Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-09T09:08:01.621Z Has data issue: false hasContentIssue false
  • English
  • Français

Introduction

Published online by Cambridge University Press:  13 April 2018

Enrique Martinez ,
Danny Perez ,
Vikram Gavani  and
Steven Kenny
Enrique Martinez
Affiliation:
Los Alamos National Laboratory, USA
Danny Perez
Affiliation:
Los Alamos National Laboratory, USA
Vikram Gavani
Affiliation:
University of Michigan, Ann Arbor, USA
Steven Kenny
Affiliation:
Loughborough University, U.K.
Get access

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Advanced Atomistic Algorithms in Materials Science
Information
Journal of Materials Research , Volume 33 , Issue 7: Focus Issue: Advanced Atomistic Algorithms in Materials Science , 13 April 2018 , pp. 773 - 776
Copyright
Copyright © Materials Research Society 2018 

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.)

References

REFERENCES

Kohn, W. and Sham, L.J.: Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, 1133–1138 (1965).CrossRefGoogle Scholar
Bowler, D.R. and Miyazaki, T.: O(N) methods in electronic structure calculations. Rep. Prog. Phys. 75, 036503 (2012).Google Scholar
Goedecker, S.: Linear scaling electronic structure methods. Rev. Mod. Phys. 71, 1085 (1999).Google Scholar
Witt, W.C., del Rio, B.G., Dieterich, J.M., and Carter, E.A.: Orbital-free density functional theory for materials research. J. Mater. Res., 1–19 (2018). doi: 10.1557/jmr.2017.462.Google Scholar
Niklasson, A., Tymczak, C., and Challacombe, M.: Time-reversible Born-Oppenheimer molecular dynamics. Phys. Rev. Lett. 97, (2006).Google Scholar
Niklasson, A.: Extended Born-Oppenheimer molecular dynamics. Phys. Rev. Lett. 100, (2008).Google Scholar
Niklasson, A.M.N. et al.: Extended Lagrangian Born–Oppenheimer molecular dynamics with dissipation. J. Chem. Phys. 130, 214109 (2009).CrossRefGoogle ScholarPubMed
Steneteg, P., Abrikosov, I.A., Weber, V., and Niklasson, A.M.N.: Wave function extended Lagrangian Born-Oppenheimer molecular dynamics. Phys. Rev. B 82, (2010).Google Scholar
Hutter, J.: Car–Parrinello molecular dynamics. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2, 604–612 (2012).Google Scholar
Souvatzis, P. and Niklasson, A.M.N.: Extended Lagrangian Born-Oppenheimer molecular dynamics in the limit of vanishing self-consistent field optimization. J. Chem. Phys. 139, 214102 (2013).Google Scholar
Niklasson, A.M.N. and Cawkwell, M.J.: Generalized extended Lagrangian Born-Oppenheimer molecular dynamics. J. Chem. Phys. 141, 164123 (2014).Google Scholar
Martínez, E., Cawkwell, M.J., Voter, A.F., and Niklasson, A.M.N.: Thermostating extended Lagrangian Born-Oppenheimer molecular dynamics. J. Chem. Phys. 142, 154120 (2015).Google Scholar
Voter, A.F.: A method for accelerating the molecular dynamics simulation of infrequent events. J. Chem. Phys. 106, 4665 (1997).Google Scholar
Voter, A.F.: Parallel replica method for dynamics of infrequent events. Phys. Rev. B 57, R13985 (1998).Google Scholar
So/rensen, M.R. and Voter, A.F.: Temperature-accelerated dynamics for simulation of infrequent events. J. Chem. Phys. 112, 9599 (2000).Google Scholar
Bris, C.L., Lelievre, T., Luskin, M., and Perez, D.: A mathematical formalization of the parallel replica dynamics. Monte Carlo Methods Appl. 18, 119 (2012).Google Scholar
Lelièvre, T.: Accelerated dynamics: Mathematical foundations and algorithmic improvements. Eur. Phys. J.: Spec. Top. 224, 2429–2444 (2015).Google Scholar
Perez, D., Huang, R., and Voter, A.F.: Long-time molecular dynamics simulations on massively parallel platforms: A comparison of parallel replica dynamics and parallel trajectory splicing. J. Mater. Res., 1–10 (2017). doi: 10.1557/jmr.2017.456.Google Scholar
Zamora, R.J., Perez, D., and Voter, A.F.: Speculation and replication in temperature accelerated dynamics. J. Mater. Res., 1–12 (2018). doi: 10.1557/jmr.2018.17.Google 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. (2015). doi: 10.1021/acs.jctc.5b00916.Google Scholar
Bortz, A.B., Kalos, M.H., and Lebowitz, J.L.: A new algorithm for Monte Carlo simulation of king spin systems. J. Comput. Phys. 17, 10–18 (1975).Google Scholar
Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22, 403–434 (1976).CrossRefGoogle Scholar
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977).Google Scholar
Yang, Q., Sing-Long, C.A., and Reed, E.J.: L1 regularization-based model reduction of complex chemistry molecular dynamics for statistical learning of kinetic Monte Carlo models. MRS Adv. 1, 1767–1772 (2016).Google Scholar
Martínez, E., Senninger, O., Fu, C-C., and Soisson, F.: Decomposition kinetics of Fe–Cr solid solutions during thermal aging. Phys. Rev. B 86, (2012).Google Scholar
