Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems

Rostami, Samare; Amsler, Maximilian; Ghasemi, S. Alireza

doi:10.1063/1.5040005

Title: Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems

Journal Article · 24 September 2018 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/1.5040005 · OSTI ID:1543873

^[1]; Amsler, Maximilian ^[2]; Ghasemi, S. Alireza ^[1]

Inst. for Advanced Studies in Basic Sciences, Zanjan (Iran)
Cornell Univ., Ithaca, NY (United States)

Current machine-learning methods to reproduce ab initio potential energy landscapes suffer from an unfavorable computational scaling with respect to the number of chemical species. In this work, we propose a new approach by using optimized symmetry functions to explore similarities of structures in multicomponent systems in order to yield linear complexity. Here, we combine these symmetry functions with the charge equilibration via neural network technique, a reliable artificial neural network potential for ionic materials, and apply this method to study alkali-halide materials MX with 6 chemical species (M = {Li, Na, K} and X = {F, Cl, Br}). Our results show that our approach provides good agreement both with experimental and DFT reference data of many physical and structural properties for any chemical combination.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1543873

Journal Information:: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 12 Vol. 149; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

References (48)

Alchemical screening of ionic crystals Solovyeva, Alisa; von Lilienfeld, O. Anatole Physical Chemistry Chemical Physics, Vol. 18, Issue 45 https://doi.org/10.1039/c6cp04258a	journal	January 2016
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network Ghasemi, S. Alireza; Hofstetter, Albert; Saha, Santanu arXiv https://doi.org/10.48550/arxiv.1501.07344	text	January 2015
Two-Dimensional Hexagonal Sheet of TiO ₂ Eivari, Hossein Asnaashari; Ghasemi, S. Alireza; Tahmasbi, Hossein Chemistry of Materials, Vol. 29, Issue 20 https://doi.org/10.1021/acs.chemmater.7b02031	journal	August 2017
A fingerprint based metric for measuring similarities of crystalline structures Zhu, Li; Amsler, Maximilian; Fuhrer, Tobias AIP Publishing https://doi.org/10.5451/unibas-ep53947	text	January 2016
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
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network Ghasemi, S. Alireza; Hofstetter, Albert; Saha, Santanu Physical Review B, Vol. 92, Issue 4 https://doi.org/10.1103/physrevb.92.045131	journal	July 2015
Energy landscape of ZnO clusters and low-density polymorphs Rasoulkhani, Robabe; Tahmasbi, Hossein; Ghasemi, S. Alireza Physical Review B, Vol. 96, Issue 6 https://doi.org/10.1103/physrevb.96.064108	journal	August 2017
Alkali Halides Sirdeshmukh, D. B.; Sirdeshmukh, L.; Subhadra, K. G. Springer Series in Materials Science https://doi.org/10.1007/978-3-662-04341-7	book	January 2001
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
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials Gastegger, M.; Schwiedrzik, L.; Bittermann, M. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019667	journal	June 2018
Publisher’s Note: On representing chemical environments [Phys. Rev. B 87 , 184115 (2013)] Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 21 https://doi.org/10.1103/physrevb.87.219902	journal	June 2013
Fast and accurate modeling of molecular atomization energies with machine learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert American Physical Society https://doi.org/10.5451/unibas-ep43360	text	January 2012
Electronic structure of AlFeN films exhibiting crystallographic orientation change from c- to a-axis with Fe concentrations and annealing effect Tatemizo, Nobuyuki; Imada, Saki; Okahara, Kizuna Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-58835-5	journal	February 2020
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
Crystal Structure of Cold Compressed Graphite Amsler, Maximilian; Flores-Livas, José A.; Lehtovaara, Lauri Physical Review Letters, Vol. 108, Issue 6 https://doi.org/10.1103/physrevlett.108.065501	journal	February 2012
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Alchemical screening of ionic crystals Solovyevaa, Alisa; Von Lilienfeld, O. Anatole Royal Society of Chemistry https://doi.org/10.5451/unibas-ep53280	text	January 2016
Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systems Goedecker, Stefan The Journal of Chemical Physics, Vol. 120, Issue 21 https://doi.org/10.1063/1.1724816	journal	June 2004
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
Second-Sound Velocities in Cubic Crystals Varshni, Y. P.; Konti, A. Physical Review B, Vol. 6, Issue 4 https://doi.org/10.1103/physrevb.6.1532	journal	August 1972
Lattice parameters and lattice energies of high-pressure polymorphs of some alkali halides Weir, C. E.; Piermarini, G. J. Journal of Research of the National Bureau of Standards Section A: Physics and Chemistry, Vol. 68A, Issue 1 https://doi.org/10.6028/jres.068a.009	journal	January 1964
