Bridging Microscopic Dynamics and Macroscopic Fate: A Molecular Dynamics-Machine Learning Approach for Predicting PFAS Solid–Liquid Distribution in Soils and Sediments

Per- and polyfluoroalkyl substances (PFAS) are persistent global contaminants, posing challenges to predicting their environmental fate. The solid-liquid distribution coefficient (log<i>K</i>_d) is a key parameter for PFAS mobility, but current machine learning (ML) models often overlook its susceptibility to real-world water chemistry. To address this, we introduce the Phys-ML Sorp Framework, a novel multiscale approach integrating molecular dynamics (MD) simulations with ML to enhance log<i>K</i>_d prediction. We quantified physically informed microscopic features from MD simulations, including radius of gyration (<i>R</i>_g), solvent accessible surface area (SASA), and a novel effective activity coefficient (logγ) that uniquely captures solute conformational responses by incorporating MD-derived <i>R</i>_g into an extended Debye-Hückel equation, offering a physically meaningful measure of nonideal solution effects. Leveraging 499 PFAS partitioning observations in pure water and calcium chloride (CaCl₂) systems, our model achieved superior predictive performance (RPD = 2.90, RMSE = 0.32). The incorporation of MD-derived microscopic features resulted in a 14.62% improvement in RPD and a 13.52% reduction in RMSE over models relying solely on macroscopic parameters. SHAP analysis revealed molecular weight (MW, 0.32), SASA (0.28), log<i>K</i>_ow (0.23), <i>R</i>_g (0.08), and logγ (0.07) as dominant factors. This framework not only advances environmental pollutant modeling but also establishes a robust, mechanistically informed approach for enhanced environmental risk assessment.