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

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.

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• Machine learning for molecular simulation
• Instantaneous Liquid Interfaces
• Dissipative particle dynamics for interacting systems
• Fluctuations of water near extended hydrophobic and hydrophilic surfaces
• DeePCG: constructing coarse-grained models via deep neural networks
• Solvent coarse-graining and the string method applied to the hydrophobic collapse of a hydrated chain
• End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
• Data-driven parameterization of the generalized Langevin equation
• Reinforced dynamics for enhanced sampling in large atomic and molecular systems
• Model of a fluid at small and large length scales and the hydrophobic effect
• Free energy surface reconstruction from umbrella samples using Gaussian process regression
• Automatic Multi-Objective Optimization of Coarse-Grained Lipid Force Fields Using SwarmCG
• Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features