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Updated: Jun 13, 2025
Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
Published on: May 27, 2020
Interpretable prediction of aggregation-induced emission molecules based on graph neural networks
Shi-Chen Zhang1, Jun Zhu2, Yi Zeng1
1Key Laboratory of Cluster Science of Ministry of Education, Key Laboratory of Medicinal Molecule Science and Pharmaceutics Engineering of Ministry of Industry and Information Technology, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China. xiaoyanzheng@bit.edu.cn.
Abstract:
We developed an interpretable graph neural network (96.4% accuracy) for AIEgen identification, revealing 24 characteristic functional groups. Based on these insights, two virtual library strategies (self-fragment and donor-acceptor docking) were proposed and predicted four experimentally confirmed AIEgens successfully, which establishes a rational design framework for AIE materials.
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