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Abstract Organic materials with room‐temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited‐state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground‐state orbital descriptors (π FMOs ) derived from the π‐electron component of the frontier molecular orbitals to characterize the RTP lifetime (τ p ), achieving a balance in screening efficiency and accuracy. Using the π FMOs , a data‐driven machine learning model gains a high accuracy in classifying long τ p , filtering out 836 candidates with long‐lived RTP from a virtual library of 19,295 molecules. With the aid of the excited‐state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high‐performance RTP materials for potential applications.
Mao et al. (Sat,) studied this question.
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