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The atomic-scale response of inhomogeneous fluids at interfaces andsurrounding solute particles plays a critical role in governing chemical,electrochemical and biological processes at such interfaces. Classicalmolecular dynamics simulations have been applied extensively to simulate theresponse of inhomogeneous fluids directly, and as inputs to classical densityfunctional theory, but are limited by the accuracy of the underlying empiricalforce fields. Here, we deploy neural network potentials (NNPs) trained to abinitio simulations to accurately predict the inhomogeneous response of twowidely different fluids: liquid water and molten NaCl. Although NNPs can bereadily trained to model complex bulk systems across a range of state points,in order to appropriately model a fluid's response at an interface,inhomogeneous configurations must be included in the training data. Weestablish protocols based on molecular dynamics simulations in external atomicpotentials in order to sufficiently sample the correct configurations ofinhomogeneous fluids. We show that NNPs trained to inhomogeneous fluidconfigurations can predict several properties such as the density response,surface tension and size-dependent cavitation free energies in water and moltenNaCl corresponding to ab initio interactions more accurately than empiricalforce fields. This work therefore provides a first demonstration and frameworkfor extracting the response of inhomogeneous fluids from first principles forclassical density-functional treatment of fluids free from empiricalpotentials.

Improving the reliability of machine learned potentials for modeling inhomogenous liquids