# A New Approach For Learning Coarse-Grained Potentials with Application to Immiscible Fluids

## TL;DR

This paper evaluates existing force fields for modeling water-hexane interfaces and introduces a new method for learning coarse-grained potentials that better predict interfacial properties.

## Contribution

The paper proposes a novel approach for learning coarse-grained potentials within the CG SDK framework, improving predictions of interfacial tension and structure.

## Key findings

- Atomistic force fields accurately reproduce interfacial tension and densities.
- Existing coarse-grained force fields fail to match interfacial tensions.
- The new learned potentials significantly improve interface property predictions.

## Abstract

Even though atomistic and coarse-grained (CG) models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are available, and most of them are limited to planar interfaces. In this work, we evaluate several existing force fields (FF)s, including two atomistic and three CG FFs, with respect to modeling the interface structure and thermodynamic properties of the water-hexane interface. Both atomistic FFs are able to quantitatively reproduce the interfacial tension and the coexisting densities of the experimentally-observed planar interface. We use the atomistic FFs to model water droplets in hexane and use these simulations to test the CG FFs. We find that the tested CG FFs cannot reproduce the interfacial tensions of planar and/or curved interfaces. Finally, we propose a new approach for learning CG potentials within the CG SDK (Shinoda-DeVane-Klein) FF framework from atomistic simulation data. We demonstrate that the new potential significantly improves the prediction of both the interfacial tension and structure of water-hexane planar and curved interfaces.