Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Podryabinkin, Evgeny V.; Tikhonov, Evgeny V.; Shapeev, Alexander V.; Oganov, Artem R.

doi:10.1103/PhysRevB.99.064114

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Journal Article · Wed Feb 27 00:00:00 UTC 2019 · Physical Review B

DOI:https://doi.org/10.1103/PhysRevB.99.064114· OSTI ID:1496896

Podryabinkin, Evgeny V.; Tikhonov, Evgeny V.; Shapeev, Alexander V.; Oganov, Artem R.

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View Accepted Manuscript (Publisher)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396; NA0003525

OSTI ID:: 1496896

Journal Information:: Physical Review B, Journal Name: Physical Review B Journal Issue: 6 Vol. 99; ISSN 2469-9950; ISSN PRBMDO

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (41)

Modern Methods of Crystal Structure Prediction Oganov, Artem R. Wiley‐VCH Verlag GmbH & Co https://doi.org/10.1002/9783527632831	book	November 2010
Kohlenstoff-induzierte gitter beim bor: I-tetragonales (B12)4B2C und (B12)4B2C2 Ploog, K.; Amberger, E. Journal of the Less Common Metals, Vol. 23, Issue 1 https://doi.org/10.1016/0022-5088(71)90005-1	journal	January 1971
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Kresse, G.; Furthmüller, J. Computational Materials Science, Vol. 6, Issue 1, p. 15-50 https://doi.org/10.1016/0927-0256(96)00008-0	journal	July 1996
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 Artrith, Nongnuch; Urban, Alexander Computational Materials Science, Vol. 114 https://doi.org/10.1016/j.commatsci.2015.11.047	journal	March 2016
Active learning of linearly parametrized interatomic potentials Podryabinkin, Evgeny V.; Shapeev, Alexander V. Computational Materials Science, Vol. 140 https://doi.org/10.1016/j.commatsci.2017.08.031	journal	December 2017
New developments in evolutionary structure prediction algorithm USPEX Lyakhov, Andriy O.; Oganov, Artem R.; Stokes, Harold T. Computer Physics Communications, Vol. 184, Issue 4 https://doi.org/10.1016/j.cpc.2012.12.009	journal	April 2013
Modeling hardness of polycrystalline materials and bulk metallic glasses Chen, Xing-Qiu; Niu, Haiyang; Li, Dianzhong Intermetallics, Vol. 19, Issue 9 https://doi.org/10.1016/j.intermet.2011.03.026	journal	September 2011
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials Thompson, A. P.; Swiler, L. P.; Trott, C. R. Journal of Computational Physics, Vol. 285 https://doi.org/10.1016/j.jcp.2014.12.018	journal	March 2015
Exploration versus Exploitation in Global Atomistic Structure Optimization Jørgensen, Mathias S.; Larsen, Uffe F.; Jacobsen, Karsten W. The Journal of Physical Chemistry A, Vol. 122, Issue 5 https://doi.org/10.1021/acs.jpca.8b00160	journal	January 2018
How Evolutionary Crystal Structure Prediction Works—and Why Oganov, Artem R.; Lyakhov, Andriy O.; Valle, Mario Accounts of Chemical Research, Vol. 44, Issue 3 https://doi.org/10.1021/ar1001318	journal	March 2011
β-Rhombohedral Boron: At the Crossroads of the Chemistry of Boron and the Physics of Frustration Ogitsu, Tadashi; Schwegler, Eric; Galli, Giulia Chemical Reviews, Vol. 113, Issue 5 https://doi.org/10.1021/cr300356t	journal	March 2013
ON THE STRUCTURE OF ELEMENTARY BORON ¹ Hoard, J. L.; Geller, S.; Hughes, R. E. Journal of the American Chemical Society, Vol. 73, Issue 4 https://doi.org/10.1021/ja01148a555	journal	April 1951
Thermodynamic Stability of Boron: The Role of Defects and Zero Point Motion van Setten, Michiel J.; Uijttewaal, Matthé A.; de Wijs, Gilles A. Journal of the American Chemical Society, Vol. 129, Issue 9 https://doi.org/10.1021/ja0631246	journal	March 2007
Ionic high-pressure form of elemental boron Oganov, Artem R.; Chen, Jiuhua; Gatti, Carlo Nature, Vol. 457, Issue 7231 https://doi.org/10.1038/nature07736	journal	January 2009
Transparent dense sodium Ma, Yanming; Eremets, Mikhail; Oganov, Artem R. Nature, Vol. 458, Issue 7235 https://doi.org/10.1038/nature07786	journal	March 2009
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Smith, J. S.; Isayev, O.; Roitberg, A. E. Chemical Science, Vol. 8, Issue 4 https://doi.org/10.1039/C6SC05720A	journal	January 2017
Crystal structure prediction using ab initio evolutionary techniques: Principles and applications Oganov, Artem R.; Glass, Colin W. The Journal of Chemical Physics, Vol. 124, Issue 24 https://doi.org/10.1063/1.2210932	journal	June 2006
