Journal of the Chinese Ceramic Society, Volume. 51, Issue 2, 552(2023)

Review on Machine Learning Accelerated Crystal Structure Prediction

LUO Xiaoshan1...2,*, WANG Zhenyu2,3, GAO Pengyue1,2, ZHANG Wei2, LV Jian1,2, and WANG Yanchao12 |Show fewer author(s)
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    References(107)

    [1] [1] STILLINGER F H. Exponential multiplicity of inherent structures[J]. Phys Rev E, 1999, 59(1): 48-51.

    [2] [2] STILLINGER F H. Inherent structures enumeration for low-density materials[J]. Phys Rev E, 2001, 63(1): 011110.

    [3] [3] WALES D J, DOYE J P K. Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms[J]. J Phys Chem, 1997, 101(28): 5111-5116.

    [4] [4] GLASS C W, OGANOV A R, HANSEN N. USPEX-Evolutionary crystal structure prediction[J]. Comput Phys Commun, 2006, 175(11/12): 713-720.

    [5] [5] OGANOV A R, GLASS C W. Crystal structure prediction using ab initio evolutionary techniques: Principles and applications[J]. J Chem Phys, 2006, 124(24): 244704.

    [6] [6] WANG Y, LV J, ZHU L, et al. Crystal structure prediction via particle-swarm optimization[J]. Phys Rev B, 2010, 82(9): 094116.

    [7] [7] WANG Y, LV J, ZHU L, et al. CALYPSO: A method for crystal structure prediction[J]. Comput Phys Commun, 2012, 183(10): 2063-2070.

    [8] [8] PICKARD C J, NEEDS R J. Ab initio random structure searching[J]. J Phys Condens Matter, 2011, 23(5): 053201.

    [9] [9] PICKARD C J, NEEDS R J. High-pressure phases of silane[J]. Phys Rev Lett, 2006, 97(4): 045504.

    [10] [10] MA Y, EREMETS M, OGANOV A R, et al. Transparent dense sodium[J]. Nature, 2009, 458(7235): 182-185.

    [11] [11] LV J, WANG Y, ZHU L, et al. Predicted novel high-pressure phases of lithium[J]. Phys Rev Lett, 2011, 106(1): 015503.

    [12] [12] WANG H, TSE J S, TANAKA K, et al. Superconductive sodalite-like clathrate calcium hydride at high pressures[J]. Proc National Acad Sci, 2012, 109(17): 6463-6466.

    [13] [13] PENG F, SUN Y, PICKARD C J, et al. Hydrogen clathrate structures in rare earth hydrides at high pressures: Possible route to room-temperature superconductivity[J]. Phys Rev Lett, 2017, 119(10): 107001.

    [14] [14] SCHMIDT J, MARQUES M R G, BOTTI S, et al. Recent advances and applications of machine learning in solid-state materials science[J]. Npj Comput Mater, 2019, 5(1): 83.

    [15] [15] WANG J, WANG Y, CHEN Y. Inverse design of materials by machine learning[J]. Materials, 2022, 15(5): 1811.

    [16] [16] WANG Y, LV J, GAO P, et al. Crystal structure prediction via efficient sampling of the potential energy surface[J]. Accounts Chem Res, 2022, 55(15): 2068-2076.

    [17] [17] YIN X, GOUNARIS C E. Search methods for inorganic materials crystal structure prediction[J]. Curr Opin Chem Eng, 2022, 35: 100726.

    [18] [18] KOCER E, KO T W, BEHLER J. Neural network potentials: A concise overview of methods[J]. Annu Rev Phys Chem, 2022, 73(1): 1-24.

    [19] [19] BEHLER J. Four generations of high-dimensional neural network potentials[J]. Chem Rev, 2021, 121(16): 10037-10072.

    [20] [20] FUHR A S, SUMPTER B G. Deep generative models for materials discovery and machine learning-accelerated innovation[J]. Front Mater, 2022, 9: 865270.

    [21] [21] CHEN L, ZHANG W, NIE Z, et al. Generative models for inverse design of inorganic solid materials[J]. J Mater Inform, 2021, 1: 4.

