Ground-State Orbital Descriptors for Accelerated Development of Organic Room-Temperature Phosphorescent Materials

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 (pi FMOs) derived from the pi-electron component of the frontier molecular orbitals to characterize the RTP lifetime (tau p), achieving a balance in screening efficiency and accuracy. Using the pi FMOs, a data-driven machine learning model gains a high accuracy in classifying long tau 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. This work built a machine learning-aided classifier for phosphorescence lifetime (tau p) based on ground-state orbital descriptors derived from pi-electron in the frontier molecular orbitals (FMOs), balancing screening efficiency and accuracy of organic room-temperature phosphorescent (RTP) materials. With the aid of excited-state calculations, 287 RTP candidates were screened from a virtual library of 19,295 molecules.+image