Active learning of linearly parametrized interatomic potentials

Computational Materials Science

Volumn 140, Issue , 2017, Pages 171-180

  Podryabinkin, Evgeny V ^a   Shapeev, Alexander V ^a  

^a SKOLKOVO INSTITUTE OF SCIENCE AND TECHNOLOGY   (Russian Federation)

Author keywords

Active learning; Atomistic simulation; Interatomic potential; Learning on the fly; Machine learning; Moment tensor potentials

Indexed keywords

LEARNING SYSTEMS; MOLECULAR DYNAMICS; SOFTWARE TESTING;

ACTIVE LEARNING; ATOMISTIC SIMULATIONS; INTERATOMIC POTENTIAL; MOMENT TENSORS; ON THE FLIES;

ARTIFICIAL INTELLIGENCE;

EID: 85028916984     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2017.08.031     Document Type: Article

References (46)

1. Artacho, E., Sánchez-Portal, D., Ordejón, P., García, A., Soler, J.M., Linear-scaling ab-initio calculations for large and complex systems. Physica Status Solidi (b) 215:1 (1999), 809–817.
2. Skylaris, C.-K., Haynes, P.D., Mostofi, A.A., Payne, M.C., Introducing ONETEP: linear-scaling density functional simulations on parallel computers. J. Chem. Phys., 122(8), 2005, 084119.
3. Bowler, D.R., Miyazaki, T., Calculations for millions of atoms with density functional theory: linear scaling shows its potential. J. Phys.: Condensed Matter, 22(7), 2010, 074207.
4. Finnis, M., Interatomic Forces in Condensed Matter, vol. 1, 2003, OUP, Oxford.
5. Artrith, N., Kolpak, A.M., Grand canonical molecular dynamics simulations of Cu–Au nanoalloys in thermal equilibrium using reactive ANN potentials. Comput. Mater. Sci. 110 (2015), 20–28.
6. Bartók, A.P., Gillan, M.J., Manby, F.R., Csányi, G., Machine-learning approach for one-and two-body corrections to density functional theory: applications to molecular and condensed water. Phys. Rev. B, 88(5), 2013, 054104.
7. Bartók, A.P., Payne, M.C., Kondor, R., Csányi, G., Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett., 104, 2010, 136403, 10.1103/PhysRevLett.104.136403.
8. Behler, J., Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys. 13:40 (2011), 17930–17955.
9. Behler, J., Representing potential energy surfaces by high-dimensional neural network potentials. J. Phys.: Condensed Matter, 26(18), 2014, 183001.
10. Behler, J., Parrinello, M., Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett., 98(14), 2007, 146401.
11. Boes, J.R., Groenenboom, M.C., Keith, J.A., Kitchin, J.R., Neural network and ReaxFF comparison for Au properties. Int. J. Quantum Chem. 116:13 (2016), 979–987.
12. Dolgirev, P.E., Kruglov, I.A., Oganov, A.R., Machine learning scheme for fast extraction of chemically interpretable interatomic potentials. AIP Adv., 6(8), 2016, 085318.
13. Gastegger, M., Marquetand, P., High-dimensional neural network potentials for organic reactions and an improved training algorithm. J. Chem. Theory Comput. 11:5 (2015), 2187–2198.
14. Manzhos, S., Dawes, R., Carrington, T., Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces. Int. J. Quantum Chem. 115:16 (2015), 1012–1020.
15. Natarajan, S.K., Morawietz, T., Behler, J., Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials. Phys. Chem. Chem. Phys. 17:13 (2015), 8356–8371.
16. Shapeev, A.V., Moment tensor potentials. Multiscale Model. Simul. 14:3 (2016), 1153–1173.
17. Szlachta, W.J., Bartók, A.P., Csányi, G., Accuracy and transferability of Gaussian approximation potential models for tungsten. Phys. Rev. B, 90(10), 2014, 104108.
18. Thompson, A., Swiler, L., Trott, C., Foiles, S., Tucker, G., Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J. Comput. Phys. 285 (2015), 316–330, 10.1016/j.jcp.2014.12.018.
19. Faber, F., Lindmaa, A., von Lilienfeld, O.A., Armiento, R., Crystal structure representations for machine learning models of formation energies. Int. J. Quantum Chem. 115:16 (2015), 1094–1101.
20. Rupp, M., Tkatchenko, A., Müller, K.-R., Von Lilienfeld, O.A., Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 108(5), 2012, 058301.
21. Snyder, J.C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K., Finding density functionals with machine learning. Phys. Rev. Lett., 108(25), 2012, 253002.
22. T. Mueller, A.G. Kusne, R. Ramprasad, Machine learning in materials science: recent progress and emerging applications, Rev. Comput. Chem.
23. Bartók, A.P., Kondor, R., Csányi, G., On representing chemical environments. Phys. Rev. B, 87(18), 2013, 184115.
24. Behler, J., Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys., 134(7), 2011, 074106.
25. Smith, J.S., Isayev, O., Roitberg, A.E., Ani-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 21:1 (2017), 124–127, 10.1039/C6SC05720A.
26. Artrith, N., Urban, A., An implementation of artificial neural-network potentials for atomistic materials simulations: performance for tio 2. Comput. Mater. Sci. 114 (2016), 135–150.
27. Deringer, V.L., Csányi, G., Machine learning based interatomic potential for amorphous carbon. Phys. Rev. B, 95, 2017, 094203, 10.1103/PhysRevB.95.094203 URL https://link.aps.org/doi/10.1103/PhysRevB.95.094203.
28. Botu, V., Ramprasad, R., Learning scheme to predict atomic forces and accelerate materials simulations. Phys. Rev. B, 92(9), 2015, 094306.
29. Li, Z., Kermode, J.R., De Vita, A., Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. Phys. Rev. Lett., 114, 2015, 096405, 10.1103/PhysRevLett.114.096405.
30. Glielmo, A., Sollich, P., De Vita, A., Accurate interatomic force fields via machine learning with covariant kernels. Phys. Rev. B, 95, 2017, 214302, 10.1103/PhysRevB.95.214302 URL https://link.aps.org/doi/10.1103/PhysRevB.95.214302.
31. B. Settles, Active learning literature survey, Computer Sciences Technical Report 1648, University of Wisconsin–Madison, 2009.
32. Frederiksen, S.L., Jacobsen, K.W., Brown, K.S., Sethna, J.P., Bayesian ensemble approach to error estimation of interatomic potentials. Phys. Rev. Lett., 93(16), 2004, 165501.
33. Botu, V., Ramprasad, R., Adaptive machine learning framework to accelerate ab initio molecular dynamics. Int. J. Quantum Chem. 115:16 (2015), 1074–1083.
34. Botu, V., Chapman, J., Ramprasad, R., A study of adatom ripening on an al (111) surface with machine learning force fields. Comput. Mater. Sci. 129 (2017), 332–335.
35. Goreinov, S., Oseledets, I., Savostyanov, D., Tyrtyshnikov, E., Zamarashkin, N., How to find a good submatrix. Matrix Methods: Theory, Algorithms, Applications, 2010, Word Scientific, 247–256.
36. De Vita, A., Car, R., A novel scheme for accurate MD simulations of large systems. MRS Proceedings, vol. 491, 1997, Cambridge Univ Press, 473.
37. Csányi, G., Albaret, T., Payne, M., De Vita, A., Learn on the fly: a hybrid classical and quantum-mechanical molecular dynamics simulation. Phys. Rev. Lett., 93(17), 2004, 175503.
38. Sherman, J., Morrison, W.J., Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Ann. Math. Stat. 21:1 (1950), 124–127.
39. Kresse, G., Hafner, J., Ab initio molecular dynamics for liquid metals. Phys. Rev. B, 47(1), 1993, 558.
40. Kresse, G., Furthmüller, J., Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6:1 (1996), 15–50.
41. Kresse, G., Furthmüller, J., Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B, 54(16), 1996, 11169.
42. Blöchl, P.E., Projector augmented-wave method. Phys. Rev. B, 50(24), 1994, 17953, 10.1103/PhysRevB.50.17953.
43. Perdew, J.P., Burke, K., Ernzerhof, M., Generalized gradient approximation made simple. Phys. Rev. Lett., 77(18), 1996, 3865, 10.1103/PhysRevLett.77.3865.
44. Voter, A.F., Parallel replica method for dynamics of infrequent events. Phys. Rev. B, 57(22), 1998, R13985.
45. Mills, G., Jónsson, H., Schenter, G.K., Reversible work transition state theory: application to dissociative adsorption of hydrogen. Surface Sci. 324:2–3 (1995), 305–337.
46. Voter, A.F., Sørensen, M.R., Accelerating atomistic simulations of defect dynamics: hyperdynamics, parallel replica dynamics, and temperature-accelerated dynamics. MRS Proceedings, vol. 538, 1998, Cambridge Univ Press, 427.