Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/ photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/ photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Automated summary

This article provides a comprehensive introduction and critical review of machine learning techniques used in electrocatalysis and photocatalysis research. It discusses the sources of electro/photocatalyst data, current approaches to representing these materials mathematically, and the most commonly used machine learning methods. The article also evaluates the quality and utility of electro/photocatalyst models and provides illustrations of how machine learning models are applied in novel electro/photocatalyst discovery and to elucidate reaction mechanisms. It serves as a guide for materials scientists in selecting machine learning methods for electrocatalysis and photocatalysis research.