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Title: Local Hyperdynamics

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  1. Los Alamos National Laboratory
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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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Conference: Stochastic Sampling and Accelerated Time Dynamics on Multidimensional Surfaces ; 2017-10-16 - 2017-10-20 ; Los Angeles, California, United States
Country of Publication:
United States
Mathematics; Material Science

Citation Formats

Voter, Arthur Ford. Local Hyperdynamics. United States: N. p., 2017. Web.
Voter, Arthur Ford. Local Hyperdynamics. United States.
Voter, Arthur Ford. 2017. "Local Hyperdynamics". United States. doi:.
title = {Local Hyperdynamics},
author = {Voter, Arthur Ford},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month =

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  • During the growth of a surface, morphology-controlling diffusion events occur over time scales that far exceed those accessible to molecular dynamics (MD) simulation. Kinetic Monte Carlo offers a way to reach much longer times, but suffers from the fact that the dynamics are correct only if all possible diffusion events are specified in advance. This is difficult due to the concerted nature of many of the recently discovered surface diffusion mechanisms and the complex configurations that arise during real growth. Here the authors describe two new approaches for this type of problem. The first, hyperdynamics, is an accelerated MD method,more » in which the trajectory is run on a modified potential energy surface and time is accumulated as a statistical property. Relative to regular MD, hyperdynamics can give computational gains of more than 10{sup 2}. The second method offers a way to parallelize the dynamics efficiently for systems too small for conventional parallel MD algorithms. Both methods exploit the infrequent-event nature of the diffusion process. After an introductory description of these methods, the authors present preliminary results from simulations combining the two approaches to reach near-millisecond time scales on systems relevant to epitaxial metal growth.« less
  • Obtaining a good atomistic description of diffusion dynamics in materials remains a daunting task due to the time-scale limitations of the molecular dynamics method. The authors discuss new methods, derived from transition state theory, for accelerating molecular dynamics simulations of these infrequent-event processes. Two of these methods (hyperdynamics and parallel replica dynamics) have been presented previously, and are briefly reviewed here. The third, temperature-accelerated dynamics (TAD), is presented in detail. In TAD, the system temperature is raised to stimulate more rapid escape out of each potential basin, but attempted transitions are filtered to allow only those that would have occurredmore » at the normal temperature. The characteristics of the methods are compared.« less
  • The authors describe their studies of two methods for generating bias potentials for use in hyperdynamics simulations. In the first method, first reported by Steiner, et al, the bias potential is the additional energy needed to keep the total potential energy above a fixed level. This potential exerts a negligible computational load and is very easy to code. The second technique involves computing the iterative Hessian-based bias potential as usual, but including in those calculations only the atoms in a small active region that surrounds the area in which a state transition is expected to occur. This subspace hyperdynamics methodmore » is less costly than full HD. The extra computational effort required for HD scales at the size of the active region(s), instead of with the size of the entire simulation domain. The authors have carried out extensive tests of both methods on two problems, the diffusion of adatoms on silver surfaces and the migration of vacancies in bulk silver. Using the first method, to obtain large boosts the bias potential must e so high that many transitions are prevented from occurring. Their results for subspace hyperdynamics are promising; the authors obtain the same results as with full hyperdynamics, but with considerably less computational effort.« less