Coarse-graining auto-encoders for molecular dynamics

Wujie Wang,Rafael Gómez-Bombarelli

doi:10.1038/s41524-019-0261-5

Wujie Wang, Rafael Gómez-Bombarelli

Open Access

https://doi.org/10.1038/s41524-019-0261-5

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Abstract

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and, as a predictive tool, enable computational design of new compounds. However, because of the large spatial and temporal scales of thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally infeasible. Coarse-graining methods allow larger systems to be simulated by reducing their dimensionality, propagating longer timesteps, and averaging out fast motions. Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates. We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse-grained force fields. The framework is tested on a number of model systems including single molecules and bulk-phase periodic simulations.

Highlights

Coarse-grained (CG) molecular modeling has been used extensively to simulate complex molecular processes with lower computational cost than all-atom simulations.[1,2] By compressing the full atomistic model into a reduced number of pseudoatoms, CG methods focus on slow collective atomic motions while averaging out fast local motions
Beyond efforts to parameterize CG potentials given a pre-defined all-atom to CG mapping, the selection of an appropriate map plays an important role in recovering consistent CG dynamics, structural correlation, and thermodynamics.[11,12]
A poor choice can lead to information loss in the description of slow collective interactions that are important for glass formation and transport

Summary

Introduction

Coarse-grained (CG) molecular modeling has been used extensively to simulate complex molecular processes with lower computational cost than all-atom simulations.[1,2] By compressing the full atomistic model into a reduced number of pseudoatoms, CG methods focus on slow collective atomic motions while averaging out fast local motions. Current approaches generally focus on parameterizing coarse-grained potentials from atomistic simulations[3] (bottom-up) or experimental statistics (top-down).[4,5] The use of structure-based coarse-grained strategies has enabled important theoretical insights into polymer dynamics[6,7,8,9] and lipid membranes[10] at length scales that are otherwise inaccessible. Beyond efforts to parameterize CG potentials given a pre-defined all-atom to CG mapping, the selection of an appropriate map plays an important role in recovering consistent CG dynamics, structural correlation, and thermodynamics.[11,12] A poor choice can lead to information loss in the description of slow collective interactions that are important for glass formation and transport.

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