System and method

Abstract

A catalyst synthesis module configured to synthesise a chemical composition, from one or more of a plurality of predefined base materials and by processing the base materials under one or more of a pl

Description

TECHNICAL FIELD The present disclosure relates to systems and methods for determining catalytic properties of chemical compositions, determining processing conditions for forming chemical compositions having target catalytic properties, identifying chemical compositions having target catalytic properties and / or manufacturing chemical compositions having target catalytic properties. In various examples, the properties relate to hydrogen production, carbon dioxide conversion or ammonia synthesis, and respective reverse processes. BACKGROUND ART There is an increasing need for more effective materials for use in applications including photovoltaics, batteries, super capacitors, and electrocatalysts, to name a few. Of particular intertest are electrocatalysts for the production of hydrogen or conversion of carbon dioxide. Investigation of catalyst materials using trial and error methods can be slow and costly and often results in biased and low-quality data. On the other hand, using first principle theoretical calculation methods for modelling catalyst structure and performance, without supporting these models with experimental measurements has not yielded the desired discovery of new catalytic candidates. These methods often necessitate an intricate understanding of catalytic properties prior to predicting novel properties, and the knowledge of the electrocatalytic performance and behaviour on the atomic scale is still absent. More recently, the pursuit of potential catalysts through high-throughput experimentation, guided by expert analysis, has similarly failed to produce substantial outcomes in identifying innovative catalysts. These experiments tend to be skewed by limited perceptions of catalytic performance, fixating on specific candidate groups while neglecting unexplored groups within the search domain that might have superior potential. It an object of the present disclosure to at least partially address some of the above problems. SUMMARY OF THE INVENTION According to an aspect of the disclosure there is provided a system for determining catalytic properties of chemical compositions, the system comprising: a catalyst synthesis module configured to synthesise a chemical composition, the catalyst synthesis module being configured to synthesise the chemical composition from one or more of a plurality of predefined base materials and by processing the base materials under one or more of a plurality of processing conditions; a catalyst analysis module configured to analyse the catalytic properties of the synthesised chemical composition under one or more of a plurality of testing conditions and output the analysis results; a control module configured to control the catalyst synthesis module, wherein the control module is configured to determine the base material composition, and processing conditions and / or testing conditions, for a next chemical composition to be synthesised based on the output analysis results from the catalyst testing module for a previous chemical composition. Optionally, the control module is configured to determine the base materials, and processing conditions and / or testing conditions, for the next chemical composition to be synthesised, based on base materials and processing conditions and / or testing conditions, predicted to improve catalytic properties compared to the previous chemical composition, with reference to predetermined target catalytic properties. Optionally, the control module is configured to execute a machine learning model configured to predict catalytic properties for a base material composition, and processing conditions and / or testing conditions, in order to determine the base materials and processing conditions and / or testing conditions for the next chemical composition. Optionally, the base materials, and processing conditions and / or testing conditions for the next chemical composition are determined based on an acquisition function applied to the output of the machine learning model. Optionally, the machine learning model is trained to output one or more objective values corresponding to catalytic properties based on training data comprising base material composition, full or a subset of processing conditions for synthesis of the catalyst and / or testing conditions, associated catalytic properties derived from experimental measurement. Optionally, the machine learning model is refined based on the analysis results through active learning in the process space comprising material composition and processing conditions and / or testing conditions. Optionally, the catalytic properties comprise one or more of: reaction product concentration, optionally corresponding to a target current and / or target voltage, reaction product selectivity, reactant conversion percentage and catalyst stability. Optionally, the catalytic properties are electrocatalytic and / or photocatalytic properties for Hydrogen production or oxidation, Carbon Dioxide conversion or reduction, and / or Ammonia synthesis or cracking. Optionally, the processing conditions comprise one or more of: the different processing steps performed, the order in which different processing steps are performed, processing temperature in each of the relevant processing steps, processing pH temperature in each of the relevant processing steps, processing time in each of the processing steps, liquid flow rate of liquid addition to the sample, the age of chemical components used in the synthesis, the deposition technique used, flow rate of deposition, deposition time, deposition cycle, deposition spin rate, and temperature applied during deposition. Optionally, the base materials comprise a plurality of monometallic or multi-metallic materials. Optionally, the base materials are in powder form. 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. Optionally, the catalyst synthesis unit is configured to deposit the liquid catalyst precursor onto a conductive substrate to form a test sample. Optionally, the deposition of the liquid catalyst precursor is performed by spray coating, doctor blading, electrochemical depositions, dip coating, chemical bathing and / or spin coating. Optionally, catalyst synthesis module is configured to transfer the chemical composition to the catalyst analysis module. Optionally, the catalyst synthesis module is configured to operate autonomously under the control of the control unit. Optionally, the catalyst analysis module is configured to perform electrolysis using a test sample comprising the synthesised chemical composition deposited on a conductive substrate. Optionally, the catalyst analysis module is configured to perform electrolysis on a plurality of test samples comprising the same chemical compositions, in parallel, under respective different testing conditions. Optionally, the testing conditions comprise one or more of: electrolyser components, electrolyte composition, gas concentration in the electrolyte, applied voltage, applied current, electrolyte pH, humidity of gas input, pressure, and temperature. Optionally, the catalyst analysis module is configured to measure product composition and / or concentration during and / or after the electrolysis is performed. Optionally, the catalyst analysis module comprises a gas chromatograph-mass spectrometer to measure product composition and / or concentration. Optionally, the catalyst analysis module comprises an inductive coupled plasma spectroscopy to measure the catalyst concentration in the electrolyte before, during and / or after the experiment. Optionally, the catalyst analysis module is configured to operate autonomously under the control of the control unit. According to a second aspect of the disclosure, there is provided a method of autonomously determining catalytic properties of chemical compositions, the method comprising: autonomously synthesising a chemical composition, the chemical composition being synthesised from one or more of a plurality of predefined base materials and by processing the base materials under one or more of a plurality of processing conditions; autonomously analysing the catalytic properties of the synthesised chemical composition under one or more of a plurality of testing conditions and outputting the analysis results; controlling the synthesising using a control module 31 configured to determine the base material composition, and processing conditions and / or testing conditions, for a next chemical composition to be synthesised based on the output analysis results from the catalyst testing module for a previous chemical composition. According to a third aspect of the disclosure, there is provided a method of identifying a chemical composition having target catalytic properties, comprising the method of the second aspect. According to a fourth aspect of the disclosure, there is provided a method of manufacturing a chemical composition having target catalytic properties, comprising the method of the second aspect. According to a fifth aspect of the disclosure, there is provided a method of determining processing conditions for forming a chemical composition having target catalytic properties, comprising the method of the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS Further features of the disclosure will be described below, by way of non-limiting examples and with reference to the accompanying drawings, in which: Fig. I schematically shows an example system according to the disclosure; Fig. 2 is a flow chart showing example process according to the disclosure. DETAILED DESCRIPTION Fig. 1 schematically shows an example system 1 according to the disclosure, for determining catalytic properties of chemical compositions. As shown, the system comprises an experimental subsystem 2 and a computer subsystem 3. The experimental subsystem 2 comprises a catalyst synthesis module 21 and a catalyst analysis module 23. The computer subsystem 1 comprises a control module 3131. The catalyst synthesis module 21 is configured to synthesise a chemical composition. The catalyst synthesis module 21 may be configured to synthesise the chemical composition from one or more of a plurality of predefined base materials. The catalyst synthesis module 21 may be configured to synthesise the chemical composition by processing the base materials under one or more of a plurality of processing conditions. The 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 comprise a plurality of monometallic or multi-metallic materials. The base materials may be formed from a metallic element, or combination or metallic elements, including transition metals and non-transition metals. The base materials may be in powder form, for example. The solvents may include one or more of: aqueous solvents e.g. water, hydrochloric acid, sulphuric acid, sodium / potassium hydroxide, sodium / potassium carbonate, organic solvents e.g. ethanol, acetone, isopropyl alcohol, methanol. The catalyst synthesis module 21 may comprise a solid handling unit 23 configured to extract samples of the base materials, measure the amount of the samples (e.g. by weight or volume) and load them into a sample container. The solid handling unit 23 may comprise one or more base material containers containing the base materials to be extracted. The solid handling unit 23 may comprise one or more robotic arms for manipulating the samples. The solid handling unit 23 may comprise one or more weighing balances for weighing the samples. The