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February 11, 2026Chemical Science0 citationsOpen Access

Unveiling Key Descriptors via Machine Learning: Toward Rational Molecular Design of Chromophores with Excited-State Intramolecular Proton Transfer

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SWShengsheng WeiZYZipeng YangCYChao Yang

Key Points

  • This research aims to optimize chromophores by understanding molecular descriptors that influence excited-state intramolecular proton transfer (ESIPT).
  • Utilized machine learning algorithms to analyze molecular data.
  • Identified key descriptors affecting ESIPT in chromophores.
  • Examined the energy difference between normal and tautomeric states.
  • Found significant relationships between molecular descriptors and proton transfer efficiency.
  • Demonstrated that controlled energy differences enhance performance in optoelectronic applications.

Abstract

Precise design of excited-state intramolecular proton transfer (ESIPT) molecules targeting advanced optoelectronics or biological sensing applications presents a fundamental challenge. Controlling the energy difference (ΔE*) between normal (N*) and tautomeric...

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Cite This Study

Wei et al. (2026) studied this question.

synapsesocial.com/papers/698c1ca1267fb587c655f22dhttps://doi.org/10.1039/d5sc07051a
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