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.