Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts
Min Wang, Hongwei Zhu
Abstract
As a pivotal technique of data mining and data analysis, machine learning (ML) is gradually revolutionizing how materials are discovered with the rise of big data. Catalysis informatics are rising from computational research in catalysis to liberate researchers from heavy trial-and-error investigations. However, ML remains at its early stage in hydrogen evolution reaction (HER) electrocatalysis with numerous underexplored opportunities. This Perspective focuses on the recent progress in ML-assisted identification of high-performance transition-metal (TM)-based HER electrocatalysts to arouse broader research ideas. The representative studies about feature engineering (descriptors) in ML and the potential applications of image identification with deep learning for TM-based HER electrocatalysts are highlighted. In addition to serving as a powerful means for discovery of electrocatalysts, these data-driven techniques also establish a deeper understanding for the relationships between intrinsic properties of TM-based materials and their electrocatalytic performance. Future perspectives on the development of ML-guided TM-based HER electrocatalysts are provided to give heuristics for further investigations in this field.