Citation: Hao Wu, Fengqi Li, Xinwei Shi, Haifeng Bian, Qing Zhou, Shunshun Jia, Yujie Ma, Jian Gu, Jingzi Zhang, Shuijian He, Xiangkang Meng. Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction[J]. Acta Physico-Chimica Sinica, ;2026, 42(8): 100227. doi: 10.1016/j.actphy.2025.100227 shu

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

Hao Wu ^1,2 ,
Fengqi Li ¹ ,
Xinwei Shi ¹ ,
Haifeng Bian ¹ ,
Qing Zhou ¹ ,
Shunshun Jia ¹ ,
Yujie Ma ¹ ,
Jian Gu ¹ ,
Jingzi Zhang ^3,, ,
Shuijian He ^2,, ,
Xiangkang Meng ^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

Corresponding author: Jingzi Zhang, Shuijian He, Xiangkang Meng,
Received Date: 14 October 2025
Revised Date: 3 December 2025
Accepted Date: 4 December 2025

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 NbZnCo₂multi-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.
- Multi-principal element alloys,
- Hydrogen evolution reaction,
- Machine learning,
- Density functional theory
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