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

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.