skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Accelerating atomistic simulations through self-learning bond-boost hyperdynamics

Abstract

By altering the potential energy landscape on which molecular dynamics are carried out, the hyperdynamics method of Voter enables one to significantly accelerate the simulation state-to-state dynamics of physical systems. While very powerful, successful application of the method entails solving the subtle problem of the parametrization of the so-called bias potential. In this study, we first clarify the constraints that must be obeyed by the bias potential and demonstrate that fast sampling of the biased landscape is key to the obtention of proper kinetics. We then propose an approach by which the bond boost potential of Miron and Fichthorn can be safely parametrized based on data acquired in the course of a molecular dynamics simulation. Finally, we introduce a procedure, the Self-Learning Bond Boost method, in which the parametrization is step efficiently carried out on-the-fly for each new state that is visited during the simulation by safely ramping up the strength of the bias potential up to its optimal value. The stability and accuracy of the method are demonstrated.

Authors:
 [1];  [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
960620
Report Number(s):
LA-UR-08-05519; LA-UR-08-5519
Journal ID: ISSN 0021-9606; JCPSA6; TRN: US1002089
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Name: Journal of Chemical Physics; Journal ID: ISSN 0021-9606
Country of Publication:
United States
Language:
English
Subject:
74; ACCURACY; DATA; DYNAMICS; KINETICS; MOLECULAR DYNAMICS METHOD; POTENTIAL ENERGY; POTENTIALS; SAMPLING; SIMULATION; STABILITY; USES

Citation Formats

Perez, Danny, and Voter, Arthur F. Accelerating atomistic simulations through self-learning bond-boost hyperdynamics. United States: N. p., 2008. Web.
Perez, Danny, & Voter, Arthur F. Accelerating atomistic simulations through self-learning bond-boost hyperdynamics. United States.
Perez, Danny, and Voter, Arthur F. 2008. "Accelerating atomistic simulations through self-learning bond-boost hyperdynamics". United States. https://www.osti.gov/servlets/purl/960620.
@article{osti_960620,
title = {Accelerating atomistic simulations through self-learning bond-boost hyperdynamics},
author = {Perez, Danny and Voter, Arthur F},
abstractNote = {By altering the potential energy landscape on which molecular dynamics are carried out, the hyperdynamics method of Voter enables one to significantly accelerate the simulation state-to-state dynamics of physical systems. While very powerful, successful application of the method entails solving the subtle problem of the parametrization of the so-called bias potential. In this study, we first clarify the constraints that must be obeyed by the bias potential and demonstrate that fast sampling of the biased landscape is key to the obtention of proper kinetics. We then propose an approach by which the bond boost potential of Miron and Fichthorn can be safely parametrized based on data acquired in the course of a molecular dynamics simulation. Finally, we introduce a procedure, the Self-Learning Bond Boost method, in which the parametrization is step efficiently carried out on-the-fly for each new state that is visited during the simulation by safely ramping up the strength of the bias potential up to its optimal value. The stability and accuracy of the method are demonstrated.},
doi = {},
url = {https://www.osti.gov/biblio/960620}, journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {1}
}