Data-Driven Optimization of the Solid-Solution Ratio of Stannite-Type Cu x Ag2-xZnGe y Sn1-yS4 Photocatalysts Using Machine Learning with Chemically Similar Out-of-Plane Data

Abstract The optimal solid-solution ratio of the stannite-type CuxAg2–xZnGeySn1–yS4 photocatalyst for sacrificial H2 evolution under visible light (λ ≥ 420 nm) was predicted using a machine learning approach. The prediction mapping indicated that the region around (x, y) = (0.6, 0.3) was the optimal solid-solution ratio. Based on the mapping, we experimentally investigated the activities of the CuxAg2–xZnGeySn1–yS4 photocatalyst, and the ratio around (x = 0.8, y = 0.6) was found to be optimal. Notably, the formation of the solid solution guided by the machine learning prediction enabled both a response to visible light with long wavelength and improvement in the activity of the CuxAg2–xZnGeySn1–yS4 photocatalyst compared with those of the previously developed Ag2ZnMS4(M = Ge and Sn) photocatalyst with a stannite structure. Furthermore, we found that the training data with compositions similar to that of CuxAg2–xZnGeySn1–yS4 played an important role in generating the prediction mapping. This finding suggests that compositionally neighboring data outside the target plane can be effectively leveraged to enable reliable data-driven materials discovery even when data within a specific composition plane are severely limited. This study is expected to promote the development of multielement functional materials beyond photocatalysts.