Dual loop active learning of hydrophobicity of patterned SAMs

Hydrophobic interactions drive numerous biological and synthetic
processes. The materials used in these processes often possess chemically
heterogeneous surfaces that are characterized by diverse chemical groups
positioned in close proximity at the nanoscale; examples include
functionalized nanomaterials and biomolecules like proteins and peptides.
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 a dual-loop active
learning framework that combines a fast, reduced-accuracy method (a
convolutional neural network) with a slow, higher-accuracy method
(molecular dynamics simulations with enhanced sampling) to efficiently
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. Paper title: Identifying Nonadditive Contributions to the
Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active
LearningAuthors: Atharva Kelkar, Bradley Dallin, Reid Van
LehnDOI: doi.org/10.1063/5.0072385