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

Abstract The process of CH 4 cracking into H 2 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 CH 4 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 gH 2 gcat –1 h –1 with 99.9% selectivity and 7.75% CH 4 conversion at 450 °C, 1 atm. Here, we show the mechanical energy boosts CH 4 conversion clearly and sustained CH 4 cracking over 240 h is achieved, significantly surpassing other approaches in the literature.