Hyperdynamics boost factor achievable with an ideal bias potential
Abstract
Hyperdynamics is a powerful method to significantly extend the time scales amenable to molecular dynamics simulation of infrequent events. One outstanding challenge, however, is the development of the so-called bias potential required by the method. In this work, we design a bias potential using information about all minimum energy pathways (MEPs) out of the current state. While this approach is not suitable for use in an actual hyperdynamics simulation, because the pathways are generally not known in advance, it allows us to show that it is possible to come very close to the theoretical boost limit of hyperdynamics while maintaining high accuracy. We demonstrate this by applying this MEP-based hyperdynamics (MEP-HD) to metallic surface diffusion systems. In most cases, MEP-HD gives boost factors that are orders of magnitude larger than the best existing bias potential, indicating that further development of hyperdynamics bias potentials could have a significant payoff. Lastly, we discuss potential practical uses of MEP-HD, including the possibility of developing MEP-HD into a true hyperdynamics.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1239372
- Alternate Identifier(s):
- OSTI ID: 1229626
- Report Number(s):
- LA-UR-15-23354
Journal ID: ISSN 0021-9606; JCPSA6; TRN: US1600512
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 143; Journal Issue: 7; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; transition state theory; surface dynamics; rotational correlation time; surface states; vacancies
Citation Formats
Huang, Chen, Perez, Danny, and Voter, Arthur F. Hyperdynamics boost factor achievable with an ideal bias potential. United States: N. p., 2015.
Web. doi:10.1063/1.4928636.
Huang, Chen, Perez, Danny, & Voter, Arthur F. Hyperdynamics boost factor achievable with an ideal bias potential. United States. https://doi.org/10.1063/1.4928636
Huang, Chen, Perez, Danny, and Voter, Arthur F. Thu .
"Hyperdynamics boost factor achievable with an ideal bias potential". United States. https://doi.org/10.1063/1.4928636. https://www.osti.gov/servlets/purl/1239372.
@article{osti_1239372,
title = {Hyperdynamics boost factor achievable with an ideal bias potential},
author = {Huang, Chen and Perez, Danny and Voter, Arthur F.},
abstractNote = {Hyperdynamics is a powerful method to significantly extend the time scales amenable to molecular dynamics simulation of infrequent events. One outstanding challenge, however, is the development of the so-called bias potential required by the method. In this work, we design a bias potential using information about all minimum energy pathways (MEPs) out of the current state. While this approach is not suitable for use in an actual hyperdynamics simulation, because the pathways are generally not known in advance, it allows us to show that it is possible to come very close to the theoretical boost limit of hyperdynamics while maintaining high accuracy. We demonstrate this by applying this MEP-based hyperdynamics (MEP-HD) to metallic surface diffusion systems. In most cases, MEP-HD gives boost factors that are orders of magnitude larger than the best existing bias potential, indicating that further development of hyperdynamics bias potentials could have a significant payoff. Lastly, we discuss potential practical uses of MEP-HD, including the possibility of developing MEP-HD into a true hyperdynamics.},
doi = {10.1063/1.4928636},
journal = {Journal of Chemical Physics},
number = 7,
volume = 143,
place = {United States},
year = {Thu Aug 20 00:00:00 EDT 2015},
month = {Thu Aug 20 00:00:00 EDT 2015}
}
Web of Science
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