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

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 polymeric fluid systems based on the recently developed deep coarse-grained potential [Zhang et al., J. Chem. Phys. 149, 034101 (2018)] 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.

Automated summary

A key characteristic of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which presents different features at the molecular and continuum scales. Constructing reliable coarse-grained (CG) models poses a challenge due to the multi-scale nature, requiring the CG potential function to accurately capture many-body interactions from unresolved atomistic interactions and account for heterogeneous density distributions at the interface. We construct CG models for single- and two-component polymeric fluid systems using a deep coarse-grained potential scheme, which accurately reproduce the probability density function of void formation in bulk and the spectrum of capillary waves across the fluid interface by solely utilizing training samples of the instantaneous force under thermal equilibrium. Moreover, the CG models accurately predict the volume-to-area scaling transition of apolar solvation energy, demonstrating their effectiveness in probing meso-scale collective behaviors with molecular-level fidelity.