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

ABSTRACT: Precise design of excited-state intramolecular proton transfer (ESIPT) molecules targeting advanced optoelectronic or biological sensing applications presents a fundamental challenge. Controlling the energy difference (ΔE*) between normal (N*) and tautomeric (T*) excited-state forms is crucial, yet the complex interplay of hydrogen bond (H-bond) strength, proton donor acidity, and proton acceptor basicity with ΔE* remains insufficiently explored. Conventional trial-and-error approaches for designing tailored ESIPT compounds suffer from inefficient synthesis. To address this, we constructed a high-quality ESIPT dataset by introducing ten substituents with progressively increasing electron-donating capacity into six representative ESIPT parent scaffolds. Integrating qualitative

SUBMITTER: Wei S 

PROVIDER: S-EPMC12910287 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature