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双金属固的单原子催化剂用于优质的缩:协同的DFT和机器学习设计

Bo Xiong¹, Chao Yang¹, Junhui Luo¹

¹School of Chemistry and Materials Science, Hubei Provincial Engineering Research Center of Key Technologies in Modern Paper and Hygiene Products Manufacturing, Hubei Engineering University, Xiaogan 432000, PR China.

Langmuir : the ACS journal of surfaces and colloids

|January 24, 2026

概括

机器学习和DFT加速了电化学转化为氨的新催化剂的发现. Ru@AgNi表现出卓越的性能,推进了降解反应电催化.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
计算化学的计算化学
催化剂是一种催化剂.

背景情况:

电化学转化为氨 (N2-to-NH3) 对于可持续的氨生产至关重要.
开发具有高活性,选择性和稳定的催化剂用于N2-到NH3转化仍然是一个重大挑战.
单原子催化剂 (SAC) 提供高效率,但合理设计是复杂的.

研究的目的:

开发一种快速且具有成本效益的方法,用于设计高性能双金属支持的单原子催化剂 (M3@M1M2),用于N2到NH3的转化.
通过使用机器学习引导的方法来识别降解反应 (NRR) 的新型催化剂.

主要方法:

机器学习 (ML) 替代模型与密度函数理论 (DFT) 计算的整合.
一个两步选协议:在一个小的DFT数据集上进行ML模型培训,以快速排名,然后对顶级候选人进行DFT重新评估.
对反应机制的分析,以确定NRR中的潜在限制步骤.

主要成果:

ML-DFT协议准确地预测了与相关系数>0.95和平均平方误差<0.05.05的催化剂性能.
Ru@AgNi被确定为最有前途的NRR电催化剂,DFT证实低电位限制0.454 eV的吉布斯自由能量.
Ru@AgNi显著优于之前调查的催化剂,显示出异常的降低性能.

结论:

基于机器学习的策略为发现高性能,低成本的NRR电催化剂提供了一条有效的途径.
双金属固的单原子催化剂在推进还原反应应用方面非常有前途.
这种方法扩大了下一代电催化剂的设计空间.

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