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Title: Adaptive enhanced sampling by force-biasing using neural networks

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables smooth estimates of generalized forces in sparsely sampled regions, force estimates in previously unexplored regions, and continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. Furthermore, the method is also found to provide improvements over previous implementations of neural network assisted algorithms.
Authors:
ORCiD logo [1] ;  [1] ;  [2] ; ORCiD logo [2] ;  [1] ;  [3]
  1. Univ. of Chicago, Chicago, IL (United States)
  2. Univ. of Notre Dame, Notre Dame, IN (United States)
  3. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 148; Journal Issue: 13; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; Midwest Integrated Center for Computational Materials (MICCoM); National Science Foundation (NSF); USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1461329
Alternate Identifier(s):
OSTI ID: 1431380

Guo, Ashley Z., Sevgen, Emre, Sidky, Hythem, Whitmer, Jonathan K., Hubbell, Jeffrey A., and de Pablo, Juan J.. Adaptive enhanced sampling by force-biasing using neural networks. United States: N. p., Web. doi:10.1063/1.5020733.
Guo, Ashley Z., Sevgen, Emre, Sidky, Hythem, Whitmer, Jonathan K., Hubbell, Jeffrey A., & de Pablo, Juan J.. Adaptive enhanced sampling by force-biasing using neural networks. United States. doi:10.1063/1.5020733.
Guo, Ashley Z., Sevgen, Emre, Sidky, Hythem, Whitmer, Jonathan K., Hubbell, Jeffrey A., and de Pablo, Juan J.. 2018. "Adaptive enhanced sampling by force-biasing using neural networks". United States. doi:10.1063/1.5020733.
@article{osti_1461329,
title = {Adaptive enhanced sampling by force-biasing using neural networks},
author = {Guo, Ashley Z. and Sevgen, Emre and Sidky, Hythem and Whitmer, Jonathan K. and Hubbell, Jeffrey A. and de Pablo, Juan J.},
abstractNote = {A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables smooth estimates of generalized forces in sparsely sampled regions, force estimates in previously unexplored regions, and continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. Furthermore, the method is also found to provide improvements over previous implementations of neural network assisted algorithms.},
doi = {10.1063/1.5020733},
journal = {Journal of Chemical Physics},
number = 13,
volume = 148,
place = {United States},
year = {2018},
month = {4}
}