Title: Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics–Monte Carlo propagator

Abstract

Molecular dynamics (MD) trajectories based on classical equations of motion can be used to sample the configurational space of complex molecular systems. However, brute-force MD often converges slowly due to the ruggedness of the underlying potential energy surface. Several schemes have been proposed to address this problem by effectively smoothing the potential energy surface. However, in order to recover the proper Boltzmann equilibrium probability distribution, these approaches must then rely on statistical reweighting techniques or generate the simulations within a Hamiltonian tempering replica-exchange scheme. The present work puts forth a novel hybrid sampling propagator combining Metropolis-Hastings Monte Carlo (MC) with proposed moves generated by non-equilibrium MD (neMD). This hybrid neMD-MC propagator comprises three elementary elements: (i) an atomic system is dynamically propagated for some period of time using standard equilibrium MD on the correct potential energy surface; (ii) the system is then propagated for a brief period of time during what is referred to as a “boosting phase,” via a time-dependent Hamiltonian that is evolved toward the perturbed potential energy surface and then back to the correct potential energy surface; (iii) the resulting configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolismore » criterion before returning to step 1. A symmetric two-end momentum reversal prescription is used at the end of the neMD trajectories to guarantee that the hybrid neMD-MC sampling propagator obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The hybrid neMD-MC sampling propagator is designed and implemented to enhance the sampling by relying on the accelerated MD and solute tempering schemes. It is also combined with the adaptive biased force sampling algorithm to examine. Illustrative tests with specific biomolecular systems indicate that the method can yield a significant speedup.« less

Authors:

Suh, Donghyuk  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-7478-4579"

Radak, Brian K.  ^[2];

• Search DOE PAGES for author "Radak, Brian K."
• Search DOE PAGES for ORCID "0000-0001-8628-5972"

Chipot, Christophe ^[3]; Roux, Benoît ^[4]

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+ Show Author Affiliations

1. Univ. of Chicago, IL (United States)
2. Argonne National Lab. (ANL), Argonne, IL (United States). Leadership Computing Facility
3. Univ. de Lorraine (France); Univ. of Illinois at Urbana-Champaign, IL (United States)
4. Univ. of Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials

Publication Date:

Tue Jan 02 00:00:00 EST 2018

Research Org.:

Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:

National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:

1490226

Alternate Identifier(s):

OSTI ID: 1415336

Grant/Contract Number:  

AC02-06CH11357; MCB-1517221

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Chemical Physics

Additional Journal Information:

Journal Volume: 148; Journal Issue: 1; Journal ID: ISSN 0021-9606

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Potential energy surfaces; Monte Carlo methods; Probability theory; Oscillators; Molecular dynamics; Amino acid; Peptides; Free energy; Dielectric properties; Langevin dynamics