Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development DOI Open Access
Hao Wu, Mingxuan Chen, Hao Cheng

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(2)

Published: Feb. 26, 2025

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

Language: Английский

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development DOI Open Access
Hao Wu, Mingxuan Chen, Hao Cheng

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(2)

Published: Feb. 26, 2025

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

Language: Английский

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