    [22] [22] HOHENBERG P, KOHN W. Inhomogeneous electron gas[J]. Phys Rev, 1964, 136(3B): B864-B871.

    [23] [23] KOHN W, SHAM L J. Self-consistent equations including exchange and correlation effects[J]. Phys Rev, 1965, 140(4A): A1133-A1138.

    [24] [24] LENNARD-JONES J E. Cohesion[J]. P Phys Soc, 1931, 43(5): 461-482.

    [25] [25] BUCKINGHAM R A. The classical equation of state of gaseous helium, neon and argon[J]. Proc Royal Soc Lond Ser Math Phys Sci, 1938, 168(933): 264-283.

    [26] [26] STILLINGER F H, WEBER T A. Computer simulation of local order in condensed phases of silicon[J]. Phys Rev B, 1984, 31(8): 5262-5271.

    [27] [27] DAW M S, BASKES M I. Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals [J]. Phys Rev B, 1984, 29(12): 6443-6453.

    [28] [28] THOMPSON A P, AKTULGA H M, BERGER R, et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales[J]. Comput Phys Commun, 2022, 271: 108171.

    [29] [29] BEHLER J. Perspective: Machine learning potentials for atomistic simulations[J]. J Chem Phys, 2016, 145(17): 170901.

    [30] [30] TONG Q, XUE L, LV J, et al. Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface[J]. Faraday Discuss, 2018, 211(0): 31-43.

    [31] [31] GUBAEV K, PODRYABINKIN E V, HART G L W, et al. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials[J]. Comp Mater Sci, 2019, 156: 148-156.

    [32] [32] BLANK T B, BROWN S D, CALHOUN A W, et al. Neural network models of potential energy surfaces[J]. J Chem Phys, 1995, 103(10): 4129-4137.

    [33] [33] BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces[J]. Phys Rev Lett, 2007, 98(14): 146401.

    [34] [34] BEHLER J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials[J]. J Chem Phys, 2011, 134(7): 074106.

    [35] [35] KHALIULLIN R Z, ESHET H, K?HNE T D, et al. Nucleation mechanism for the direct graphite-to-diamond phase transition[J]. Nat Mater: 2011, 10(9): 693-697.

    [36] [36] ARTRITH N, URBAN A. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2[J]. Comp Mater Sci: 2016, 114: 135-150.

    [37] [37] BEHLER J. Four generations of high-dimensional neural network potentials[J]. Chem Rev, 2021, 121(16): 10037-10072.

    [38] [38] BART?K A P, KONDOR R, CS?NYI G. On representing chemical environments[J]. Phys Rev B, 2013, 87(18): 184115.

    [39] [39] BART?K A P, PAYNE M C, KONDOR R, et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons[J]. Phys Rev Lett, 2010, 104(13): 136403.

    [40] [40] DERINGER V L, CS?NYI G, PROSERPIO D M. Extracting crystal chemistry from amorphous carbon structures[J]. ChemPhysChem, 2017, 18(8): 873-877.

    [41] [41] DERINGER V L, CS?NYI G. Machine learning based interatomic potential for amorphous carbon[J]. Phys Rev B, 2017, 95(9): 094203.

    [42] [42] ROWE P, DERINGER V L, GASPAROTTO P, et al. An accurate and transferable machine learning potential for carbon[J]. J Chem Phys, 2020, 153(3): 034702.

    [43] [43] DERINGER V L, PICKARD C J, CS?NYI G. Data-driven learning of total and local energies in elemental boron[J]. Phys Rev Lett, 2018, 120(15): 156001.

    [44] [44] SZLACHTA W J, BART?K A P, CS?NYI G. Accuracy and transferability of Gaussian approximation potential models for tungsten[J]. Phys Rev B, 2014, 90(10): 104108.

    [45] [45] DRAGONI D, DAFF T D, CS?NYI G, et al. Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron[J]. Phys Rev Mater, 2018, 2(1): 013808.

