Data-guided rational design of additives for halogenation of highly fluorinated naphthalenes: integrating fluorine chemistry and machine learning
Abstract
= 0.90, RMSE = 10.9). Feature importance and SHAP analyses clarified the design criteria-moderately balanced functional-group charges and non-negative aromatic contributions-as critical for reactivity. Guided by these criteria, a chlorine-substituted additive, 1-chloro-4-(ethoxymethyl)benzene (3a), was designed, predicted to give >99% yield, and experimentally validated to deliver the iodinated product in 98% yield with 96% additive recovery at room temperature. Moreover, additive 3a was effective in iodination, bromination, and chlorination, demonstrating the generality of the design principle. This study advances additive development from empirical trial-and-error to a predictive, machine learning-enabled strategy, offering guidelines for selective transformations of perfluorinated arenes.
Fields of Study & Subjects
Fluorine in Organic Chemistry
Inorganic Fluorides and Related Compounds
Radical Photochemical Reactions
Publication details
- Journal / source
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 28
- Issue
- 11
- Pages
- 6917–6927
- Publication date
- 1 January 2026
- Language
- EN