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Computational aspects of many-body potentials

Published online by Cambridge University Press:  09 May 2012

Steven J. Plimpton  and
Aidan P. Thompson
Steven J. Plimpton
Affiliation:
Sandia National Laboratories, Albuquerque, NM; email [email protected]
Aidan P. Thompson
Affiliation:
Scalable Algorithms Department, Sandia National Laboratories, Albuquerque, NM; [email protected]
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Abstract

We discuss the relative complexity and computational cost of several popular many-body empirical potentials, developed by the materials science community over the past 30 years. The inclusion of more detailed many-body effects has come at a computational cost, but the cost still scales linearly with the number of atoms modeled. This is enabling very large molecular dynamics simulations with unprecedented atomic-scale fidelity to physical and chemical phenomena. The cost and scalability of the potentials, run in serial and parallel, are benchmarked in the LAMMPS molecular dynamics code. Several recent large calculations performed with these potentials are highlighted to illustrate what is now possible on current supercomputers. We conclude with a brief mention of high-performance computing architecture trends and the research issues they raise for continued potential development and use.

Keywords

Simulationfracturenanostructureembrittlement
Type
Research Article
Information
MRS Bulletin , Volume 37 , Issue 5: Three decades of many-body potentials in materials research , May 2012 , pp. 513 - 521
Copyright
Copyright © Materials Research Society 2012

