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Title: Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations
Authors: Xu, S 
Li, J 
Cai, P
Liu, X 
Liu, B 
Wang, X 
Issue Date: 1-Dec-2021
Publisher: American Chemical Society (ACS)
Citation: Xu, S, Li, J, Cai, P, Liu, X, Liu, B, Wang, X (2021-12-01). Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations. Journal of the American Chemical Society 143 (47) : 19769-19777. ScholarBank@NUS Repository.
Abstract: Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet-triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.
Source Title: Journal of the American Chemical Society
ISSN: 0002-7863
DOI: 10.1021/jacs.1c08211
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