to control the sequence of analysis of the chemical composition using an analysis workstation.

29. The system according to claim 28, optionally when subordinate to claim 26, wherein, The control unit is configured to determine a workflow, which includes a series of processing and / or analytical steps performed for each chemical composition by one or more synthesis and / or analysis workstations.

30. A system for determining the catalytic properties of a chemical composition, the system comprising: A modular catalyst synthesis module is used to synthesize a chemical composition from one or more of a plurality of preset base materials by treating the base materials under one or more of a plurality of processing conditions. The modular catalyst synthesis module includes a plurality of synthesis workstations, each of which is configured to perform one or more processing steps to synthesize the chemical composition, and at least a subset of the plurality of synthesis workstations performs optional and / or alternative processing steps. The catalyst analysis module is used to analyze the catalytic properties of the synthesized chemical composition under one or more test conditions and output the analysis results. A control module for controlling the catalyst synthesis module, wherein the control module is configured to determine the basic material composition and processing conditions for synthesizing the chemical composition, and to determine a workflow comprising a series of processing steps performed by one or more of the synthesis workstations, the workflow defining which of the optional and / or alternative synthesis workstations are used to synthesize the chemical composition, and / or the order in which the synthesis workstations are used to synthesize the chemical composition.

31. The system according to claim 30, wherein, The control module is configured to determine the workflow for synthesizing the next chemical composition based on the output analysis results of the catalyst testing module for the previous chemical composition.

32. The system according to claim 30 or 31, wherein, The catalyst analysis module is modular and configured to analyze the properties of synthesized chemical compositions, base materials, and / or intermediate chemical compositions. The modular catalyst analysis module includes multiple analysis workstations, each configured to perform one or more analytical steps. At least a subset of the multiple analysis workstations performs optional and / or alternative analytical steps. The workflow, determined by the control module, further defines which optional and / or alternative analysis workstations are used to analyze the properties of the synthesized chemical compositions, base materials, and / or intermediate chemical compositions, and / or the order in which the analysis workstations are used to analyze the properties of the synthesized chemical compositions, base materials, and / or intermediate chemical compositions.

33. The system according to any one of claims 30 to 32, wherein, The system is configured to execute multiple workflows in parallel to synthesize and analyze a variety of different chemical compositions.

34. The system according to any of the preceding claims, wherein, The chemical composition contains inorganic nanoparticles.

35. The system according to any one of claims 25 to 34, comprising a transfer module for transferring the base material, the chemical composition, and / or intermediate chemical composition between workstations.

36. A method for autonomously determining the catalytic properties of a chemical composition, the method comprising: A self-synthesized chemical composition, wherein the chemical composition is synthesized from one or more of a variety of pre-defined base materials and by treating the base materials under one or more of a variety of processing conditions; The system can independently analyze the catalytic properties of synthesized chemical compositions under one or more test conditions and output the analytical results. The synthesis is controlled using a control module configured to determine the basic material composition, processing conditions, and / or testing conditions of the next chemical composition to be synthesized, based on the analytical results output by the catalyst testing module for the previous chemical composition.

37. A method for determining the catalytic properties of a chemical composition, the system comprising, and the method comprising: A modular catalyst synthesis module is used to autonomously synthesize chemical compositions. The modular catalyst synthesis module is configured to synthesize chemical compositions from one or more of a plurality of preset base materials and by treating the base materials under one or more of a plurality of processing conditions. The modular catalyst synthesis module includes a plurality of synthesis workstations, each of which is configured to perform one or more processing steps to synthesize the chemical composition. At least a subset of the plurality of synthesis workstations performs optional and / or alternative processing steps. The catalyst analysis module is used to autonomously analyze the catalytic properties of the synthesized chemical composition. The catalyst analysis module is configured to analyze the catalytic properties of the synthesized chemical composition under one or more test conditions and output the analysis results. The synthesis is controlled using a control module configured to control the catalyst synthesis module, wherein the control module is configured to determine the basic material composition and processing conditions for synthesizing the chemical composition, and to determine a workflow comprising a series of processing steps performed by one or more of the synthesis workstations, the workflow defining which optional and / or alternative synthesis workstations are used to synthesize the chemical composition, and / or the order in which the synthesis workstations are used to synthesize the chemical composition.

38. A method for identifying a chemical composition having a target catalytic property, comprising the method according to claim 36 or 37.

Images