Archarya, S.R. and Rahman, T.S.: Toward multiscale modeling of thin-film growth processes using SLKMC. J. Mater. Res. 33, 709–719 (2018).Google Scholar
Caturla, M.J. et al.: Comparative study of radiation damage accumulation in Cu and Fe. J. Nucl. Mater. 276, 13–21 (2000).Google Scholar
Martin-Bragado, I., Rivera, A., Valles, G., Gomez-Selles, J.L., and Caturla, M.J.: MMonCa: An object kinetic Monte Carlo simulator for damage irradiation evolution and defect diffusion. Comput. Phys. Commun. 184, 2703–2710 (2013).Google Scholar
Becquart, C.S. and Domain, C.: Modeling microstructure and irradiation effects. Metall. Mater. Trans. A 42, 852–870 (2010).Google Scholar
Stoller, R.E., Golubov, S.I., Domain, C., and Becquart, C.S.: Mean field rate theory and object kinetic Monte Carlo: A comparison of kinetic models. J. Nucl. Mater. 382, 77–90 (2008).Google Scholar
Soisson, F.: Kinetic Monte Carlo simulations of radiation induced segregation and precipitation. J. Nucl. Mater. 349, 235–250 (2006).Google Scholar
Soisson, F.: Monte Carlo simulations of segregation and precipitation in alloys under irradiation. Philos. Mag. 85, 489–495 (2005).Google Scholar
Soisson, F. and Jourdan, T.: Radiation-accelerated precipitation in Fe–Cr alloys. Acta Mater. 103, 870–881 (2016).Google Scholar
Soisson, F. and Martin, G.: Monte Carlo simulations of the decomposition of metastable solid solutions: Transient and steady-state nucleation kinetics. Phys. Rev. B 62, 203 (2000).Google Scholar
Terentyev, D. et al.: Further development of large-scale atomistic modelling techniques for Fe–Cr alloys. J. Nucl. Mater. 409, 167–175 (2011).CrossRefGoogle Scholar
Opplestrup, T., Bulatov, V., Gilmer, G., Kalos, M., and Sadigh, B.: First-passage Monte Carlo algorithm: Diffusion without all the hops. Phys. Rev. Lett. 97, (2006).Google Scholar
Hudson, T.S., Dudarev, S.L., Caturla, M-J., and Sutton, A.P.: Effects of elastic interactions on post-cascade radiation damage evolution in kinetic Monte Carlo simulations. Philos. Mag. 85, 661–675 (2005).Google Scholar
Wen, M., Ghoniem, N.M., and Singh, B.N.: Dislocation decoration and raft formation in irradiated materials. Philos. Mag. 85, 2561–2580 (2005).Google Scholar
Fu, C-C., Torre, J.D., Willaime, F., Bocquet, J-L., and Barbu, A.: Multiscale modelling of defect kinetics in irradiated iron. Nat. Mater. 4, 68–74 (2004).Google Scholar
Oppelstrup, T., Jefferson, D.R., Bulatov, V.V., and Zepeda-Ruiz, L.A.: SPOCK: Exact Parallel Kinetic Monte-Carlo on 1.5 Million Tasks (ACM Press, 2016); pp. 127–130. doi: 10.1145/2901378.2901403.Google Scholar
Enrique, R.A. and Bellon, P.: Compositional patterning in systems driven by competing dynamics of different length scale. Phys. Rev. Lett. 84, 2885 (2000).Google Scholar
Henkelman, G. and Jónsson, H.: Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table. J. Chem. Phys. 115, 9657 (2001).CrossRefGoogle Scholar
Vernon, L., Kenny, S.D., Smith, R., and Sanville, E.: Growth mechanisms for TiO2 at its rutile (110) surface. Phys. Rev. B 83, (2011).Google Scholar
Béland, L.K., Brommer, P., El-Mellouhi, F., Joly, J-F., and Mousseau, N.: Kinetic activation-relaxation technique. Phys. Rev. E 84, (2011).Google Scholar
El-Mellouhi, F., Mousseau, N., and Lewis, L.: Kinetic activation-relaxation technique: An off-lattice self-learning kinetic Monte Carlo algorithm. Phys. Rev. B 78, (2008).CrossRefGoogle Scholar
Marinica, M-C., Willaime, F., and Mousseau, N.: Energy landscape of small clusters of self-interstitial dumbbells in iron. Phys. Rev. B 83, (2011).CrossRefGoogle Scholar
Xu, L. and Henkelman, G.: Adaptive kinetic Monte Carlo for first-principles accelerated dynamics. J. Chem. Phys. 129, 114104 (2008).Google Scholar
Xu, H., Osetsky, Y.N., and Stoller, R.E.: Simulating complex atomistic processes: On-the-fly kinetic Monte Carlo scheme with selective active volumes. Phys. Rev. B 84, (2011).Google Scholar
Shim, Y. and Amar, J.: Rigorous synchronous relaxation algorithm for parallel kinetic Monte Carlo simulations of thin film growth. Phys. Rev. B 71, (2005).Google Scholar
Shim, Y. and Amar, J.: Semirigorous synchronous sublattice algorithm for parallel kinetic Monte Carlo simulations of thin film growth. Phys. Rev. B 71, (2005).Google Scholar
Martínez, E., Marian, J., Kalos, M.H., and Perlado, J.M.: Synchronous parallel kinetic Monte Carlo for continuum diffusion-reaction systems. J. Comput. Phys. 227, 3804–3823 (2008).Google Scholar
Martínez, E., Monasterio, P.R., and Marian, J.: Billion-atom synchronous parallel kinetic Monte Carlo simulations of critical 3D Ising systems. J. Comput. Phys. 230, 1359–1369 (2011).Google Scholar
Nandipati, G. et al.: Parallel kinetic Monte Carlo simulations of Ag(111) island coarsening using a large database. J. Phys.: Condens. Matter 21, 084214 (2009).Google Scholar