solid handling unit 23 may comprise a mixing unit for mixing two or more samples of different base materials together. The solid handling unit 23 may comprise one or more dispensing units for dispensing samples from the base material containers, and / or extracting units for extracting samples from the base material containers. The dispensing units and / or extracting units may comprise one or more pumps, for example. The dispensing units may be attached to base material containers, for example. The extracting units may be attached to one or more of the robotic arms, for example. The catalyst synthesis module 21 may comprise a liquid handling unit 24 configured to add liquids to the sample, mix the sample and heat and / or cool the sample to form the liquid catalyst precursor. The liquid handling unit 24 may be configured to add liquid solvents and, optionally, further liquids, such as chemical buffers, acids, bases, reducing agents to the sample. The liquid handling unit 24 may comprise one or more liquid containers containing the liquids. The liquid handling unit 24 may comprise one or more robotic arms for manipulating the samples. The liquid handling unit 24 may comprise a heating device (e.g. one or multiple thermocouple temperature devices) for heating and / or cooling the samples. The liquid handling unit 24 may comprise a stirring device for stirring the samples, such as a magnetic stirrer or a shaker. The liquid handling unit 24 may comprise one or more dispensing units for dispensing liquids from the liquid containers, and / or extracting units for extracting liquids from the liquid containers. The dispensing units and / or extracting units may comprise one or more pumps, for example. The dispensing units may be attached to the liquid containers, for example. The extracting units may also extract the catalyst in solid form, (known as solute) with the means of vacuum or gravitational filtration process. The extracting units may be attached to one or more of the robotic arms, for example. The catalyst synthesis module 21 may be further configured to deposit the liquid catalyst precursor onto a conductive substrate to form a test sample. The conductive substrate may be a gas diffusion electrode or an anionic-cationic bipolar membrane, for example. The conductive substrate may be formed from an electrode material comprising one or more of indium-doped tin oxide, fluoride doped-tin oxide, glassy carbon, nickel, gold and silver. The liquid catalyst precursor may be deposited as a thin film. The catalyst synthesis module 21 may comprise a deposition unit 25 configured to deposit the liquid catalyst precursor onto a conductive substrate to form a test sample. The deposition of the liquid catalyst precursor may be performed by spray coating, doctor blading and / or spin coating. Accordingly, the deposition unit 25 may comprise spray coating, doctor blading and / or spin coating equipment. The deposition unit 25 may comprise one or more robotic arms for manipulating the substrate. The deposition unit 25 may comprise one or more robotic arms for manipulating the liquid catalyst precursor. The catalyst synthesis module 21 may be configured to process the base materials under one or more predefined processing conditions. The processing conditions may relate to synthesis of the catalyst precursor or formation of the test sample from the catalyst precursor. For example, processing conditions relating to the synthesis of the catalyst precursor may comprise one or more of: the different processing steps performed, the order in which different processing steps are performed, processing temperature in each of the relevant processing steps, processing pH, temperature, atmosphere in each of the relevant processing steps, processing time in each of the processing steps, liquid flow rate of liquid addition to the sample, and the age of chemical components used in the synthesis. For example, the processing conditions relating to the formation of the test sample from the catalyst precursor may comprise on or more of: the deposition technique used, flow rate of deposition, deposition time, deposition cycle, deposition spin rate, deposition height, position (e.g. cartesian coordinate) of the deposition and temperature applied during deposition. The catalyst synthesis module 21 may be configured to transfer the chemical composition, now part of the test sample, to the catalyst analysis module 22. This may be performed by one or more robotic arms, for example. The catalyst analysis module 22 may be configured to perform electrolysis using the test sample comprising the synthesised chemical composition deposited on the conductive substrate and to analyse the catalytic properties of the synthesised chemical composition under one or more of a plurality of testing conditions and output the analysis results. For example, the catalyst synthesis module 22 may be configured to measure product composition and / or concentration. These may be measured before, during and / or after the electrolysis is performed. The catalyst analysis module 22 may be configured to perform one or more of electrocatalytic Hydrogen production, Carbon Dioxide conversion, and photovoltaic conversion. Accordingly, the catalytic properties may correspond to one or more of these catalytic processes. The catalyst analysis module 22 may comprise an electrolyser unit 26 to perform the electrolysis. The electrolyser unit 26 may be configured to receive the test sample and form an electrolyser using the test sample, e.g. as an electrode within the electrolyser. The electrolyser may comprise electrodes and an electrolyte. The electrolyser unit 26 may comprise a potentiostat for controlling electrode voltage. The electrolyser unit 26 may further comprise a temperature control means, e.g. comprising a heater, for controlling the temperature of the electrolyser. The catalyst analysis module 22 may be configured to perform electrolysis on a plurality of test samples in parallel. The plurality of test samples may comprise the same chemical compositions, under respective different testing conditions. Alternatively, or additionally, the plurality of test samples may comprise respectively different chemical compositions, under the same testing conditions. The electrolyser unit 26 may be configured to control the testing conditions for each test sample. For example, the testing conditions may comprise one or more of electrolyser components, electrolyser stack, electrolyte composition, electrolyte concentration, flow rate of electrolyte, gas concentration in the electrolyte, flow rate of gas, concentration of gas, applied voltage, applied current (e.g. of either direct or alternating current), electrolyte pH, humidity of gas input, pressure, and temperature. The catalyst analysis module 22 may further comprise a measurement unit 27 for measuring catalytic performance. The measurement unit 27 may comprise a gas chromatograph-mass spectrometer to measure product composition and / or concentration, and / or an inductively coupled plasma to measure the catalyst decomposition, for example. The measurements obtained by the catalyst analysis module 22 may be provided to the control module 31 for further analysis. Catalytic properties may be determined based on the obtained measurement. The catalytic properties may comprise one or more of: reaction product concentration, optionally corresponding to a target current and / or target voltage, reaction product selectivity, reactant conversion percentage and catalyst stability. The control module 31 may 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 may be is configured to operate autonomously under the control of the control module 31. The control module 31 may comprise one or more experimental subsystem control units 33 configured to control the modules forming the experimental subsystem. The experimental subsystem control units 33 may be configured to execute one or more machine learning algorithms configured to control the modules forming the experimental subsystem based on base material compositions for the test samples, and processing conditions and / or testing conditions, determined by the control module 31. Additionally, the one or more experimental subsystem control units 33 may receive as input metadata relating to the modules forming the experimental subsystem. This metadata may allow the experimental subsystem control units 33 to perform feedback control base on the metadata. Accordingly, the metadata may comprise metadata corresponding to the processing or test conditions. With regard to physical manipulation, e.g. by robotic arms, data collected by cameras, position sensors and / or motions sensors may be used. The control module 31 may comprise a prediction unit 32 configured to determine the base material composition, and one or both of the processing conditions and the testing conditions, for a next chemical composition to be synthesised based on the output results from the catalyst testing module for a previous chemical composition. The prediction unit 32 may be configured to determine the base materials, and processing conditions and / or 10 testing conditions, for the next chemical composition to be synthesised, based on base materials and processing conditions and / or testing conditions, predicted to improve catalytic properties compared to the previous chemical composition, with reference to predetermined target catalytic properties. The prediction unit 32 may be configured to execute a machine learning model configured to predict catalytic properties for a base material composition, and processing conditions and / or testing conditions, in order to determine the base materials and processing conditions for the next chemical composition. The machine learning model may be random forest regression model, or Bayesian neural network, for example. The machined learning model may be trained to output one or more objective values corresponding to catalytic properties based on training data comprising base material composition, full or a subset of processing conditions for synthesis of the catalyst and / or testing conditions, associated catalytic properties derived from experimental measurement. The machine learning model may be periodically refined based on the results from the catalyst analysis module 22, e.g. through active learning in the process space comprising material composition and processing conditions and / or testing conditions. The initial base material composition, processing conditions and the testing conditions may be selected randomly or based on an initial prediction by the control unit. The control module 31 may be configured to iteratively change one or more of the base material compositions, the processing conditions and the testing conditions, for subsequent experiments. The system may be configured to operate in an iterative cycle of synthesis and analysis until a predefined condition is met. The predefined condition may be convergence of the catalytic properties of the chemical composition to a predefined set of catalytic properties. The base materials, and processing conditions and / or testing conditions for the next chemical composition are determined based on an acquisition function applied to the output of the machine learning model. The acquisition function may determine the exploration of the process space comprising material composition and processing conditions and / or testing conditions. Fig. 2 is a flow diagram showing an example process according to the disclosure. In Step SI, initial base material composition, processing conditions and testing conditions are selected. In step S2, a liquid catalyst precursor is synthesised from the base materials, solvents and additives by the solid handling unit 23 and liquid handling unit 24 of the synthesis module. In Step S3, the liquid catalyst precursor is deposited as a thin film on a conductive substrate by the deposition unit 25 of the synthesis module to form a test sample. In Step S4, the test sample is integrated into an electrolyser by the electrolyser unit 26 of the analysis module and electrolysis is performed. In Step S5, catalytic performance is measured by the measurement unit 27 of the analysis module. In Step S6, measurements are provided to the control module 31, together with data regarding the base material composition, processing and test conditions. In Step S7, the machine learning model is updated based on the measurements and data received. In Step S8, the control unit determines whether the catalytic properties correspond to predefined desired properties. If so, the process ends. If not, the process continues to Step S9. In Step S9, subsequent base material composition, processing conditions and testing conditions are determined by the control unit. The processes then return to Step S2. The use of machine learning enables smarter selection of experiments based on the predefined goals, e.g. target properties. This may enable less exhaustive, more economic, searching for new catalysts. The machine learning module employs a random forest regression approach to develop a predictive model. This model learns from experimental data generated during electrocatalyst synthesis and testing, identifying correlations and patterns. Using the random forest model, the machine learning module selects candidate compositions for synthesis and testing. This approach ensures a diverse and unbiased exploration of the compositional space, minimizing the risk of overlooking promising candidates. As the system progresses through successive iterations of synthesis, testing, and prediction, the machine learning module continually refines its predictive model. This iterative process enhances the accuracy of predictions, resulting in increasingly efficient candidate selection. The experimental apparatus facilitates high throughput, precise and controlled synthesis and testing of electrocatalysts. The solid handling unit 23 uses robotic arms, precise pumps, and weighing balances, to accurately loads, weighs, and mixes solid powder samples. This ensures consistent and reproducible catalyst formulations. The liquid 12 handling unit 24 is equipped with sample containers, a thermocouple heating / cooling plate, a magnetic stirrer, and liquid pumps, to accurately mix, stir, and heat / cool liquid precursor samples. This contributes to the reproducibility of catalyst synthesis. Employing techniques like spray coating, doctor blading and spin coating, uniformly deposits liquid precursors onto conductive substrates. This yields catalyst thin films with precise compositions and thickness. The electrolyser module enables concurrent testing of multiple catalysts. This parallelized setup accelerates experimentation throughput. The gas chromatograph-mass spectrometer measures product composition and concentration postelectrocatalysis, providing insights into reaction pathways and catalytic efficiency. The inductively coupled plasma spectrometer measures the concentration of the catalyst in the electrolyte which indicates the decomposition of catalyst during electrolysis experiment. The data management and analysis facilitate data acquisition, organization, and interpretation of data. Data is captured and organized during the synthesis and testing processes. This structured repository of information ensures systematic tracking and analysis of catalyst performance trends. Aided by acquired data, the machine learning algorithm conducts sophisticated analysis of catalytic performance. The machine learning algorithm compares experimental results against predefined boundary conditions and targeted catalytic properties, supporting informed decision-making for subsequent experiments. The invention's iterative process promotes continuous improvement and optimization. The iterative cycle commences with the machine learning module selecting candidate compositions for synthesis. These candidates undergo controlled synthesis, electrochemical testing, and analysis. The resulting data informs the module's predictive model, guiding the selection of subsequent candidates. With each iteration, the machine learning module's predictive model becomes increasingly accurate. The module fine-tunes its predictions based on newly acquired data, gradually narrowing down optimal electrocatalyst compositions. Accordingly, the integration of machine learning algorithms and advanced experimental techniques offers the following advantages: • Acceleration of Discovery: The system's parallelized setup, rapid synthesis, and data-driven predictions significantly expedite the electrocatalyst discovery process, enabling researchers to explore a vast compositional space efficiently. • Enhanced Exploration: Unbiased candidate selection and comprehensive analysis ensure thorough exploration of catalyst compositions, reducing the risk of overlooking high-performing materials. • High-Quality Data: The systematic data acquisition and analysis mechanisms yield high-quality, structured data, enabling robust conclusions and insights into catalyst behaviour. • Informed Decision-Making: The machine learning module's predictions empower researchers to make informed decisions about the next set of experiments, optimizing resource utilization. • Iterative Learning: The iterative nature of the system encourages continuous learning and improvement. The machine learning module refines its predictive model with each iteration, leading to increasingly accurate predictions and higher-quality catalyst discoveries.

Claims

1. A system for determining catalytic properties of chemical compositions, the system comprising:a catalyst synthesis module configured to synthesise a chemical composition, the catalyst synthesis module being configured to synthesise the chemical composition from one or more of a plurality of predefined base materials and by processing the base materials under one or more of a plurality of processing conditions;a catalyst analysis module configured to analyse the catalytic properties of the synthesised chemical composition under one or more of a plurality of testing conditions and output the analysis results;a control module configured to control the catalyst synthesis module, wherein the control module is configured to determine the base material composition, and processing conditions and / or testing conditions, for a next chemical composition to be synthesised based on the output analysis results from the catalyst testing module for a previous chemical composition.

2. The system of claim 1, wherein the control module is configured to determine the base materials, and processing conditions and / or testing conditions, for the next chemical composition to be synthesised, based on base materials and processing conditions and / or testing conditions, predicted to improve catalytic properties compared to the previous chemical composition, with reference to predetermined target catalytic properties.

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

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

5. The system of claim 3 or 4, wherein the machine learning model is trained to output one or more objective values corresponding to catalytic properties based on training data comprising base material composition, full or a subset of processing conditions for synthesis of the catalyst and / or testing conditions, associated catalytic properties derived from experimental measurement.

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

7. The system of any preceding claim, wherein the catalytic properties comprise one or more of: reaction product concentration, optionally corresponding to a target current and / or target voltage, reaction product selectivity, reactant conversion percentage and catalyst stability.

8. The system of any preceding claim, wherein the catalytic properties are electrocatalytic and / or photocatalytic properties for Hydrogen production or oxidation, Carbon Dioxide conversion or reduction, and / or Ammonia synthesis or cracking.

9. The system of any preceding claim, wherein the processing conditions comprise one or more of: the different processing steps performed, the order in which different processing steps are performed, processing temperature in each of the relevant processing steps, processing pH temperature in each of the relevant processing steps, processing time in each of the processing steps, liquid flow rate of liquid addition to the sample, the age of chemical components used in the synthesis, the deposition technique used, flow rate of deposition, deposition time, deposition cycle, deposition spin rate, and temperature applied during deposition.

10. The system of any preceding claim, wherein the base materials comprise a plurality of monometallic or multi-metallic materials.

11. The system of any preceding claim, wherein the base materials are in powder form.

12. The system of any preceding claim, 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 of any preceding claim, 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 performed by spray coating, doctor blading, electrochemical depositions, dip coating, chemical bathing and / or spin coating.

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

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

17. The system of any preceding claim, wherein the catalyst analysis module is configured to perform electrolysis using a test sample comprising the synthesised chemical composition deposited on a conductive substrate.

18. The system of any preceding claim, wherein the catalyst analysis module is configured to perform electrolysis on a plurality of test samples comprising the same chemical compositions, in parallel, under respective different testing conditions.

19. The system of any preceding claim, wherein the testing conditions comprise one or more of: electrolyser components, electrolyte composition, gas concentration in the electrolyte, applied voltage, applied current, electrolyte pH, humidity of gas input, pressure, and temperature.

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

21. The system of any preceding claim, wherein the catalyst analysis module comprises a gas chromatograph-mass spectrometer to measure product composition and / or concentration.

22. The system of any preceding claim, wherein the catalyst analysis module comprises an inductive coupled plasma spectroscopy to measure the catalyst concentration in the electrolyte before, during and / or after the experiment.

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

24. A method of autonomously determining catalytic properties of chemical compositions, the method comprising:autonomously synthesising a chemical composition, the chemical composition being synthesised from one or more of a plurality of predefined base materials and by processing the base materials under one or more of a plurality of processing conditions;autonomously analysing the catalytic properties of the synthesised chemical composition under one or more of a plurality of testing conditions and outputting the analysis results;controlling the synthesising using a control module 31 configured to determine the base material composition, and processing conditions and / or testing conditions, for a next chemical composition to be synthesised based on the output analysis results from the catalyst testing module for a previous chemical composition.

25. A method of identifying a chemical composition having target catalytic properties, comprising the method of claim 24.19

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