Identifying Nonadditive Contributions to the Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active Learning

Hydrophobic interactions drive numerous phenomena involving surfaces that are chemically heterogeneous at the nanoscale. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop an active learning framework that utilizes molecular dynamics (MD) simulations, enhanced sampling, and a convolutional neural network to predict the hydration free energy (a thermodynamic descriptor of hydrophobicity) for nearly 200,000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this data set reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further MD analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach.