Acta Phys. -Chim. Sin. ›› 2026, Vol. 42 ›› Issue (8): 100227.doi: 10.1016/j.actphy.2025.100227

• ARTICLE • Previous Articles     Next Articles

Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction

Hao Wu1,2, Fengqi Li1, Xinwei Shi1, Haifeng Bian1, Qing Zhou1, Shunshun Jia1, Yujie Ma1, Jian Gu1, Jingzi Zhang3,*(), Shuijian He2,*(), Xiangkang Meng1,*()   

  1. 1 National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures, College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, Jiangsu Province, China
    2 Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu Province, China
    3 School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong Province, China
  • Received:2025-10-14 Revised:2025-12-03 Accepted:2025-12-04 Published:2026-06-11
  • Contact: Email: zjzhang@hit.edu.cn (Jingzi Zhang)shuijianhe@njfu.edu.cn (Shuijian He)mengxk@nju.edu.cn (Xiangkang Meng)

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Abstract:

Owing to synergistic interactions among their components, multi-principal element alloys manifest remarkable physicochemical properties that render them highly promising candidates for hydrogen evolution reaction (HER) electrocatalysts. Despite extensive experimental investigations, the intricate composition of multi-principal components and the absence of systematic machine learning (ML) screening poses significant challenges in identifying optimal elemental configurations for electrocatalysts, thereby constraining the rational design and development of multi-principal alloy electrocatalysts. In this work, the NbZnCo2 multi-principal component alloy emerges as the optimal candidate from a pool of 601 candidate alloys using the Light Gradient Boosting model, demonstrating approximately 34-fold cost efficiency enhancement over Pt/C while surpassing HER activity. Combined density functional theory (DFT) calculations and experimental validation confirmed the ML model's reliability, with the micrometer NbZnCo2 catalyst achieving an ultralow overpotential of 20 mV at 10 mA cm−2 and remarkable stability over a period of 60 h. Furthermore, the NbZnCo2 nanoparticle retained exceptional HER properties, validating the universality of NbZnCo2 element composition. Our work establishes a synergistic "ML-DFT-Experiment" framework for the precise design of high-performance HER electrocatalysis. This methodology exhibits extensibility to diverse other electrocatalytic processes, thereby broadening the applicability in sustainable energy conversion technologies.

Key words: Multi-principal element alloys, Hydrogen evolution reaction, Machine learning, Density functional theory