Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids

TL;DR

This paper develops machine learning-based coarse-grained models for meso-scale interfacial fluids, accurately capturing multi-scale behaviors and collective phenomena by leveraging deep learning trained on equilibrium force data.

Contribution

It introduces a deep coarse-grained potential scheme for polymeric fluids that faithfully encodes many-body interactions and heterogeneity at the meso-scale.

Findings

Accurately reproduces void formation probability density functions.

Captures the spectrum of capillary waves at the interface.

Predicts volume-to-area scaling transition in solvation energy.

Abstract

A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component of polymeric fluid systems based on the recently developed deep coarse-grained potential (DeePCG) scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity.