ArticleChemical science2026
A unified solvatochromic-computational-machine-learning approach for solvent property prediction based on iodine color response.
Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Solvent effects govern the majority of chemical transformations, yet experimentally accessible and universally applicable tools for predicting solvating ability remain limited. Here, we introduce iSolv, a simple but information-rich solvatochromic descriptor derived from the color of diluted iodine solutions in organic solvents. Using a combined experimental-computational approach, we demonstrate that iSolv directly reflects the nature and strength of intermolecular interactions, including dispersion interaction, halogen bonding, donor-acceptor interactions and hydrogen bonding. We establish clear correlations between iSolv and key electronic, physicochemical, and empirical solvent parameters, such as HOMO energy, donor number, log
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