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Ground-State Orbital Descriptors for Accelerated Development of Organic Room-Temperature Phosphorescent Materials.
Mao, Yufeng; Yao, Xiaokang; Yu, Ze; An, Zhongfu; Ma, Huili.
Afiliação
  • Mao Y; Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China.
  • Yao X; The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005 Fujian, China.
  • Yu Z; Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China.
  • An Z; Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China.
  • Ma H; Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, 211816, China.
Angew Chem Int Ed Engl ; 63(11): e202318836, 2024 Mar 11.
Article em En | MEDLINE | ID: mdl-38141053
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 (πFMOs ) derived from the π-electron component of the frontier molecular orbitals to characterize the RTP lifetime (τp ), achieving a balance in screening efficiency and accuracy. Using the πFMOs , a data-driven machine learning model gains a high accuracy in classifying long τp , 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Angew Chem Int Ed Engl Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Angew Chem Int Ed Engl Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China