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

Abstract

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:

Guo, Ashley Z.  ^[1];

• Search DOE PAGES for author "Guo, Ashley Z."
• Search DOE PAGES for ORCID "0000-0001-5853-0758"

Sevgen, Emre ^[1]; Sidky, Hythem ^[2]; Whitmer, Jonathan K.  ^[2];

• Search DOE PAGES for author "Whitmer, Jonathan K."
• Search DOE PAGES for ORCID "0000-0003-0370-0873"

Hubbell, Jeffrey A. ^[1]; de Pablo, Juan J. ^[3]

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+ Show Author Affiliations

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:

Tue Apr 03 00:00:00 EDT 2018

Research Org.:

Argonne National Laboratory (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

OSTI Identifier:

1461329

Alternate Identifier(s):

OSTI ID: 1431380

Grant/Contract Number:  

AC02-06CH11357

Resource 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)

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING