Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage and global environmental pollution. Searching for efficient electrocatalysts for HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning is a possible method to screen excellent HER electrocatalysts effectively. This will pave the way for high‐efficiency and low‐price electrocatalyst findings. In this review, we comprehensively introduce the machine learning workflow and standard models for hydrogen reduction reactions. This mainly illustrates how machine learning is used in catalyst filtration and descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) electrocatalysts, and single/dual atom electrocatalysts using machine learning in electrocatalytic HER, are highlighted and introduced. Finally, the corresponding challenge and perspective for machine learning in electrocatalytic hydrogen reduction reactions are concluded. We hope this critical review can provide a comprehensive understanding of machine learning for HER catalyst design and guide the future theoretical and experimental investigation of HER catalyst findings.

中文翻译：

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机器学习辅助理论计算加速析氢反应最佳电催化剂寻找

电催化析氢反应（HER）是解决和减轻即将到来的能源短缺和全球环境污染的一种有前景的策略。通过传统的试错方法从众多潜在候选材料中寻找有效的 HER 电催化剂仍然具有挑战性。机器学习辅助的理论高通量计算是有效筛选优秀HER电催化剂的一种可能方法。这将为高效率和低成本电催化剂的发现铺平道路。在这篇综述中，我们全面介绍了氢还原反应的机器学习工作流程和标准模型。这主要说明了机器学习如何用于催化剂过滤和描述符探索。随后，重点介绍了一些应用，包括表面电催化剂、二维 (2D) 电催化剂以及在电催化 HER 中使用机器学习的单/双原子电催化剂。最后，总结了电催化氢还原反应中机器学习的相应挑战和前景。我们希望这篇批判性评论能够提供对 HER 催化剂设计的机器学习的全面理解，并指导 HER 催化剂研究结果的未来理论和实验研究。