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Cited by 700Advanced Physical and Chemical Molecular InteractionsTheoretical and Computational PhysicsMachine Learning in Materials Science

Extending the Time Scale in Atomistic Simulation of Materials

Voter, Arthur F., Montalenti, Francesco, Germann, Timothy C.·2002·Annual Review of Materials Research
DOI 10.1146/annurev.matsci.32.112601.141541
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Abstract

▪ Abstract Obtaining a good atomistic description of diffusion dynamics in materials has been a daunting task owing to the time-scale limitations of the molecular dynamics method. We discuss promising new methods, derived from transition state theory, for accelerating molecular dynamics simulations of these infrequent-event processes. These methods, hyperdynamics, parallel replica dynamics, temperature-accelerated dynamics, and on-the-fly kinetic Monte Carlo, can reach simulation times several orders of magnitude longer than direct molecular dynamics while retaining full atomistic detail. Most applications so far have involved surface diffusion and growth, but it is clear that these methods can address a wide range of materials problems.

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  • In order to observe a rare (activated) event without developing dedicated hardware, one needs to selectively increase the speed at which the pathways related to the transition are traversed, to modify the probability distribution that is sampled during the simulation so that configurations close to the transition state occur more often, or to generate adaptively ensembles of trajectories that覆盖the

    “In order to observe a rare (activated) event without developing dedicated hardware 1 one needs to selectively increase the speed at which the pathways related to the transition are traversed 51,87,88 , to modify the probability distribution that is sampled during the simulation so that configurations that are close to the transition state occur more often 15,89 , or to generate adaptively ens”

    From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [51]

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Authors
Voter, A. F., Montalenti, F., & Germann, T. C.
Affiliations
Los Alamos National Laboratory (US)
ORCID
Venue
Annual Review of Materials Research
Publisher
Annual Reviews
Published
2002-08
Volume
32
Issue
1
Pages
321-346
ISSN
  • 1531-7331 (ISSN-L)
  • 1545-4118
DOI
10.1146/annurev.matsci.32.112601.141541
OpenAlex
W2095938967
Cited by
700
References
92
Citations by year
2026: 25 · 2025: 12 · 2024: 16 · 2023: 16 · 2022: 22 · 2021: 29 · 2020: 21 · 2019: 27 · 2018: 19 · 2017: 18 · 2016: 36 · 2015: 31 · 2014: 41 · 2013: 35 · 2012: 33
FWCI
2.75
Type
journal article
Language
en
Open access
closed
Locations
  • Annual Review of Materials Research (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion)
Funders
Division of Materials Research
Topics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
hyperdynamics, transition state theory, diffusion dynamics, surface diffusion, surface growth, atomistic simulation

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