Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations

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.