Machine-learning prediction of metal sulfide photocatalysts for sacrificial hydrogen evolution under visible light irradiation

Abstract

The development of promising inorganic semiconductor photocatalysts for water splitting to produce green H₂ is required to achieve a sustainable society. Machine learning is expected to accelerate the exploration of novel inorganic semiconductor photocatalysts. We applied machine learning to explore novel metal sulfide photocatalysts for sacrificial H₂ evolution under visible light irradiation. A machine-learning model that exhibited good accuracy was successfully constructed using our original in-house dataset (not openly shared data) of metal sulfide photocatalysts developed by our group. Then, data on materials in the Inorganic Crystal Structure Database (ICSD) were input into the constructed machine-learning model, resulting in the identification of various metal sulfide candidates with high activities for H₂ evolution in the first screening. We selected Ag₂CdGeS₄ and Cu₂CdMS₄ (M = Ge and Sn) among the candidates for the second screening because many photocatalysts containing Cu(I) and/or Ag(I) ions and corner-shared MS₄ tetrahedra have been reported as visible-light-responsive photocatalysts for sacrificial H₂ evolution. Ag₂CdGeS₄ and Cu₂CdMS₄ (M = Ge and Sn) photocatalysts, prepared by a solid-state reaction, showed activities for sacrificial H₂ evolution under visible light irradiation. Thus, we developed novel visible-light-responsive metal sulfide photocatalysts for sacrificial H₂ evolution by employing machine learning on our original dataset.