在电子描述器的双原子催化剂中解开缩放关系:对OER/ORR活动的机器学习调查
Rahul Kumar Sharma1, Harpriya Minhas1, Biswarup Pathak1
1Department of Chemistry, Indian Institute of Technology Indore, Indore 453552, India.
The journal of physical chemistry letters
|February 18, 2026
概括
机器学习加速了用于燃料电池的双原子催化剂 (DAC) 的发现. 该框架将CoPd和CoCu二极体识别为高度活跃的氧气演变和还原反应,绕过了昂贵的计算.
科学领域:
- 不同质的催化剂.
- 材料科学是一种材料科学.
- 计算化学是一种计算化学.
背景情况:
- 双原子催化剂 (DAC) 在电催化中提供了更高的稳定性和性能.
- 对复杂反应的多金属DAC的选受到巨大的化学空间的阻碍.
- 传统方法通常需要计算昂贵的密度函数理论 (DFT) 计算.
研究的目的:
- 开发一个机器学习 (ML) 框架,用于快速选双原子催化剂 (DAC).
- 为了确定最佳的DAC,以提高氧进化反应 (OER) 和氧减少反应 (ORR) 的性能.
- 了解d频段电子结构在DAC催化活动中的作用.
主要方法:
- 开发了一个基于固态衍生d频段描述器训练的ML模型.
- 编码描述符以捕捉没有DFT的非单调双功能活动.
- 采用表面充电方法来评估潜在依赖活动.
主要成果:
- 确定了CoPd和CoCu二元体,显示出优异的OER和ORR活性.
- 在选的DAC的性能中观察到非扩展行为.
- 揭示了催化活性和电极潜力之间的非线性关系.
结论:
- 确定了d-状态在控制DAC催化性能方面的关键作用.
- 演示了一个实用的ML路径来加速电催化剂的发现.
- 突出了DAC在下一代燃料电池应用中的潜力.
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