Bayesian molecular optimization for accelerating reverse intersystem crossing
Abstract
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 of 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 5000 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.
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