Accelerated discovery of plasmonic photocatalysts for overall water splitting via Bayesian optimization Hong Wang, Abdoulatif Cisse, Mengjia Zhu, Rob Clowes, Xenophon Evangelopoulos, Charlotte E. Boottand, Andrew I. Cooper Materials Innovation Factory & Department of Chemistry, University of Liverpool, Liverpool L7 3NY, United Kingdom Photocatalytic overall water splitting (OWS) offers a sustainable route to convert solar energy into chemical fuels. Although Al-doped SrTiO 3 exhibits a quantum efficiency of up to 96% under ultraviolet (UV) light, UV accounts for less than 5% of the solar spectrum, limiting its practical utility. [1] However, visible-light-driven photocatalytic overall water splittingremains a highly challenge, due to the lower photon energy and sluggish surface kinetics. To address this, we investigate a plasmon-induced photocatalytic system that enables overall water splitting under visible-light irradiation. Building on our previous advances in autonomous catalyst discovery through Bayesian optimization and data-guided experimentation [2,3] , we extend this approach to tackle the multifaceted design space of plasmonic photocatalysts. We designed an 11-dimensional parameter space encompassing metal particle size, interfacial charge transfer, cocatalyst composition, and strategies for suppressing the reverse reaction. This complex space, encompassing over 10 8 possible synthetic combinations, is explored through a high- throughput experimental workflow tightly coupled with Bayesian optimization algorithms. By integrating rational design principles with machine learning-guided exploration, this study aims to accelerate the discovery of visible-light-active photocatalysts for overall water splitting. References 1. Takata, T.; Jiang, J.; Sakata, Y.; Nakabayashi, M.; Shibata, N.; Nandal, V.; Seki, K.; Hisatomi, T.; Domen, K., Photocatalytic Water Splitting with a Quantum Efficiency of Almost Unity. Nature, 581, 411-414 (2020). 2. Burger, B.; Maffettone, P. M.; Gusev, V. V.; Aitchison, C. M.; Bai, Y.; Wang, X.; Li, X.; Alston, B. M.; Li, B.; Clowes, R.; et al., A Mobile Robotic Chemist. Nature, 583, 237-241 (2020). 3. Li, X.; Che, Y.; Chen, L.; Liu, T.; Wang, K.; Liu, L.; Yang, H.; Pyzer-Knapp, E. O.; Cooper, A. I., Sequential closed-loop Bayesian Optimization as a Guide for Organic Molecular Metallophotocatalyst Formulation Discovery. Nature Chemistry, 16, 1286-1294 (2024).
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