Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

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.