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无形高电催化剂的自动发现和最佳生成

Zhanwu Lei¹, Yan Huang¹, Yuanmin Zhu^2,3

¹State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.

Journal of the American Chemical Society

|June 12, 2025

概括

研究人员开发了一种机器学习方法,以发现氧化物演化反应 (OER) 的最佳无形高氧化电催化剂. 这种方法有效地识别了高性能催化剂,克服了设计复杂材料的挑战.

科学领域:

材料科学
电化学
催化剂

背景情况:

无形高材料具有工业潜力,但缺乏优化定义的结构-活动关系.
设计和优化这些材料用于催化等应用仍然是一个重大挑战.

研究的目的:

在整个设计空间中发现和优化性高氧化电催化剂用于氧演化反应 (OER).
建立由机器学习驱动的组成-活动关系,以有效地发现催化剂.

主要方法:

使用合成系统探索超过1,900,000种无形高氧化物电催化剂的组成.
采用机器学习 (ML) 技术来建立组合-活动关系并确定最佳的催化剂组合.
来自六种非贵金属的超薄二维协调聚合物的催化剂,在现场转化为无形氧化.

主要成果:

通过设计空间的ML指导探索,确定了无形高电催化剂的最佳组合组.
已验证的ML模型具有高回忆率 (近100%),准确地分类高和低活性区域.
在性OER中实现了超低的超电位 (159 mV在10 mA cm-2) 和特殊的耐用性 (10,218 h在1 A cm-2).

结论:

开发了一种用于无形高氧化电催化剂的自动发现和优化的一般策略.
基于ML的方法对各种应用的先进无形高材料的开发产生了重大影响.
证明了无形高电催化剂对高效和持久的氧演变反应的潜力.

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