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Title: Molecular latent space simulators

Abstract

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.

Authors:
 [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. Pritzker School of Molecular Engineering, University of Chicago, Chicago, USA
  2. Department of Physics, University of Illinois at Urbana-Champaign, Urbana, USA
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
OSTI Identifier:
1657300
Alternate Identifier(s):
OSTI ID: 1651122
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Name: Chemical Science Journal Volume: 11 Journal Issue: 35; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry
Country of Publication:
United Kingdom
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Sidky, Hythem, Chen, Wei, and Ferguson, Andrew L. Molecular latent space simulators. United Kingdom: N. p., 2020. Web. doi:10.1039/D0SC03635H.
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Sidky, Hythem, Chen, Wei, & Ferguson, Andrew L. Molecular latent space simulators. United Kingdom. https://doi.org/10.1039/D0SC03635H
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Sidky, Hythem, Chen, Wei, and Ferguson, Andrew L. Wed . "Molecular latent space simulators". United Kingdom. https://doi.org/10.1039/D0SC03635H.
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@article{osti_1657300,
title = {Molecular latent space simulators},
author = {Sidky, Hythem and Chen, Wei and Ferguson, Andrew L.},
abstractNote = {Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates.},
doi = {10.1039/D0SC03635H},
journal = {Chemical Science},
number = 35,
volume = 11,
place = {United Kingdom},
year = {Wed Sep 16 00:00:00 EDT 2020},
month = {Wed Sep 16 00:00:00 EDT 2020}
}
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https://doi.org/10.1039/D0SC03635H
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