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/content/aip/journal/jcp/144/16/10.1063/1.4947024
1.
1.D. Wales, Energy Landscapes: Applications to Clusters, Biomolecules and Glasses (Cambridge University Press, 2003).
2.
2.C. J. Pickard and R. J. Needs, J. Phys. B: Condens. Matter 23, 053201 (2011).
http://dx.doi.org/10.1088/0953-8984/23/5/053201
3.
3.A. Voter, Phys. Rev. Lett. 78, 3908 (1997).
http://dx.doi.org/10.1103/PhysRevLett.78.3908
4.
4.M. C. Payne, M. P. Teter, D. C. Allan, T. A. Arias, and J. D. Joannopoulos, Rev. Mod. Phys. 64, 1045 (1992).
http://dx.doi.org/10.1103/RevModPhys.64.1045
5.
5.H. C. Elman, D. J. Silvester, and A. J. Wathen, Finite Elements and Fast Iterative Solvers: With Applications in Incompressible Fluid Dynamics (Oxford University Press, 2014).
6.
6.J. Nocedal and S. J. Wright, Numerical Optimisation, Springer Series in Operations Research and Financial Engineering (Springer, New York, 2006).
7.
7.B. G. Pfrommer, M. Côté, S. G. Louie, and M. L. Cohen, J. Comput. Phys. 131, 233 (1997).
http://dx.doi.org/10.1006/jcph.1996.5612
8.
8.M. V. Fernandex-Serra, E. Artacho, and J. M. Soler, Phys. Rev. B 67, 100101 (2013).
http://dx.doi.org/10.1103/PhysRevB.67.100101
9.
9.D. P. Bertsekas, Nonlinear Programming, 2nd ed. (Athena Scientific, 1999).
10.
10.W. N. Bell, L. N. Olson, and J. B. Schroder, PyAMG: Algebraic multigrid solvers in Python v2.0, 2011 release 2.0.
11.
11.N. Higham, Accuracy and Stability of Numerical Algorithms (SIAM, 2002).
12.
12.E. B. Tadmor, G. S. Smith, N. Bernstein, and E. Kaxiras, Phys. Rev. B 59, 235 (1999).
http://dx.doi.org/10.1103/PhysRevB.59.235
13.
13.F. H. Stillinger and T. A. Weber, Phys. Rev. B 31, 5262 (1985).
http://dx.doi.org/10.1103/PhysRevB.31.5262
14.
14.R. Thomson, C. Hsieh, and V. Rana, J. Appl. Phys. 42, 3154 (1971).
http://dx.doi.org/10.1063/1.1660699
15.
15.J. R. Kermode, T. Albaret, D. Sherman, N. Bernstein, P. Gumbsch, M. C. Payne, G. Csányi, and A. De Vita, Nature 455, 1224 (2008).
http://dx.doi.org/10.1038/nature07297
16.
16.G. Csányi, S. Winfield, J. Kermode, A. Comisso, A. De Vita, N. Bernstein, and M. C. Payne, “Expressive programming for computational physics in Fortran 95+,” in IoP Comput. Phys. Newsletter , Spring 2007, http://www.libatoms.org.
17.
17.G. Kresse and J. Hafner, Phys. Rev. B 49, 14251 (1994).
http://dx.doi.org/10.1103/PhysRevB.49.14251
18.
18.G. Kresse and J. Furthmüller, Phys. Rev. B 54, 11169 (1996).
http://dx.doi.org/10.1103/PhysRevB.54.11169
19.
19.J. VandeVondele, M. Krack, F. Mohamed, M. Parrinello, T. Chassaing, and J. Hutter, Comput. Phys. Commun. 167, 103 (2005).
http://dx.doi.org/10.1016/j.cpc.2004.12.014
20.
20.J. Hutter, M. Iannuzzi, F. Schiffmann, and J. VandeVondele, Wiley Interdiscip. Rev.: Comput. Mol. Sci. 4, 15 (2014).
http://dx.doi.org/10.1002/wcms.1159
21.
21.G. Henkelman and H. Jónsson, J. Chem. Phys. 111, 7010 (1999).
http://dx.doi.org/10.1063/1.480097
22.
22.N. Gould, C. Ortner, and D. Packwood, “An efficient dimer method with preconditioning and linesearch,” Math. Comput. (to be published); e-print arXiv:1407.2817.
23.
23.S. R. Bahn and K. W. Jacobsen, Comput. Sci. Eng. 4, 56 (2002).
http://dx.doi.org/10.1109/5992.998641
24.
24.S. J. Clark, M. D. Segall, C. J. Pickard, P. J. Hasnip, M. I. J. Probert, K. Refson, and M. C. Payne, Z. Kristallogr. - Cryst. Mater. 220, 567 (2005).
http://dx.doi.org/10.1524/zkri.220.5.567.65075
25.
25.S. Plimpton, J. Comput. Phys. 117, 1 (1995).
http://dx.doi.org/10.1006/jcph.1995.1039
26.
26.M. Born and K. Huang, Dynamical Theory of Crystal Lattices (Clarendon Press, 1998).
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/content/aip/journal/jcp/144/16/10.1063/1.4947024
2016-04-26
2016-09-30

Abstract

We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of materials. Our preconditioner is based on the neighbourhood structure and we demonstrate the gain in computational efficiency in a wide range of materials that include metals, insulators, and molecular solids. The simple structure of the preconditioner means that the gains can be realised in practice not only when using expensive electronic structuremodels but also for fast empirical potentials. Even for relatively small systems of a few hundred atoms, we observe speedups of a factor of two or more, and the gain grows with system size. An open source Python implementation within the Atomic Simulation Environment is available, offering interfaces to a wide range of atomistic codes.

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