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

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

doi:10.1039/d5cc03076e

January 1, 2025Chemical Communications

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

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Key Points

Enhanced 2e- ORR activity was achieved through optimized ΔG*OOH by shifting d-bands of Hg/Cu.
The identified optimal configuration featured 0.97 ideal active sites, maximizing catalyst efficacy.
High-throughput DFT calculations integrated with machine learning guided the screening of the catalysts.
This approach indicates promising strategies for future catalyst design and performance enhancements.

Implication

This study demonstrates optimized 2e- ORR activity in AuAgPdHgCu catalysts, suggesting machine learning aids rational design.

Abstract

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

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Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c1afcd54b1d3bfb60e7ea8 https://doi.org/10.1039/d5cc03076e

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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