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Citation: Haoyun SHENG, Jiejie LI, Ziqi TIAN. Progress in the design of oxygen evolution catalysts for water electrolysis driven by simulation and data[J]. Chinese Journal of Inorganic Chemistry, ;2026, 42(9): 1845-1870. doi: 10.11862/CJIC.20260059 shu

Progress in the design of oxygen evolution catalysts for water electrolysis driven by simulation and data

  • 1.

    School of Materials Science & Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China

  • 2.

    Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China

  • Corresponding author: Ziqi TIAN, tianziqi@nimte.ac.cn
  • Received Date: 21 February 2026
    Revised Date: 23 April 2026

Figures(8)

  • Water electrolysis for hydrogen production is pivotal for the large-scale deployment of green hydrogen. However, its energy conversion efficiency is constrained by the sluggish kinetics of the anodic oxygen evolution reaction (OER), and current catalysts often lack stability under harsh operating conditions. The vastness of the material chemical space poses significant challenges to the discovery of high-performance anode materials via traditional experimental "trial-and-error" approaches. In recent years, the integration of density functional theory (DFT)-based high-throughput computing (HTC) with machine learning (ML) techniques has emerged as a novel solution for developing high-performance OER catalysts. This paradigm drives material discovery through theoretical simulations and data mining. This review summarizes recent progress in applying this research paradigm to OER catalyst design. First, we outline the OER mechanisms, computational frameworks, and HTC workflows. We also introduce ML workflows, encompassing multimodal dataset construction, physical feature engineering, and advanced algorithmic architectures. Subsequently, the review focuses on the exploration of three representative material systems: noble metal (Ir/Ru-based) oxides, earth-abundant metal oxides/oxyhydroxides (e.g., layered double hydroxides and perovskites), and high-entropy alloys and their oxide derivatives. Finally, we summarize current challenges regarding data standardization, model generalization, and the simulation of realistic operating conditions. We also provide an outlook on the prospects of generative AI (AIGC) and autonomous laboratories in enabling full-process automated material research and development.
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