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 K 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 K d prediction. We quantified physically informed microscopic features from MD simulations, including radius of gyration ( R g ), solvent accessible surface area (SASA), and a novel effective activity coefficient (logγ) that uniquely captures solute conformational responses by incorporating MD-derived R 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 (CaCl 2 ) 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 K ow (0.23), R 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.