In brief

With cleaner reaction data, improved data structure and added physics-based guidance, a new ML framework could uncover better catalysts for removing toxic CO from hydrogen fuel systems.

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The catalyst cookbook

6 Dec 2022

A purpose-built machine learning platform takes the guesswork out of catalyst formulations for green energy generation.

Catalyst chemistry is a lot like cooking in a fine dining kitchen. Just as chefs spend hours adjusting recipes for palate perfection, chemists likewise spend time painstakingly tweaking catalysts (chemical reaction boosters) for optimal results.

“Figuring out an optimised catalyst is a formidable task when there’s a vast space of possibilities to explore,” said Joyjit Chattoraj, a Research Scientist from A*STAR’s Institute of High Performance Computing (IHPC). “You need to play around with a wide array of parameters, ranging from preparation methods and operating conditions to the presence and concentration of base and promoter metals.”

The good news is that catalyst chemists now have a secret weapon in their kitchens: machine learning, or ML. Through ML, computational models can help automate the trial-and-error nature of catalyst optimisation, which Chattoraj said speeds things up tremendously.

However, existing ML models are only as good as the data they’re trained with. Just as even a good kitchen appliance with bad ingredients makes poor dishes, a good ML model with bad data makes bad predictions. For example, without incorporating a basic knowledge of physics, some of today’s purely data-driven models sometimes start bending the laws of thermodynamics by predicting catalytic conversion efficiencies over 100 percent.

To figure out a model with more realistic predictions, Chattoraj and Teck Leong Tan led a team of A*STAR scientists from IHPC, in collaboration with the Institute of Sustainability for Chemical, Energy and Environment (ISCE2) who aim to build better ML frameworks for hydrogen fuel technology, a greener energy alternative to fossil fuels. They focused on ML-driven catalyst optimisation for the water-gas shift (WGS) reaction, a key process that eliminates toxic carbon monoxide (CO) produced from hydrogen fuel cells.

They started with a cleanup of an open-source WGS dataset used in previous models to ensure all data points in it adhered to chemical reaction principles. Next, they added physical and chemical data tags (known as ‘fingerprints’) to the dataset to facilitate new catalyst discovery. They leveraged this spruced-up data to develop a new-and-improved physics-guided ML framework more likely to obey the laws of thermodynamics.

“Our state-of-the-art model can establish complex relationships between catalyst synthesis parameters through forward modelling: a training process more accurate and reliable than its conventional counterparts in predicting CO conversion,” said Chattoraj. “This paves the way for reliable inverse modelling, allowing us to search for potential catalyst candidates based on optimal compositions and reaction conditions.”

With this ML-powered catalyst cookbook under their belts, the researchers are now working on applying their model to discover next-generation WGS catalysts that stay effective even at low reaction temperatures, which could benefit many industries with stakes in fuel cell technology.

Again, getting the ingredients right is part of the challenge; the current training dataset for their model has limited experimental data on low-temperature reactions, said Chattoraj. “Overcoming this would require more data accumulation and better understandings of how catalyst properties affect CO conversion, which will take knowledge-sharing efforts among different research domains.”

The A*STAR-affiliated researchers contributing to this research are from the Institute of High Performance Computing (IHPC) and the Institute of Sustainability for Chemical, Energy and Environment (ISCE2).

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Chattoraj, J., Hamadicharef, B., Kong, J.F., Pargi, M.K., Zeng, Y., et al. Theory-Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water-Gas Shift Reaction. ChemCatChem 14 (16), e202200355 (2022).│article

About the Researchers

Joyjit Chattoraj is a research scientist from the Computing & Intelligence Department (CI) of ASTAR's Institute of High Performance Computing (IHPC). He received his PhD from Université Paris-Est France. His research interests include machine learning, theory and atomistic simulations for material design. In CI, he is driving one key research area: physics-guided artificial intelligence for inverse modelling. He is one of the recipients of the A*STAR Career Development Fund 2022.
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Luwei Chen

Senior Scientist and Deputy Director (Catalysis and Reaction Engineering)

Institute of Sustainability for Chemical, Energy and Environment (ISCE2)
Luwei Chen received her PhD from National University of Singapore. She is a senior scientist and Deputy Director of Catalysis and Reaction Engineering (CRE) division at the Institute of Sustainability for Chemical, Energy and Environment (ISCE2). She is also an Adjunct Associate Professor in the Materials Science and Engineering Department at the National University of Singapore (NUS). Chen’s research interest is in the development of catalysts/materials for renewable/alternative energy, biomass valorisation, and carbon dioxide utilisation.
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Teck Leong Tan

Senior Scientist and Department Director (Materials Science & Chemistry)

Institute of High Performance Computing (IHPC)
Teck Leong Tan is the Director of the Materials Science and Chemistry (MSC) Department at IHPC, where he leads a multidisciplinary team of computational materials scientists, chemists and physicists to accelerate materials discovery and development in a wide range of materials systems including alloys, catalysts, polymers and electronic materials. Concurrently, he is also the Director of Graduate Affairs at SERC and an Adjunct Associate Professor in the Materials Science and Engineering Department at the National University of Singapore (NUS). His research focus is in the area of alloy materials design with the aim of accelerating materials development in the areas of aerospace, nanoscale technology, catalysis, electronics, corrosion science and sustainability.

This article was made for A*STAR Research by Wildtype Media Group