磷光
激发态
工作流程
材料科学
分子轨道
原子轨道
纳米技术
生物电子学
计算机科学
化学
分子
电子
荧光
原子物理学
物理
数据库
有机化学
生物传感器
量子力学
作者
Yufeng Mao,Xiaokang Yao,Ze Yu,Zhongfu An,Huili Ma
标识
DOI:10.1002/anie.202318836
摘要
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.
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