Automatic Discovery and Optimal Generation of Amorphous High-Entropy Electrocatalysts.

Journal: Journal of the American Chemical Society
Published Date: Jun 12, 2025

Abstract

Amorphous materials are ubiquitous in nature and are widely used for many industrial applications, including catalysis, energy storage, and environmental science. However, significant challenges remain in designing and optimizing amorphous high-entropy materials because of the lack of well-defined structure-activity relationships. Here, we use synthesis systems to discover and optimize amorphous high-entropy oxyhydroxide electrocatalysts within the entire design space for the alkaline oxygen evolution reaction. Amorphous high-entropy electrocatalysts are derived from ultrathin 2D coordination polymers composed of six nonprecious metal elements that were selected from top 16 candidate metal elements involved in oxygen evolution reaction (OER)-related literature searching, which can then be transformed into amorphous oxyhydroxides. Leveraging machine learning (ML) techniques, we establish a composition-activity relationship and thereby identify an optimal composition group by traversing the entire design space (over 1,900,000 compositions). Our ML-model is validated by using 100 compositions in the high-activity region and 588 compositions in the low-activity region, which results in excellent recall values of nearly 100%. The predicted optimal amorphous high-entropy electrocatalyst demonstrates an ultralow overpotential of 159 mV at a current density of 10 mA cm for the alkaline OER in a 1 M KOH while exhibiting ultralong durability 10,218 h under a practical current density of 1 A cm in a 6 M KOH. Our work provides a general strategy for the automatic discovery and optimization of amorphous high-entropy oxyhydroxide electrocatalysts and could significantly impact the development of other amorphous high-entropy materials.

Authors

  • Zhanwu Lei
    State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
  • Yan Huang
    Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX.
  • Yuanmin Zhu
    Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Donglai Zhou
    State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Song Wang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Wanxia Li
    School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing.
  • Huirong Li
    Ocular and Stem Cell Translational Research Section, National Eye Institute, Bethesda, MD, USA.
  • Xiaoke Xi
    State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yuchen Zhang
    School of Computer Science, Shaanxi Normal University, Xi'an, China.
  • Guozhen Zhang
    Hefei National Laboratory for Physical Sciences at the Microscale, CAS Center for Excellence in Nanoscience, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
  • Xiyu Li
    School of Physical Sciences, Great Bay University, Dongguan 523000, China.
  • Qing Zhu
    Zhejiang Pharmaceutical College, Ningbo, 15100.
  • Baicheng Zhang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Shuo Feng
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Sheng Ye
    Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Wensheng Yan
    National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China.
  • Shuo Zhang
    Ph.D. Program in Computer Science, The City University of New York, New York, NY, United States.
  • Shuhong Jiao
    State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
  • Jun Jiang
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, China.
  • Meng Gu
    Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
  • Ruiguo Cao
    State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.

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