A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal

We present the first application of machine learning on per- and polyfluoroalkyl substances (PFAS) for predicting and rationalizing carbon-fluorine (C-F) bond dissociation energies to aid in their efficient treatment and removal. Using a variety of machine learning algorithms (including Random Forest, Least Absolute Shrinkage and Selection Operator Regression, and Feed-forward Neural Networks), we were able to obtain extremely accurate predictions for C-F bond dissociation energies (with deviations less than 0.70 kcal/mol) that are within chemical accuracy of the PFAS reference data. In addition, we show that our machine learning approach is extremely efficient, requiring less than 10 min to train the data and less than a second to predict the C-F bond dissociation energy of a new compound. Most importantly, our approach only needs knowledge of the simple chemical connectivity in a PFAS structure to yield reliable results-without recourse to a computationally expensive quantum mechanical calculation or a three-dimensional structure. Finally, we present an unsupervised machine learning algorithm that can automatically classify and rationalize chemical trends in PFAS structures that would otherwise have been difficult to humanly visualize or process manually. Collectively, these studies (1) comprise the first application of machine learning techniques for PFAS structures to predict/rationalize C-F bond dissociation energies and (2) show immense promise for assisting experimentalists in the targeted defluorination of specific bonds in PFAS structures (or other unknown environmental contaminants) of increasing complexity.