An automatic robot system for machine learning–assisted high-throughput screening of composite electrocatalysts

Abstract

The expected shift from fossil fuels to H₂ as the main renewable energy carrier inspires the search for inexpensive, reliable, and green H₂ production methods such as seawater electrolysis. However, the noble metal-based catalysts used for electrolytic H₂ production are costly and should be replaced by cheaper non-noble metal–based ones. Currently, progress in this field remains slow because of the multidimensionality and vastness of the related search space. Herein, a high-throughput automatic robot was used to prepare Co–Mn–Fe–Ni–based composite-oxide anodic electrocatalysts and characterize their ability to promote the selective and stable production of O₂/HClO at the anode during the electrolysis of model seawater (aqueous NaCl). Moreover, machine learning–aided composition optimization was performed using a Bayesian optimization framework. The adopted approach is not limited to electrocatalysts and thus accelerates research and development in the field of materials chemistry and paves the way for technological advances.