Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation

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Abstract: 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.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation ; day:22 ; month:11 ; year:2024 ; extent:17
Advanced science ; (22.11.2024) (gesamt 17)

Urheber
Gong, Junyi
Deng, Ziwei
Xie, Huilin
Qiu, Zijie
Zhao, Zheng
Tang, Ben Zhong

DOI
10.1002/advs.202411345
URN
urn:nbn:de:101:1-2411221409249.501788121180
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
2025-08-15T07:23:44+0200

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Beteiligte

  • Gong, Junyi
  • Deng, Ziwei
  • Xie, Huilin
  • Qiu, Zijie
  • Zhao, Zheng
  • Tang, Ben Zhong

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