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Wu et al. J. Mater. Inf. 2025, 5, 15 Journal of
DOI: 10.20517/jmi.2024.67
Materials Informatics

Review Open Access

Interpretable physics-informed machine learning
approaches to accelerate electrocatalyst

development

1
3
2
1
1
Hao Wu , Mingxuan Chen , Hao Cheng , Tong Yang , Minggang Zeng , Ming Yang 1,4,5,*
1
Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China.
2
School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, UK.
3
Institute of High-Performance Computing, A*STAR, Singapore 138632, Singapore.
4
Research Centre on Data Sciences & Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong 999077, China.
5
Research Centre for Nanoscience and Nanotechnology, The Hong Kong Polytechnic University, Hong Kong 999077, China.
* Correspondence to: Dr. Ming Yang, Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Hong
Kong 999077, China. E-mail: kevin.m.yang@polyu.edu.hk
How to cite this article: Wu, H.; Chen, M.; Cheng, H.; Yang, T.; Zeng, M.; Yang, M. Interpretable physics-informed machine
learning approaches to accelerate electrocatalyst development. J. Mater. Inf. 2025, 5, 15. https://dx.doi.org/10.20517/jmi.
2024. 67
Received: 30 Oct 2024 First Decision: 2 Dec 2024 Revised: 24 Dec 2024 Accepted: 2 Jan 2025 Published: 26 Feb 2025
Academic Editors: Hao Li, Fengyu Li Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Identifying exceptional electrocatalysts from the vast materials space remains a formidable challenge. Machine
learning (ML) has emerged as a powerful tool to address this challenge, offering high efficiency while maintaining
good accuracy in predictions. From this perspective, we provide a brief overview of recent advancements in ML for
electrocatalyst discoveries. We emphasize the applications of physics-informed ML (PIML) models and
explainable artificial intelligence (XAI) to electrocatalyst development, through which valuable physical and
chemical insights can be distilled. Additionally, we delve into the challenges faced by PIML approaches, explore
future directions, and discuss potential breakthroughs that could revolutionize the field of electrocatalyst
development.
Keywords: Electrocatalysts, machine learning, physics-informed machine learning, explainable artificial intelligence

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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