A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation


Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered.