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Title: Variational control forces for enhanced sampling of nonequilibrium molecular dynamics simulations

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/1.5128956· OSTI ID:1721632
 [1]; ORCiD logo [2]
  1. Univ. of California, Berkeley, CA (United States)
  2. Univ. of California, Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibrium molecular dynamics simulation through the evaluation of an optimal control force. Optimization of a control force within a chosen basis is made possible by explicit forms for the gradients of a cost function in terms of the susceptibility of driven trajectories to changes in variational parameters. We consider probabilities of time-integrated dynamical observables as characterized by their large deviation functions and find that in many cases, the variational estimate is quantitatively accurate. Additionally, we provide expressions to exactly correct the variational estimate that can be evaluated directly. We benchmark this algorithm against the numerically exact solution of a model of a driven particle in a periodic potential, where the control force can be represented with a complete basis. Additionally, we then demonstrate the utility of the algorithm in a model of repulsive particles on a line, which undergo a dynamical phase transition, resulting in singular changes to the form of the optimal control force. In both systems, we find fast convergence and are able to evaluate large deviation functions with significant increases in statistical efficiency over alternative Monte Carlo approaches.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES), Chemical Sciences, Geosciences & Biosciences Division; USDOE
Grant/Contract Number:
AC02-05CH11231; SC0019375
OSTI ID:
1721632
Alternate ID(s):
OSTI ID: 1580664
Journal Information:
Journal of Chemical Physics, Vol. 151, Issue 24; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 24 works
Citation information provided by
Web of Science

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Cited By (2)

Differentiable Molecular Simulations for Control and Learning preprint January 2020
A reinforcement learning approach to rare trajectory sampling text January 2020