Deciphering Design of Aggregation-Induced Emission Materials by Data Interpretation.

Autor: Gong J; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China.; Faculty of Chemistry, Shenzhen MSU-BIT University, Longgang, Shenzhen, Guangdong, 518172, P. R. China., Deng Z; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China., Xie H; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China., Qiu Z; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China., Zhao Z; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China., Tang BZ; School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China.; Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, Institute of Molecular Functional Materials, Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, P. R. China.
Jazyk: angličtina
Zdroj: Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2024 Nov 22, pp. e2411345. Date of Electronic Publication: 2024 Nov 22.
DOI: 10.1002/advs.202411345
Abstrakt: This work presents a novel methodology for elucidating the characteristics of aggregation-induced emission (AIE) systems through the application of data science techniques. A new set of chemical fingerprints specifically tailored to the photophysics of AIE systems is developed. The fingerprints are readily interpretable and have demonstrated promising efficacy in addressing influences related to the photophysics of organic light-emitting materials, achieving high accuracy and precision in the regression of emission transition energy (mean absolute error (MAE) ∼ 0.13eV) and the classification of optical features and excited state dynamics mechanisms (F1score ∼ 0.94). Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. This methodology promotes a more profound and thorough comprehension of the characteristics of AIE and guides the development strategies for AIE systems. It offers a solid and overarching framework for the theoretical analysis involved in the design of AIE-generating compounds and elucidates the optical phenomena associated with these compounds.
(© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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