Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T13:20:54.608Z Has data issue: false hasContentIssue false
  • English
  • Français

Long-time molecular dynamics simulations on massively parallel platforms: A comparison of parallel replica dynamics and parallel trajectory splicing

Published online by Cambridge University Press:  20 December 2017

Danny Perez ,
Rao Huang  and
Arthur F. Voter
Danny Perez*
Affiliation:
Theoretical Division T-1, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Rao Huang
Affiliation:
Theoretical Division T-1, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Arthur F. Voter
Affiliation:
Theoretical Division T-1, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
*
a)Address all correspondence to this author. e-mail: [email protected]
Get access

Abstract

Molecular dynamics (MD) is one of the most widely used techniques in computational materials science. By providing fully resolved trajectories, it allows for a natural description of static, thermodynamic, and kinetic properties. A major hurdle that has hampered the use of MD is the fact that the timescales that can be directly simulated are very limited, even when using massively parallel computers. In this study, we compare two time-parallelization approaches, parallel replica dynamics (ParRep) and parallel trajectory splicing (ParSplice), that were specifically designed to address this issue for rare event systems by leveraging parallel computing resources. Using simulations of the relaxation of small disordered platinum nanoparticles, a comparative performance analysis of the two methods is presented. The results show that ParSplice can significantly outperform ParRep in the common case where the trajectory remains trapped for a long time within a region of configuration space but makes rapid structural transitions within this region.

Keywords

simulationnanoscale
Type
Article
Information
Journal of Materials Research , Volume 33 , Issue 7: Focus Issue: Advanced Atomistic Algorithms in Materials Science , 13 April 2018 , pp. 813 - 822
Check for updates
Copyright
Copyright © Materials Research Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

b)

This author was an editor of this journal during the review and decision stage. For the JMR policy on review and publication of manuscripts authored by editors, please refer to http://www.mrs.org/editor-manuscripts/.

c)

Permanent address: Department of Physics, Xiamen University, Xiamen 361005, China.

Contributing Editor: Vikram Gavini

References

REFERENCES

Lions, J.L., Maday, Y., and Turinici, G.: A “parareal” in time discretization of PDE’s. C. R. Acad. Sci. Paris Sér. I Math. 332, 661–668 (2001).Google Scholar
Voter, A.F.: Hyperdynamics: Accelerated molecular dynamics of infrequent events. Phys. Rev. Lett. 78, 3908–3911 (1997).Google Scholar
Zamora, R.J., Uberuaga, B.P., Perez, D., and Voter, A.F.: The modern temperature-accelerated dynamics approach. Annu. Rev. Chem. Biomol. Eng. 7, 87–110 (2016).Google Scholar
Sorensen, M.R. and Voter, A.F.: Temperature-accelerated dynamics for simulation of infrequent events. J. Chem. Phys. 112, 9599–9606 (2000).Google Scholar
Henkelman, G. and Jonsson, H.: Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table. J. Chem. Phys. 115, 9657–9666 (2001).Google Scholar
Perez, D., Uberuaga, B.P., and Voter, A.F.: The parallel replica dynamics method—Coming of age. Comput. Mater. Sci. 100, 90–103 (2015).Google Scholar
Voter, A.F.: Parallel replica method for dynamics of infrequent events. Phys. Rev. B 57, 13985–13988 (1998).CrossRefGoogle Scholar
Perez, D., Cubuk, E.D., Waterland, A., Kaxiras, E., and Voter, A.F.: Long-time dynamics through parallel trajectory splicing. J. Chem. Theory Comput. 12, 18–28 (2016).CrossRefGoogle ScholarPubMed
Le Bris, C., Lelievre, T., Luskin, M., and Perez, D.: A mathematical formalization of the parallel replica dynamics. Monte Carlo Methods Appl. 18, 119–146 (2012).Google Scholar
El-Mellouhi, F., Mousseau, N., and Lewis, L.J.: Kinetic activation-relaxation technique: An off-lattice self-learning kinetic Monte Carlo algorithm. Phys. Rev. B 78, 153202 (2008).CrossRefGoogle Scholar
Fichthorn, K.A. and Lin, Y.: A local superbasin kinetic Monte Carlo method. J. Chem. Phys. 138, 164104 (2013).CrossRefGoogle ScholarPubMed
Chatterjee, A. and Voter, A.F.: Accurate acceleration of kinetic Monte Carlo simulations through the modification of rate constants. J. Chem. Phys. 132, 194101 (2010).CrossRefGoogle ScholarPubMed
Novotny, M.A.: Monte Carlo algorithms with absorbing Markov chains: Fast local algorithms for slow dynamics. Phys. Rev. Lett. 74, 1 (1995).Google Scholar
Wales, D.: Energy Landscapes: Applications to Clusters, Biomolecules and Glasses (Cambridge University Press, Cambridge, United Kingdom, 2003).Google Scholar
Available at: http://gitlab.com/exaalt.Google Scholar
Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).Google Scholar
Voter, A.F. and Chen, S.P.: Accurate interatomic potentials for Ni, Al and Ni3Al. MRS Online Proc. Libr. 82, 175–180, (1986).Google Scholar
Voter, A.F.: Embedded Atom Method Potentials for Seven FCC Metals: Ni, Pd, Pt, Cu, Ag, Au, and Al; Los Alamos Unclassified Technical Report, LA-UR-93–3901 (1993).Google Scholar
Schenk, T., Holland-Moritz, D., Simonet, V., Bellissent, R., and Herlach, D.M.: Icosahedral short-range order in deeply undercooled metallic melts. Phys. Rev. Lett. 89, 075507 (2002).Google Scholar
Reichert, H., Klein, O., Dosch, H., and Donk, M.: Observation of five-fold local symmetry in liquid lead. Nature 408, 839 (2000).Google Scholar
Deuflhard, P. and Weber, M.: Robust Perron cluster analysis in conformation dynamics. Linear Algebra Appl. 398, 161–184 (2005).Google Scholar
Huang, R., Lo, L-T., Wen, Y., Voter, A.F., and Perez, D.: Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles. J. Chem. Phys. 147, 152717 (2017).Google Scholar
Clarke, A.S. and Jónsson, H.: Structural changes accompanying densification of random hard-sphere packings. Phys. Rev. E 47, 3975 (1993).Google Scholar
Apra, E., Baletto, F., Ferrando, R., and Fortunelli, A.: Amorphization mechanism of icosahedral metal nanoclusters. Phys. Rev. Lett. 93, 065502 (2004).Google Scholar
Doye, J.P.K., Miller, M.A., and Wales, D.J.: The double-funnel energy landscape of the 38-atom Lennard-Jones cluster. J. Chem. Phys. 110, 6896–6906 (1999).Google Scholar