Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts

Experimental catalyst optimization is plagued by slow and laborious efforts. Finding innovative materials is key to advancing research areas for sustainable energy conversion, such as electrocatalysis. Artificial intelligence (AI)-guided optimization bears great potential to autonomously learn from data and plan new experiments, identifying a global optimum significantly faster than traditional design of experiment approaches. Furthermore, it is vital to incorporate essential electrocatalyst features such as activity and stability into the optimization campaign to screen for a truly high-performing material. In this study, a multiobjective Bayesian optimization (MOBO) was used in conjunction with an experimental high-throughput (HT) pipeline to refine the composition of a non-noble Co–Mn–Sb–Sn–Ti oxide toward its activity and stability for the oxygen evolution reaction (OER) in acid. The viability of the MOBO algorithm was verified on a gathered data set, and an acceleration of 17x was achieved in subsequent experimental screening compared to a hypothetical grid search scenario. During the ML-driven assessment, Mn-rich compositions were critical to designing high-performing OER catalysts, while Ti incorporation into MnOx triggered an improved activity after short accelerated stress tests. To examine this finding further, an operando mass spectrometry technique was used to probe the evolution of activity, metal dissolution, and surface area over 3 h of operation. This work demonstrates the importance of respecting the multiobjective nature in electrocatalyst performance during HT campaigns. AI-based decision-making helps to bridge the gap between fast HT screening (limited property extraction) and slow fundamental research (rich property extraction) by avoiding less informative experiments.