Prediction of Vertical Excitation and Emission Energies for Optoelectronic Molecules: An Automated Workflow Combining High-Throughput Computations and Machine Learning.

Journal: The journal of physical chemistry letters
Published Date: Jun 15, 2025

Abstract

Organic optoelectronic materials with localized or delocalized excitation features are widely used in various optoelectronic devices. A data-driven automated workflow was implemented for rapidly predicting excited state properties and screening of candidate molecules. A database was built with a collection of 1223 samples in a wide chemical space, including acceptor/donor complexes (such as PM6-Y6) and their derived subsystems. The ground state Dr index was demonstrated to be an important descriptor for evaluating the charge transfer tendency and predicting the Dr* index at the excited state through machine learning (ML) models. The ML-derived relationship among the Dr descriptor, excitation energy, and emission energy was applied to reproduce experimental results without the need for time-consuming calculations of excited states. The π-conjugated units containing three to six fused rings and N/S heteroatoms were found to have a stronger charge transfer tendency than the other functional units, highlighting the great potential of these chromophores in optoelectronic materials.

Authors

  • Lifeng Zheng
    State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Engineering Research Center of Photoresist Materials of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
  • Zhongye Wang
    State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Engineering Research Center of Photoresist Materials of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
  • Jiawei Chen
  • Shaoyi Hou
    State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Engineering Research Center of Photoresist Materials of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
  • Shiyu Dong
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: dsy_shiyu@163.com.
  • Lulu Fu
    State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Engineering Research Center of Photoresist Materials of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
  • Jianan Fan
    Analysis and Testing Central Facility, Institutes of Molecular Engineering and Applied Chemistry, Anhui University of Technology, Ma'anshan 243002, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Po Sun
    Analysis and Testing Central Facility, Institutes of Molecular Engineering and Applied Chemistry, Anhui University of Technology, Ma'anshan 243002, China.
  • Jing Ma
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.

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