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机器学习指导筛选多主元合金作为析氢反应电催化剂

伍昊 ,  李凤麒 ,  石欣伟 ,  卞海峰 ,  周庆 ,  贾顺顺 ,  马玉洁 ,  顾坚 ,  张靖梓 ,  何水剑 ,  孟祥康

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引用本文: 伍昊, 李凤麒, 石欣伟, 卞海峰, 周庆, 贾顺顺, 马玉洁, 顾坚, 张靖梓, 何水剑, 孟祥康. 机器学习指导筛选多主元合金作为析氢反应电催化剂[J]. 物理化学学报, 2026, 42(8): 100227. doi: 10.1016/j.actphy.2025.100227 shu
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

机器学习指导筛选多主元合金作为析氢反应电催化剂

  • 1.

    南京大学现代工程与应用科学学院, 固体微结构物理全国重点实验室, 人工微结构科学与技术协同创新中心, 江苏 南京 210093

  • 2.

    南京林业大学材料科学与工程学院, 林业资源高效加工利用协同创新中心, 林产化学与材料国际创新高地, 江苏 南京 210037

  • 3.

    哈尔滨工业大学(深圳), 计算机科学与技术学院, 广东 深圳 518055

    • 通讯作者: Email: zjzhang@hit.edu.cn (张靖梓); shuijianhe@njfu.edu.cn (何水剑); mengxk@nju.edu.cn (孟祥康)
摘要: 多主元合金凭借组分间的协同作用，展现出卓越的物理化学性质，成为极具潜力的析氢反应(HER)电催化剂候选材料。然而，多主元组分结构的复杂性及系统性机器学习(ML)筛选方法的缺乏，使得电催化剂组分的最佳配比无法确定，这制约了多主元合金电催化剂的合理设计与开发。本研究通过Light Gradient Boosting模型从601种候选合金中筛选出了NbZnCo2多主元合金作为最优候选材料，与Pt/C相比，其成本缩减了约34倍，同时HER活性更优。结合密度泛函理论(DFT)计算与实验验证，证实了ML模型的可靠性。微米级NbZnCo2催化剂在10 mA cm−2电流密度下仅需20 mV超低过电位，并保持60 h的稳定运行。此外，纳米级NbZnCo2颗粒仍保持了优异HER性能，验证了NbZnCo2合金作为HER电催化剂的普适性。本研究构建了"机器学习-密度泛函理论-实验"框架，筛选出高性能HER电催化剂，该方法可扩展至其他电催化反应，为可持续能源转换技术提供了更广阔的应用前景。

English

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  • 发布日期:  2026-08-15
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