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

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 (<i>R</i>_int) reaching 1641.5 s/m, representing a significant improvement over the commercial DTAB benchmark.