A New Approach to Linear Filtering and Prediction Problems Kalman, R. E. Journal of Basic Engineering, Vol. 82, Issue 1 https://doi.org/10.1115/1.3662552	journal	March 1960
High-resolution X-ray luminescence extension imaging Ou, Xiangyu; Qin, Xian; Huang, Bolong Nature, Vol. 590, Issue 7846 https://doi.org/10.1038/s41586-021-03251-6	journal	February 2021
Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand Physical Review B, Vol. 96, Issue 1 https://doi.org/10.1103/physrevb.96.014112	journal	July 2017
Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide Artrith, Nongnuch; Hiller, Björn; Behler, Jörg physica status solidi (b), Vol. 250, Issue 6 https://doi.org/10.1002/pssb.201248370	journal	November 2012
Crystal structure prediction using the minima hopping method Amsler, Maximilian; Goedecker, Stefan The Journal of Chemical Physics, Vol. 133, Issue 22 https://doi.org/10.1063/1.3512900	journal	December 2010
Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks Lorenz, Sönke; Groß, Axel; Scheffler, Matthias Chemical Physics Letters, Vol. 395, Issue 4-6 https://doi.org/10.1016/j.cplett.2004.07.076	journal	September 2004
A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models Rivals, Isabelle; Personnaz, Léon Neurocomputing, Vol. 20, Issue 1-3 https://doi.org/10.1016/s0925-2312(98)00021-6	journal	August 1998
Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals Daw, Murray S.; Baskes, M. I. Physical Review B, Vol. 29, Issue 12 https://doi.org/10.1103/physrevb.29.6443	journal	June 1984
Alkali Halides Bennewitz, Roland; Bammerlin, Martin; Meyer, Ernst Noncontact Atomic Force Microscopy https://doi.org/10.1007/978-3-642-56019-4_5	book	January 2002
A fingerprint based metric for measuring similarities of crystalline structures Zhu, Li; Amsler, Maximilian; Fuhrer, Tobias The Journal of Chemical Physics, Vol. 144, Issue 3 https://doi.org/10.1063/1.4940026	journal	January 2016
Crystal structure prediction using the Minima Hopping method Amsler, Maximilian; Goedecker, Stefan arXiv https://doi.org/10.48550/arxiv.1007.2003	text	January 2010
ReaxFF: A Reactive Force Field for Hydrocarbons van Duin, Adri C. T.; Dasgupta, Siddharth; Lorant, Francois The Journal of Physical Chemistry A, Vol. 105, Issue 41 https://doi.org/10.1021/jp004368u	journal	October 2001
Low-energy structures of zinc borohydride Zn(BH 4 ) 2 Huan, Tran Doan; Amsler, Maximilian; Tuoc, Vu Ngoc Physical Review B, Vol. 86, Issue 22 https://doi.org/10.1103/physrevb.86.224110	journal	December 2012
Atom-centered symmetry functions for constructing high-dimensional neural network potentials Behler, Jörg The Journal of Chemical Physics, Vol. 134, Issue 7 https://doi.org/10.1063/1.3553717	journal	February 2011
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert arXiv https://doi.org/10.48550/arxiv.1109.2618	text	January 2011
A particle-particle, particle-density algorithm for the calculation of electrostatic interactions of particles with slablike geometry Alireza Ghasemi, S.; Neelov, Alexey; Goedecker, Stefan The Journal of Chemical Physics, Vol. 127, Issue 22 https://doi.org/10.1063/1.2804382	journal	December 2007
Identification of Novel Cu, Ag, and Au Ternary Oxides from Global Structural Prediction Cerqueira, Tiago F. T.; Lin, Sun; Amsler, Maximilian Chemistry of Materials, Vol. 27, Issue 13 https://doi.org/10.1021/acs.chemmater.5b00716	journal	June 2015
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi arXiv https://doi.org/10.48550/arxiv.0910.1019	text	January 2009
High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride Faraji, Somayeh; Ghasemi, S. Alireza; Rostami, Samare Physical Review B, Vol. 95, Issue 10 https://doi.org/10.1103/physrevb.95.104105	journal	March 2017
Empirical force fields for biological macromolecules: Overview and issues Mackerell, Alexander D. Journal of Computational Chemistry, Vol. 25, Issue 13 https://doi.org/10.1002/jcc.20082	journal	January 2004
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/physrevlett.108.058301	journal	January 2012
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
ElaStic: A tool for calculating second-order elastic constants from first principles Golesorkhtabar, Rostam; Pavone, Pasquale; Spitaler, Jürgen Computer Physics Communications, Vol. 184, Issue 8 https://doi.org/10.1016/j.cpc.2013.03.010	journal	August 2013
WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials Gastegger, Michael; Schwiedrzik, Ludwig; Bittermann, Marius arXiv https://doi.org/10.48550/arxiv.1712.05861	text	January 2017
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
First principles phonon calculations in materials science Togo, Atsushi; Tanaka, Isao Scripta Materialia, Vol. 108 https://doi.org/10.1016/j.scriptamat.2015.07.021	journal	November 2015
First principles phonon calculations in materials science Togo, Atsushi; Tanaka, Isao arXiv https://doi.org/10.48550/arxiv.1506.08498	preprint	January 2015

Cited By (4)

Anharmonic Thermodynamics of Vacancies Using a Neural Network Potential Bochkarev, Anton S.; van Roekeghem, Ambroise; Mossa, Stefano arXiv https://doi.org/10.48550/arxiv.1909.10253	text	January 2019
Anharmonic thermodynamics of vacancies using a neural network potential Bochkarev, Anton S.; van Roekeghem, Ambroise; Mossa, Stefano Physical Review Materials, Vol. 3, Issue 9 https://doi.org/10.1103/physrevmaterials.3.093803	journal	September 2019
Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function Wang, Xiaocong; Gao, Jun RSC Advances, Vol. 10, Issue 2 https://doi.org/10.1039/c9ra09337k	journal	January 2020
Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2 Zhang, Yanhui; Lunghi, Alessandro; Sanvito, Stefano arXiv https://doi.org/10.48550/arxiv.1911.03307	preprint	January 2019

Similar Records

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Journal Article · 2019 · Physical Review B · OSTI ID:1577810

Wen, Tongqi; Wang, Cai-Zhuang; Kramer, M. J.; +8 more

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

Journal Article · 2022 · Journal of Materials Science · OSTI ID:1898519

Mannodi-Kanakkithodi, Arun; Chan, Maria K. Y.

DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning

Journal Article · 2020 · Journal of Physical Chemistry. C · OSTI ID:1774556

Tovey, Samuel; Narayanan Krishnamoorthy, Anand; Sivaraman, Ganesh; +4 more

Related Subjects

74
Chemistry
Physics

Title: Optimized symmetry functions for machine-learning interatomic potentials of multicomponent systems

Citation Formats

References (48)

Cited By (4)

Similar Records

Related Subjects