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

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