    [46] [46] DERINGER V L, BARTO?K A P, BERNSTEIN N, et al. Gaussian process regression for materials and molecules[J]. Chem Rev: 2021, 121(16): 10073-10141.

    [47] [47] SHAPEEV A V. Moment tensor potentials: A class of systematically improvable interatomic potentials[J]. Multiscale Model Sim, 2016, 14(3): 1153-1173.

    [48] [48] NOVIKOV I S, GUBAEV K, PODRYABINKIN E V, et al. The MLIP package: moment tensor potentials with MPI and active learning[J]. Mach Learn Sci Technology, 2021, 2(2): 025002.

    [49] [49] CHEN C, DENG Z, TRAN R, et al. Accurate force field for molybdenum by machine learning large materials data[J]. Phys Rev Mater, 2017, 1(4): 043603.

    [50] [50] LI X-G, HU C, CHEN C, et al. Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals[J]. Phys Rev B, 2018, 98(9): 094104.

    [51] [51] DENG Z, CHEN C, LI X G, et al. An electrostatic spectral neighbor analysis potential for lithium nitride[J]. NPJ Comput Mater, 2019, 5(1): 75.

    [52] [52] WOOD M A, CUSENTINO M A, WIRTH B D, et al. Data-driven material models for atomistic simulation[J]. Phys Rev B, 2019, 99(18): 184305.

    [53] [53] ZHANG L, HAN J, WANG H, et al. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, CANADA, 2018: 4441-4451.

    [54] [54] HAN J, ZHANG L, CAR R, et al. Deep potential: A general representation of a many-body potential energy surface[J]. Commun Comput Phys, 2018, 23(3): 629-639.

    [55] [55] ZHANG L, HAN J, WANG H, et al. Deep Potential molecular dynamics: a scalable model with the accuracy of quantum mechanics[J]. Phys Rev Lett, 2018, 120(14): 143001.

    [56] [56] WEN T, ZHANG L, WANG H, et al. Deep potentials for materials science[J]. Mater Futur, 2022, 1(2): 022601.

    [57] [57] JIA W, WANG H, CHEN M, et al. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning[J]. Arxiv, 2020. Doi:10.48550/arXiv.2005.00223.

    [58] [58] XIE T, GROSSMAN J C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties[J]. Phys Rev Lett, 2018, 120(14): 145301.

    [59] [59] TAKAHASHI K, TAKAHASHI L. Creating machine learning-driven material recipes based on crystal structure[J]. J Phys Chem Lett, 2019, 10(2): 283-288.

    [60] [60] DERINGER V L, CARO M A, CS?NYI G. Machine learning interatomic potentials as emerging tools for materials science[J]. Adv Mater, 2019, 31(46): 1902765.

    [61] [61] OUYANG R, XIE Y, JIANG D. Global minimization of gold clusters by combining neural network potentials and the basin-hopping method[J]. Nanoscale, 2015, 7(36): 14817-14821.

    [62] [62] JINDAL S, CHIRIKI S, BULUSU S S. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster[J]. J Chem Phys, 2017, 146(20): 204301.

    [63] [63] CHIRIKI S, JINDAL S, BULUSU S S. c-T phase diagram and Landau free energies of (AgAu)55 nanoalloy via neural-network molecular dynamic simulations[J]. J Chem Phys, 2017, 147(15): 154303.

    [64] [64] CHIRIKI S, JINDAL S, BULUSU S S. Neural network potentials for dynamics and thermodynamics of gold nanoparticles[J]. J Chem Phys, 2017, 146(8): 084314.

    [65] [65] HAJINAZAR S, SANDOVAL E D, CULLO A J, et al. Multitribe evolutionary search for stable Cu-Pd-Ag nanoparticles using neural network models[J]. Phys Chem Chem Phys, 2019, 21(17): 8729-8742.

    [66] [66] THORN A, ROJAS-NUNEZ J, HAJINAZAR S, et al. Toward ab initio ground states of gold clusters via neural network modeling[J]. J Phys Chem C, 2019, 123(50): 30088-30098.

