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

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. An automatic robot developed for the high-throughput preparation and characterization of electrocatalysts was used in conjunction with machine learning-aided composition optimization to achieve the selective production of O2/HClO from NaCl solution.