Unveiling key descriptors via machine learning: toward rational molecular design of chromophores with excited-state intramolecular proton transfer

Chemical Science 2026 Open access

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Authors: Shengsheng Wei, Zipeng Yang, Chao Yang, Hongmei Zhao, Yang Li, Yuanyuan Guo, Andong Xia, Zhuoran Kuang
DOI: 10.1039/d5sc07051a
Cited by: 1
References: 59

Abstract

This work introduces a data-driven, closed-loop strategy for ESIPT molecular design with Δ E * prediction, interpretation, and candidate generation. The strategy was validated by synthesizing two AI-designed molecules with distinct dual emission.

Fields of Study & Subjects

Photochemistry and Electron Transfer Studies Molecular Sensors and Ion Detection Photochromic and Fluorescence Chemistry

Publication details

Journal / source
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Volume
17
Issue
14
Pages
7146–7156
Publication date
1 January 2026
Language
EN