机器学习驱动双电子氧还原反应的AuAgPdHgCu HEA催化剂的合理设计

IF 4.3 2区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Chemical Communications Pub Date : 2025-07-25 DOI:10.1039/d5cc03076e

Zhen Chen, Xi Liu, Junyi Zhu, Bihua Hu, Lin Yang, Xin Wang, Shuqin Song, Zhongwei Chen

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引用次数: 0

摘要

本研究结合高通量DFT计算和机器学习筛选AuAgPdHgCu高熵合金催化剂，发现Hg/Cu的负d波段位移优化ΔG*OOH以增强2e - ORR活性。结构-活性分析确定了最佳构型（0.97个理想活性位点），指导了高效催化剂的设计。

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Machine learning driven rational design of AuAgPdHgCu HEA catalysts for two-electron oxygen reduction reaction

This study integrated high-throughput DFT calcuulations and machine learning to screen AuAgPdHgCu high-entropy alloy catalysts, revealing that negative d-band shifts of Hg/Cu optimize ΔG_*OOH for enhanced 2e⁻ ORR activity. Structural-activity analysis identified an optimal configuration (0.97 ideal active sites), guiding efficient catalyst design.

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来源期刊

Chemical Communications 化学-化学综合

CiteScore

8.60

自引率

4.10%

发文量

2705

审稿时长

1.4 months

期刊介绍： ChemComm (Chemical Communications) is renowned as the fastest publisher of articles providing information on new avenues of research, drawn from all the world''s major areas of chemical research.