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Page 2 of 15 Wu et al. J. Mater. Inf. 2025, 5, 15 https://dx.doi.org/10.20517/jmi.2024.67

INTRODUCTION
Excessive fossil fuel combustion since the Industrial Revolution has led to severe environmental problems
such as global warming, extreme weather, and environmental pollution. In response, there is a concerted
effort to develop green energy solutions to reduce our dependence on fossil fuels. Electrochemical reactions,
including but not limited to hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen
reduction reaction (ORR), carbon dioxide reduction reaction (CO RR), and nitrogen reduction reaction
2
(NRR), hold significant promise for storing and utilizing essential intermittent renewable energies in the
[1,2]
near future . Electrocatalysts are essential in increasing the reaction rate of the electrochemical reactions,
thereby improving the conversion and utilization efficiency of renewable energies. However, the commonly
used electrocatalysts are usually based on precious metals such as platinum, which are both expensive and
scarce. In addition, the existing electrocatalysts also suffer from issues such as limited efficiency, durability,
and scalability . Therefore, discovering electrocatalysts that can solve the above issues is fundamental to the
[3]
development of high-performance electrochemical energy conversion/storage devices, including fuel cells,
[4]
batteries, and supercapacitors .

Over the past few decades, electrocatalyst discovery has been greatly accelerated by the rapid advancement
of theoretical approaches and experimental capabilities . For instance, density-functional theory (DFT)
[5]
calculations have been widely used to predict various critical properties of electrocatalysts, such as
formation energies, adsorption energies and d-band centers . Based on thermodynamic and kinetic
[6]
calculations, the free energy diagrams and micro-kinetic models can be constructed, which are essential to
identifying catalysts with improved activity and selectivity . Furthermore, high-throughput calculations
[7,8]
have been applied to navigate vast materials and configurations space [9-12] . Semi-automated experiments
have also been developed to accelerate the development of electrocatalysts. Although high-throughput
computational and experimental methods can significantly reduce the development time and cost compared
with the traditional trial-and-error approach, the vastness of the search space remains a grand challenge for
these techniques to explore efficiently. In this regard, machine learning (ML) has emerged as an
indispensable tool in the efficient discovery of electrocatalyst [13-16] . By leveraging large computational or
experimental datasets amassed from the high-throughput methods, ML has been used to predict the key
properties of electrocatalysts [17-22] [Figure 1]. Significant progress has been made in this area, with ML
models achieving remarkable results. However, most of these models require large and complex neural
networks, which come with high computational costs and a lack of physical and chemical interpretability.
As a result, substantial efforts have been devoted to incorporating physical or chemical insights into ML
models, which has led to the physics-informed ML (PIML) models and explainable artificial intelligence
(XAI) methods [Figure 1]. These approaches not only aim to improve the accuracy and efficiency of
predictions, but also provide valuable interpretability, making it easier to understand the results generated
by ML models and eventually leading to effective and efficient design and development of functional
materials. This area has seen significant advancements in recent years, highlighting the need for a
comprehensive review to summarize the state-of-the-art findings.

In this review, we summarize the recent advancement in ML for electrocatalysts, with a particular focus on
the PIML models. We begin by discussing the progress made in the application of ML to electrocatalysts,
providing an overview of fundamental concepts in this field. Next, we emphasize the necessity and
advantages of integrating PIML models, highlighting their recent applications and significant contributions
to the development of electrocatalysts. Finally, we conclude with a brief discussion of the challenges in the
field, along with perspectives on future directions and potential breakthroughs in PIML for electrocatalysts.