Expanded Group Additivity Framework for Thermochemical Prediction of Fluorocarbons and PFAS from Large-Scale DFT Data

Fluorinated molecules, including per- and polyfluoroalkyl substances (PFAS), present persistent challenges for thermochemical characterization due to limited experimental data, strong carbon-fluorine bonding, and the rapidly expanding size and diversity of fluorinated chemical space. While density functional theory (DFT) calculations can provide useful thermochemical data for individual fluorinated species, their routine application becomes increasingly impractical as molecular size, conformational complexity, and the number of distinct PFAS compounds continue to grow. Existing Benson-type group additivity schemes provide limited resolution for fluorinated environments, restricting their applicability to modern fluorinated and PFAS-relevant systems. Here, we develop a chemically resolved group additivity (GA) framework for fluorinated and PFAS-relevant species by fragmenting DFT-derived thermochemistry for 3070 molecules. This approach expands the available fluorinated Benson-type group library from 14 to 159 local environments and integrates the resulting groups within the Python Group Additivity (pGrAdd) framework. 10-fold cross-validated regression against DFT data yields root-mean-square deviations (RMSDs) of 8.14 kcal·mol-1 for enthalpy and 10.05 cal·mol-1·K-1 for entropy, which are reduced to 2.55 kcal·mol-1 and 6.36 cal·mol-1·K-1, respectively, following application of independently defined nongroup interaction correction terms in pGrAdd. Comparison with available experimental thermochemical data shows improved agreement and reduced bias compared to legacy Benson group libraries. This expanded fluorinated GA framework enables scalable and chemically interpretable thermochemical predictions for fluorinated and PFAS-relevant species, supporting kinetic modeling and mechanistic studies where direct electronic structure calculations are feasible but not scalable.