An Active Learning Framework for PFAS-Free Surfactant Discovery in Firefighting Foams

Abstract

The growing concerns over the environmental and health impacts of per- and polyfluoroalkyl substances (PFAS) in firefighting foams have necessitated the development of efficient discovery frameworks for eco-friendly alternatives. In this work, we demonstrate an integrated pipeline leveraging active learning and coarse-grained (CG) molecular dynamics simulations to explore the design space of single-tail hydrocarbon surfactants for inhibiting fuel transport across interfaces. We design a targeted chemical space of 12,124 CG surfactants, represented by latent vectors learned from molecular graphs using a GNN-based autoencoder. Through four rounds of active learning, simulating only 2.5% of this space, we identify top-performing candidates with calculated interfacial transport resistance (Rint) reaching 1641.5 s/m, representing a significant improvement over the commercial DTAB benchmark. Furthermore, we evaluate the effectiveness of constant and adaptive sampling strategies in Bayesian Optimization during the discovery process. Overall, this work establishes an active-learning-driven computational paradigm for accelerating the discovery of PFAS-free surfactants, providing both chemical design insights and an open-access data set to guide future experimental synthesis and validation. All data and source code used in this study are publicly available at https://github.com/elliewang21/Active_Learning.