Machine learning driven rational design of AuAgPdHgCu HEA catalysts for the two-electron oxygen reduction reaction.

Journal: Chemical communications (Cambridge, England)
Published Date: Aug 5, 2025

Abstract

This study integrated high-throughput DFT calculations and machine learning to screen AuAgPdHgCu high-entropy alloy catalysts, revealing that negative d-band shifts of Hg/Cu optimize Δ for an enhanced 2e ORR activity. Structure-activity analysis identified an optimal configuration (0.97 ideal active sites), guiding efficient catalyst design.

Authors

  • Zhen Chen
    School of Basic Medicine, Qingdao University, Qingdao 266021, China.
  • Xi Liu
    Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou Medical College, Soochow University, Suzhou, Jiangsu 215123, China.
  • Junyi Zhu
    Institute of Carbon Neutrality, Zhejiang Wanli University, Ningbo, 315100, China. wangx@zwu.edu.cn.
  • Bihua Hu
    Institute of Carbon Neutrality, Zhejiang Wanli University, Ningbo, 315100, China. wangx@zwu.edu.cn.
  • Lin Yang
    National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Shuqin Song
    The Key Lab of Low-carbon Chemistry & Energy Conservation of Guangdong Province, PCFM Laboratory, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. stsssq@mail.sysu.edu.cn.
  • Zhongwei Chen
    Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

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