Coarse-graining auto-encoders for molecular dynamics
Name
s41524-019-0261-5.pdf
Description
Published version
Size
2.45 MB
Format
Adobe PDF
Checksum (MD5)
98dc96a5836d5938fac047cd9a1a7df0
Author(s)
Wang, Wujie
Gomez-Bombarelli, Rafael
Date Issued
December 2019
Journal
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Citation
Wang, Wujie and Rafael Gómez-Bombarelli. “Coarse-graining auto-encoders for molecular dynamics.” npj Computational Materials, 5, 1 (December 2019): 125 © 2019 The Author(s)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41524-019-0261-5