Construction of high-dimensional neural network potentials using environment-dependent atom pairs Jose, K. V. Jovan; Artrith, Nongnuch; Behler, Jörg The Journal of Chemical Physics, Vol. 136, Issue 19 https://doi.org/10.1063/1.4712397	journal	May 2012
XCII. Relations between the elastic moduli and the plastic properties of polycrystalline pure metals Pugh, S. F. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 45, Issue 367 https://doi.org/10.1080/14786440808520496	journal	August 1954
An adaptive genetic algorithm for crystal structure prediction Wu, S. Q.; Ji, M.; Wang, C. Z. Journal of Physics: Condensed Matter, Vol. 26, Issue 3 https://doi.org/10.1088/0953-8984/26/3/035402	journal	December 2013
Ab initiomolecular dynamics for liquid metals Kresse, G.; Hafner, J. Physical Review B, Vol. 47, Issue 1, p. 558-561 https://doi.org/10.1103/PhysRevB.47.558	journal	January 1993
Projector augmented-wave method Blöchl, P. E. Physical Review B, Vol. 50, Issue 24, p. 17953-17979 https://doi.org/10.1103/PhysRevB.50.17953	journal	December 1994
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
From ultrasoft pseudopotentials to the projector augmented-wave method Kresse, G.; Joubert, D. Physical Review B, Vol. 59, Issue 3, p. 1758-1775 https://doi.org/10.1103/PhysRevB.59.1758	journal	January 1999
High-pressure high-temperature x-ray diffraction of β-boron to 30 GPa Ma, Yanzhang; Prewitt, Charles T.; Zou, Guangtian Physical Review B, Vol. 67, Issue 17 https://doi.org/10.1103/PhysRevB.67.174116	journal	May 2003
Symmetry-broken crystal structure of elemental boron at low temperature Widom, M.; Mihalkovič, M. Physical Review B, Vol. 77, Issue 6 https://doi.org/10.1103/PhysRevB.77.064113	journal	February 2008
Single-crystal studies of incommensurate Na to 1.5 Mbar Lundegaard, L. F.; Gregoryanz, E.; McMahon, M. I. Physical Review B, Vol. 79, Issue 6 https://doi.org/10.1103/PhysRevB.79.064105	journal	February 2009
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
Accuracy and transferability of Gaussian approximation potential models for tungsten Szlachta, Wojciech J.; Bartók, Albert P.; Csányi, Gábor Physical Review B, Vol. 90, Issue 10 https://doi.org/10.1103/PhysRevB.90.104108	journal	September 2014
Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential Behler, Jörg; Martoňák, Roman; Donadio, Davide Physical Review Letters, Vol. 100, Issue 18 https://doi.org/10.1103/PhysRevLett.100.185501	journal	May 2008
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi Physical Review Letters, Vol. 104, Issue 13 https://doi.org/10.1103/PhysRevLett.104.136403	journal	April 2010
New Ground-State Crystal Structure of Elemental Boron An, Qi; Reddy, K. Madhav; Xie, Kelvin Y. Physical Review Letters, Vol. 117, Issue 8 https://doi.org/10.1103/PhysRevLett.117.085501	journal	August 2016
On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B. Physical Review Letters, Vol. 120, Issue 2 https://doi.org/10.1103/PhysRevLett.120.026102	journal	January 2018
Data-Driven Learning of Total and Local Energies in Elemental Boron Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor Physical Review Letters, Vol. 120, Issue 15 https://doi.org/10.1103/PhysRevLett.120.156001	journal	April 2018
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Melting of Dense Sodium Gregoryanz, Eugene; Degtyareva, Olga; Somayazulu, Maddury Physical Review Letters, Vol. 94, Issue 18 https://doi.org/10.1103/PhysRevLett.94.185502	journal	May 2005
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
Constrained evolutionary algorithm for structure prediction of molecular crystals: methodology and applications Zhu, Qiang; Oganov, Artem R.; Glass, Colin W. Acta Crystallographica Section B Structural Science, Vol. 68, Issue 3 https://doi.org/10.1107/S0108768112017466	journal	May 2012
Structural Diversity of Sodium Gregoryanz, E.; Lundegaard, L. F.; McMahon, M. I. Science, Vol. 320, Issue 5879 https://doi.org/10.1126/science.1155715	journal	May 2008
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Shapeev, Alexander V. Multiscale Modeling & Simulation, Vol. 14, Issue 3 https://doi.org/10.1137/15M1054183	journal	January 2016
A new orthorhombic boron phase B _51.5–52 obtained by dehydrogenation of “α-tetragonal boron” Ekimov, Evgeny A.; Lebed, Yuliya B.; Uemura, Naoki Journal of Materials Research, Vol. 31, Issue 18 https://doi.org/10.1557/jmr.2016.209	journal	June 2016

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