Copper antimony sulfides are regarded as promising catalysts for photo-electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed-loop workflow is built, which explores Cu-Sb-S compositional space to optimize its photo-electrocatalytic hydrogen evolution from water, by integrating a high-throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi-objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments-machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials' properties and photocurrent. An optimum photocurrent of -186 µA cm-2 is observed on Cu-Sb-S in the ratio of 9:45:46 in the form of single-layer coating on F-doped SnO2 (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu1+ /Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.
Keywords: high-throughput experiments; machine learning; photo-electrochemical water splitting.
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