Bayesian Molecular Optimization for Accelerating Reverse Intersystem Crossing

Spin conversion in molecular excited states is crucial for the development of next-generation optoelectronic devices. However, optimizing molecular structures for rapid spin conversion has relied on time-consuming experimental trial-and-error, which limits the elucidation of the structure-property relationships. Here, we report a Bayesian molecular optimization approach that accelerates virtual screening for rapid triplet-to-singlet reverse intersystem crossing (RISC). One the molecules identified through this virtual screening exhibits a fast RISC rate constant of 1.3 × 108 s–1 and a high external electroluminescence quantum efficiency of 25.7%, which remains as high as 22.8% even at a practical luminance of 5,000 cd m–2 in organic light-emitting diodes. Post-hoc analysis of the trained machine learning model reveals the impact of molecular structural features on spin conversion, paving the way for informed and precise materials development.