    [67] [67] WANG H, ZHANG Y, ZHANG L, et al. Crystal structure prediction of binary alloys via deep potential[J]. Front Chem, 2020, 8: 589795.

    [68] [68] ZHANG L, LIN D-Y, WANG H, et al. Active learning of uniformly accurate interatomic potentials for materials simulation[J]. Phys Rev Mater, 2019, 3(2): 023804.

    [69] [69] DERINGER V L, PICKARD C J, CS?NYI G. Data-driven learning of total and local energies in elemental boron[J]. Phys Rev Lett, 2018, 120(15): 156001.

    [70] [70] DERINGER V L, PROSERPIO D M, CS?NYI G, et al. Data-driven learning and prediction of inorganic crystal structures[J]. Faraday Discuss, 2018, 211(0): 45-59.

    [71] [71] BERNSTEIN N, CS?NYI G, DERINGER V L. De novo exploration and self-guided learning of potential-energy surfaces[J]. Npj Comput Mater, 2019, 5(1): 99.

    [72] [72] TONG Q, GAO P, LIU H, et al. Combining machine learning potential and structure prediction for accelerated materials design and discovery[J]. J Phys Chem Lett, 2020, 11(20): 8710-8720.

    [73] [73] PODRYABINKIN E V, TIKHONOV E V, SHAPEEV A V, et al. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning[J]. Phys Rev B, 2019, 99(6): 064114.

    [74] [74] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative Adversarial Networks[C]//Advances in Neural Information Processing Systems 27, Montréal, CANADA, 2014: 2672-2680.

    [75] [75] KINGMA D P, WELLING M. Auto-encoding variational bayes[J]. Arxiv, 2014. Doi:10.48550/arxiv.1312.6114.

    [76] [76] REZENDE D J, MOHAMED S. Variational Inference with Normalizing Flows[C]//Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015: 1530-1538.

    [77] [77] HO J, JAIN A, ABBEEL P. Denoising Diffusion Probabilistic Models[C]//Advances in Neural Information Processing Systems 33, Virtual, 2020: 6840-6851.

    [78] [78] SOHL-DICKSTEIN J, WEISS E A, MAHESWARANATHAN N, et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics[C]//Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015: 2256-2265.

    [79] [79] RYAN K, LENGYEL J, SHATRUK M. Crystal structure prediction via deep learning[J]. J Am Chem Soc, 2018, 140(32): 10158-10168.

    [80] [80] NOUIRA A, SOKOLOVSKA N, CRIVELLO J-C. CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks[C]//Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering, California, USA, 2018: doi:10.48550/arxiv.1810.11203.

    [81] [81] KIM S, NOH J, GU G H, et al. Generative adversarial networks for crystal structure prediction[J]. Acs Central Sci, 2020, 6(8): 1412-1420.

    [82] [82] ZHAO Y, AL‐FAHDI M, HU M, et al. High‐throughput discovery of novel cubic crystal materials using deep generative neural networks[J]. Adv Sci, 2021, 8(20): 2100566.

    [83] [83] REN Z, TIAN S I P, NOH J, et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties[J]. Matter, 2022, 5(1): 314-335.

    [84] [84] HOFFMANN J, MAESTRATI L, SAWADA Y, et al. Data-driven approach to encoding and decoding 3-D crystal structures[J]. Arxiv: 2019, doi:10.48550/arxiv.1909.00949.

    [85] [85] NOH J, KIM J, STEIN H S, et al. Inverse design of solid-state materials via a continuous representation[J]. Matter, 2019, 1(5): 1370-1384.

    [86] [86] KIM B, LEE S, KIM J. Inverse design of porous materials using artificial neural networks[J]. Sci Adv, 2020, 6(1): eaax9324.

    [87] [87] COURT C J, YILDIRIM B, JAIN A, et al. 3D inorganic crystal structure generation and property prediction via representation learning[J]. J Chem Inf Model, 2020, 60(10): 4518-4535.

    [88] [88] LONG T, FORTUNATO N M, OPAHLE I, et al. Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures[J]. NPJ Comput Mater, 2021, 7(1): 66.

    [89] [89] XIE T, FU X, GANEA O-E, et al. Crystal Diffusion Variational Autoencoder for Periodic Material Generation[C]//The Tenth International Conference on Learning Representations (ICLR 2022), Virtual, 2022. Doi:10.48550/arxiv.2110.06197.

    [90] [90] KIM S, NOH J, GU G H, et al. Generative adversarial networks for crystal structure prediction[J]. Acs Central Sci, 2020, 6(8): 1412-1420.

    [91] [91] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN[J]. Arxiv, 2017. Doi:10.48550/arxiv.1701.07875.

    [92] [92] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved Training of Wasserstein GANs[C]//Advances in Neural Information Processing Systems 30, Los Angeles, USA, 2017: 5767-5777.

    [93] [93] REN Z, TIAN S I P, NOH J, et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties[J]. Matter, 2022, 5(1): 314-335.

    [94] [94] RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional Networks for Biomedical Image Segmentation[C]//Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234-241.

    [95] [95] OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-Net: learning where to look for the pancreas[J]. Arxiv, 2018. Doi:10.48550/arxiv.1804.03999.

    [96] [96] SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model[J]. Ieee T Neural Networ, 2009, 20(1): 61-80.

    [97] [97] GILMER J, SCHOENHOLZ S S, RILEY P F, et al. Neural Message Passing for Quantum Chemistry[C]//Proceedings of the 34th International Conference on Machine Learning - Volume 70, 2017: 1263-1272.

    [98] [98] BATTAGLIA P W, HAMRICK J B, BAPST V, et al. How good is my GAN?[J]. Arxiv:,2018. Doi:10.48550/arxiv.1807.09499.

    [99] [99] THOMAS N, SMIDT T, KEARNES S, et al. Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds[J]. Arxiv, 2018. Doi:10.48550/arxiv.1802.08219.

    [100] [100] FUCHS F B, WORRALL D E, FISCHER V, et al. SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks[C]//Advances in Neural Information Processing Systems 33, Virtual, 2020: 1970-1981.

    [101] [101] SATORRAS V G, HOOGEBOOM E, WELLING M. E(n) Equivariant Graph Neural Networks[C]//Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021: 9323-9332.

    [102] [102] KLIPFEL A, BOURAOUI Z, FRE?GIER Y, et al. Equivariant graph neural network for crystalline materials[C]//Proceedings of the 1st International Workshop on Spatio-Temporal Reasoning and Learning, Vienna, Austria, 2022: doi:10.48550/arxiv.2102.09844.

    [103] [103] WANG S, GUO X, ZHAO L. Deep Generative Model for Periodic Graphs[J]. Arxiv, 2022. Doi:10.48550/arxiv.2201.11932.

    [104] [104] JAIN A, ONG S P, HAUTIER G, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation[J]. Apl Mater, 2013, 1(1): 011002.

    [105] [105] YUE L, BIRU G, XINXIN Z, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Materials, 2020, 31: 434-450.

    [106] [106] SIQI S, ZHANGWEI T, XINXIN Z, et al. Applying data-driven machine learning to studying electrochemical energy storage materials[J]. Energy Storage Sci Technol, 2022, 11(3): 739.

    [107] [107] YUE L, XINXIN Z, ZHENGWEI Y, et al. Machine learning embedded with materials domain knowledge[J]. J Chin Ceram Soc, 2022, 50(3): 863-876.

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    LUO Xiaoshan, WANG Zhenyu, GAO Pengyue, ZHANG Wei, LV Jian, WANG Yanchao. Review on Machine Learning Accelerated Crystal Structure Prediction[J]. Journal of the Chinese Ceramic Society, 2023, 51(2): 552

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    Paper Information

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    Received: Oct. 14, 2022

    Accepted: --

    Published Online: Mar. 11, 2023

    The Author Email: Xiaoshan LUO (luoxs21@mails.jlu.edu.cn)

    DOI:10.14062/j.issn.0454-5648.20220835

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