Machine learning aided design of single-atom alloy catalysts for methane cracking

Jikai Sun, Rui Tu, Yuchun Xu, Hongyan Yang, Tie Yu (), Dong Zhai, Xiuqin Ci and Weiqiao Deng ()
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Jikai Sun: Shandong University
Rui Tu: Shandong University
Yuchun Xu: Shandong University
Hongyan Yang: Shandong University
Tie Yu: Shandong University
Dong Zhai: Shandong University
Xiuqin Ci: Shandong University
Weiqiao Deng: Shandong University

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract The process of CH4 cracking into H2 and carbon has gained wide attention for hydrogen production. However, traditional catalysis methods suffer rapid deactivation due to severe carbon deposition. In this study, we discover that effective CH4 cracking can be achieved at 450 °C over a Re/Ni single-atom alloy via ball milling. To explore single-atom alloy catalysis, we construct a library of 10,950 transition metal single-atom alloy surfaces and screen candidates based on C–H dissociation energy barriers predicted by a machine learning model. Experimental validation identifies Ir/Ni and Re/Ni as top performers. Notably, the non-noble metal Re/Ni achieves a hydrogen yield of 10.7 gH2 gcat–1 h–1 with 99.9% selectivity and 7.75% CH4 conversion at 450 °C, 1 atm. Here, we show the mechanical energy boosts CH4 conversion clearly and sustained CH4 cracking over 240 h is achieved, significantly surpassing other approaches in the literature.

Date: 2024
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DOI: 10.1038/s41467-024-50417-7

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