Artificial intelligence-driven catalyst design for electrocatalytic hydrogen production: Paradigm innovation and challenges in material discovery

Abstract

The global overreliance on fossil fuels has precipitated dual crises of energy scarcity and ecological degradation. Therefore, the development of green and sustainable new energy to achieve carbon neutrality and energy structure innovation is an urgent task. Electrocatalytic hydrogen production technology is a transformative solution that uses water as a feedstock and renewable electricity as a carbon-free energy source to produce hydrogen with zero-carbon emissions. Achieving highly efficient hydrogen production via electrocatalysis critically depends on the performance of the catalytic materials employed. However, the traditional “trial-and-error” approach for catalyst development remains expensive and time-consuming. Furthermore, the material systems involved in current research are becoming increasingly complex, with a growing number of synthetic parameters requiring optimization, which makes the development of high-performance catalysts even more challenging. Machine learning (ML) presents significant opportunities for the rational discovery of electrocatalysts by accurately predicting and screening their catalytic performance through the analysis of extensive datasets. This review systematically examines the state-of-the-art progress in electrocatalytic hydrogen production technology. Furthermore, we highlight how cutting-edge research in this field integrates ML and other artificial intelligence (AI) technologies, alongside the profound impact and potential challenges of increasingly sophisticated automated experimental tools in accelerating the development of catalysts. This work aims to inspire paradigm-shifting research methodologies in electrocatalysis and promote the more efficient and sustainable development of hydrogen production technology.

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