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References

1.Pixar Animation Studios; www.pixar.com.Google Scholar
2.Pickavance, M., online posting, www.denofgeek.com/movies/417298/the_cgi_achievements_of_pixar.html (accessed March 2012).Google Scholar
3.Good, C., online posting; www.quora.com/Toy-Story-movie-series/How-much-faster-would-it-be-to-render-Toy-Story-in-2011-compared-to-how-long-it-took-in-1995 (accessed March 2012).Google Scholar
4.Jones, J.E., Proc. R. Soc. London, Ser. A 106, 463 (1924).Google Scholar
5.MacKerell, A.D. Jr., Bashford, D., Bellott, M., Dunbrack, R.L. Jr., Evanseck, J.D., Field, M.J., Fischer, S., Gao, J., Guo, H., Ha, S., Joseph-McCarthy, D., Kuchnir, L., Kuczera, K., Lau, F.T.K., Mattos, C., Michnick, S., Ngo, T., Nguyen, D.T., Prodhom, B., Reiher, W.E. III, Roux, B., Schlenkrich, M., Smith, J.C., Stote, R., Straub, J., Watanabe, M., Wirkiewicz-Kuczera, J., Yin, D., Karplus, M., J. Phys. Chem. B 102, 3586 (1998).CrossRefGoogle Scholar
6.Cheatham, T.E. III, Young, M.A., Biopolymers 56, 232 (2001).3.0.CO;2-H>CrossRefGoogle Scholar
7.Daw, M.S., Baskes, M.I., Phys. Rev. Lett. 50, 1285 (1983).CrossRefGoogle Scholar
8.Daw, M.S., Baskes, M.I., Phys. Rev. B 29, 6443 (1984).CrossRefGoogle Scholar
9.Baskes, M.I., Phys. Rev. Lett. 59, 2666 (1987).CrossRefGoogle Scholar
10.Tersoff, J., Phys. Rev. B 37, 6991 (1988).CrossRefGoogle Scholar
11.Brenner, D.W., Phys. Rev. B 42, 9458 (1990).CrossRefGoogle Scholar
12.Stuart, S.J., Tutein, A.B., Harrison, J.A.. J. Chem. Phys. 112, 6472 (2000).CrossRefGoogle Scholar
13.Pettifor, D.G., Oleinik, I.I., Phys. Rev. B 59, 8487 (1999).CrossRefGoogle Scholar
14.van Duin, A.C.T., Dasgupta, S., Lorant, F., Goddard, W.A. III, J. Phys. Chem. A 105, 9396 (2001).CrossRefGoogle Scholar
15.Yu, J., Sinnott, S.B., Phillpot, S.R., Phys. Rev. B 75, 085311 (2007).CrossRefGoogle Scholar
16.Rick, S.W., Stuart, S.J., Berne, B.J., J. Chem. Phys. 101, 16141 (1994).CrossRefGoogle Scholar
17.Wolf, D., Keblinski, P., Phillpot, S.R., Eggebrecht, J., J. Chem. Phys. 110, 8254 (1999).CrossRefGoogle Scholar
18.Ewald, P., Ann. Phys. 369, 253–287 (1921).CrossRefGoogle Scholar
19.LAMMPS molecular dynamics package, http://lammps.sandia.gov; Potential benchmarks, http://lammps.sandia.gov/bench.html#potentials.Google Scholar
20.Plimpton, S., J. Comp. Phys. 117, 1 (1995).CrossRefGoogle Scholar
21.Brenner, D.W., Shenderova, O.A., Harrison, J.A., Stuart, S.J., Ni, B., Sinnott, S.B., J. Phys. Condens. Matter 14, 783 (2002).CrossRefGoogle Scholar
22.Shan, T.-R., Devine, B.D., Kemper, T.W., Sinnott, S.B., Phillpot, S.R., Phys. Rev. B 81, 125328 (2010).CrossRefGoogle Scholar
23.Thompson, A.P., Plimpton, S.J., Mattson, W., J. Chem. Phys. 131, 154107 (2009).CrossRefGoogle Scholar
24.Hockney, R.W., Eastwood, J.W., Computer Simulation Using Particles (IOP, Bristol, 1988).CrossRefGoogle Scholar
25.Pollock, E.L., Glosli, J., Comput. Phys. Commun. 95, 93 (1996).CrossRefGoogle Scholar
26.Darden, T., York, D., Pedersen, L., J. Chem. Phys. 98, 10089 (1993).CrossRefGoogle Scholar
27.Bártok, A.P., Payne, M.C., Kondor, R., Csányi, G., Phys. Rev. Lett. 104, 136403 (2010).CrossRefGoogle Scholar
28.Mattsson, T.R., Desjarlais, M.P., Phys. Rev. Lett. 97 (1) (2006).CrossRefGoogle Scholar
29.Root, S., Magyar, R.J., Carpenter, J.H., Hanson, D.L., Mattsson, T.R., Phys. Rev. Lett. 105 (8) (2010).CrossRefGoogle Scholar
30.Kresse, G., Hafner, J., Phys. Rev. B 49 (20), 14251 (1994).CrossRefGoogle Scholar
31.Kubota, A., Wolfer, W.G., Valone, S.M., Baskes, M.I., J. Comput.-Aided Mater. Des. 14, 367 (2007).CrossRefGoogle Scholar
32.Cornwell, C.F., Welch, C.R., J. Chem. Phys. 134, 204708 (2011).CrossRefGoogle Scholar
33.Chen, H.P., Kalia, R.K., Kaxiras, E., Lu, G., Nakano, A., Nomura, K., van Duin, A.C.T., Vashishta, P., Yuan, Z., Phys. Rev. Lett. 104, 155502 (2010).CrossRefGoogle Scholar
34.Tsuzuki, H., Branicio, P.S., Rino, J.P.. Comput. Phys. Commun. 177, 518 (2007).CrossRefGoogle Scholar
35.Lane, J.M.D., Grest, G.S., Thompson, A.P., Cochrane, K.R., Desjarlais, M.P., Mattsson, T.R., in AIP Conference Proceedings, Shock Compression of Condensed Matter 2011, Elert, M., Buttler, W.T., Borg, J.P., Jordan, J.L., Vogler, T.J., Eds., vol. 1426, p. 1435 (2012).Google Scholar
36.Knowledgebase of Interatomic Models (KIM); www.openkim.org.Google Scholar
37.Anderson, J.A., Lorenz, C.D., Travesset, A., J. Comput. Phys. 227, 5342 (2008).CrossRefGoogle Scholar
38.Brown, W.M., Kohlmeyer, A., Plimpton, S.J., Tharringon, A.N., Comput. Phys. Commun. 183, 449 (2012).CrossRefGoogle Scholar
39.Brown, W.M., Wang, P., Plimpton, S.J., Tharrington, A.N., Comput. Phys. Commun. 182 (4), 898 (2011).CrossRefGoogle Scholar
40.Stone, J.E., Phillips, J.C., Freddolino, P.L., Hardy, D.J., Trabuco, L.G., Schulten, K., J. Comput. Chem. 28 (16), 2618 (2007).CrossRefGoogle Scholar
41.Shaw, D.E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R.O., Eastwood, M.P., Bank, J.A., Jumper, J.M., Salmon, J.K., Shan, Y.B., Wriggers, W., Science 330, 341 (2010).CrossRefGoogle Scholar
42.Berendsen, H.J.C., Grigera, J.R., Straatsma, T.P., J. Phys. Chem. 91, 6269 (1987).CrossRefGoogle Scholar