ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

ABSTRACT The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates new remediation technologies, yet the vast chemical space makes traditional exploration methods for understanding degradation‐relevant properties intractable. Rational design of PFAS degradation strategies requires accurate prediction of three critical molecular properties: bond dissociation energies (BDEs) to govern kinetics, polarizability to control catalytic interactions, and thermodynamic stability to govern reaction feasibility. Guided by theoretically‐rooted principles, we identify that global properties (polarizability, stability) require spatially‐informed features (3D electron density patterns), while bond‐specific properties are governed by topological features (atomic connectivity). We developed two distinct physics‐informed ML workflows implementing this principle: For global properties, two‐point spatial correlations were compressed via Principal Component Analysis (PCA) and input to a Gaussian Process Regression (GPR) model. For local properties, a graph‐based feature scheme was coupled with a Random Forest (RF) algorithm. Both workflows demonstrated strong predictive performance (GPR R 2 ≈ 0.92 for polarizability; R 2 ≈ 0.97 for enthalpy; RF R 2 ≈ 0.87 for BDE) across multiple datasets, establishing robust Structure‐Property linkages for PFAS. These physics‐informed models provide a foundational capability for rapid, high‐throughput screening of the vast PFAS library, enabling prioritization of candidate molecules and bonding motifs for subsequent experimental and process‐level remediation studies, rather than constituting complete remediation workflows by themselves.