Year: 2026
Type: preprint
Abstract: Characteristic curvature (Cc) quantifies the intrinsic interfacial curvature tendency of surfactants under defined reference conditions and is a key input to the hydrophilic–lipophilic deviation (HLD) framework for predicting microemulsion phase behavior; however, experimental determination remains laborious, and interpretable molecular structure–Cc relationships across diverse surfactant chemistries are comparatively underdeveloped. Here, we developed a quantitative structure–property relationship (QSPR) framework using a curated literature-derived dataset of 63 anionic and nonionic surfactants, with molecular structures verified against the original sources and experimental values curated to improve consistency of the Cc endpoint, and benchmarked multiple tree-based regression algorithms using two-dimensional RDKit descriptors, fold-contained preprocessing, Yeo–Johnson target transformation, SHAP-based feature selection, and nested cross-validation. XGBoost achieved the strongest performance on 13-surfactants test set, with R 2 = 0.85, RMSE = 0.53, and MAE = 0.44 Cc units, with all metrics calculated on the inverse-transformed scale; the observed error was of the same order of magnitude as reported experimental uncertainty, supporting preliminary prediction within the represented chemical domain rather than replacing experimental Cc determination. SHAP and partial-dependence analyses identified electrotopological, topological, hydrophobicity-related, and other physicochemical descriptors associated with the model response, revealing nonlinear and threshold-like patterns that support chemically interpretable structure–property hypotheses without establishing causal mechanisms or experimentally validated design rules; the framework therefore offers a reproducible basis for prioritizing structurally related surfactants for subsequent experimental evaluation and provides a methodological reference for future Cc modeling with broader chemical coverage and prospective validation.
Source: ChemRxiv
Institution: Chulalongkorn University
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Funder: Chulalongkorn University
OpenAlex ID: W7216204463