Extending the accuracy of the SNAP interatomic potential form

Wood, Mitchell A.; Thompson, Aidan P.

doi:10.1063/1.5017641

Title: Extending the accuracy of the SNAP interatomic potential form

Journal Article · Wed Mar 28 00:00:00 EDT 2018 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.5017641· OSTI ID:1429723

Wood, Mitchell A. ^[1];

^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. It is also argued that the quadratic SNAP form is a special case of an artificial neural network (ANN). The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similarly to ANN potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting, as measured by cross-validation analysis.

View Accepted Manuscript (DOE)

View Accepted Manuscript (Publisher)

Cite

Export

Save

Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; 17-SC-20-SC; NA0003525

OSTI ID:: 1429723

Alternate ID(s):: OSTI ID: 1430387

Report Number(s):: SAND-2017-13691J; 659612; TRN: US1802482

Journal Information:: Journal of Chemical Physics, Vol. 148, Issue 24; ISSN 0021-9606

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

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 103 works

Citation information provided by
Web of Science

References (41)

Energy conserving, linear scaling Born-Oppenheimer molecular dynamics Cawkwell, M. J.; Niklasson, Anders M. N. The Journal of Chemical Physics, Vol. 137, Issue 13 https://doi.org/10.1063/1.4755991	journal	October 2012
Ab initio calculations of grain boundaries in bcc metals Scheiber, Daniel; Pippan, Reinhard; Puschnig, Peter Modelling and Simulation in Materials Science and Engineering, Vol. 24, Issue 3 https://doi.org/10.1088/0965-0393/24/3/035013	journal	March 2016
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations Rappe, A. K.; Casewit, C. J.; Colwell, K. S. Journal of the American Chemical Society, Vol. 114, Issue 25, p. 10024-10035 https://doi.org/10.1021/ja00051a040	journal	December 1992
Gaussian approximation potentials: A brief tutorial introduction Bartók, Albert P.; Csányi, Gábor International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24927	journal	April 2015
Representing potential energy surfaces by high-dimensional neural network potentials Behler, J. Journal of Physics: Condensed Matter, Vol. 26, Issue 18 https://doi.org/10.1088/0953-8984/26/18/183001	journal	April 2014
The ReaxFF reactive force-field: development, applications and future directions Senftle, Thomas P.; Hong, Sungwook; Islam, Md Mahbubul npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2015.11	journal	March 2016
Charge equilibration for molecular dynamics simulations Rappe, Anthony K.; Goddard, William A. The Journal of Physical Chemistry, Vol. 95, Issue 8 https://doi.org/10.1021/j100161a070	journal	April 1991
Permutation-invariant distance between atomic configurations Ferré, Grégoire; Maillet, Jean-Bernard; Stoltz, Gabriel The Journal of Chemical Physics, Vol. 143, Issue 10 https://doi.org/10.1063/1.4930541	journal	September 2015
Computational aspects of many-body potentials Plimpton, Steven J.; Thompson, Aidan P. MRS Bulletin, Vol. 37, Issue 5 https://doi.org/10.1557/mrs.2012.96	journal	May 2012
Interatomic Forces in Condensed Matter Finnis, Mike https://doi.org/10.1093/acprof:oso/9780198509776.001.0001	book	October 2003
An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters Jindal, Shweta; Bulusu, Satya S. Chemical Physics Letters, Vol. 693 https://doi.org/10.1016/j.cplett.2018.01.023	journal	February 2018
Molecular potential energy surfaces by interpolation Ischtwan, Josef; Collins, Michael A. The Journal of Chemical Physics, Vol. 100, Issue 11 https://doi.org/10.1063/1.466801	journal	June 1994
Accurate interatomic force fields via machine learning with covariant kernels Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro Physical Review B, Vol. 95, Issue 21 https://doi.org/10.1103/physrevb.95.214302	journal	June 2017
Fast Parallel Algorithms for Short-Range Molecular Dynamics Plimpton, Steve Journal of Computational Physics, Vol. 117, Issue 1 https://doi.org/10.1006/jcph.1995.1039	journal	March 1995
A reactive potential for hydrocarbons with intermolecular interactions Stuart, Steven J.; Tutein, Alan B.; Harrison, Judith A. The Journal of Chemical Physics, Vol. 112, Issue 14 https://doi.org/10.1063/1.481208	journal	April 2000
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
Cohesion Lennard-Jones, J. E. Proceedings of the Physical Society, Vol. 43, Issue 5 https://doi.org/10.1088/0959-5309/43/5/301	journal	September 1931
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
Application of the modified Shepard interpolation method to the determination of the potential energy surface for a molecule–surface reaction: H2+Pt(111) Crespos, C.; Collins, M. A.; Pijper, E. The Journal of Chemical Physics, Vol. 120, Issue 5 https://doi.org/10.1063/1.1637337	journal	February 2004
Comparing molecules and solids across structural and alchemical space De, Sandip; Bartók, Albert P.; Csányi, Gábor Physical Chemistry Chemical Physics, Vol. 18, Issue 20 https://doi.org/10.1039/c6cp00415f	journal	January 2016
Machine learning of molecular electronic properties in chemical compound space Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand New Journal of Physics, Vol. 15, Issue 9 https://doi.org/10.1088/1367-2630/15/9/095003	journal	September 2013
Multi-dimensional potential energy surface determination by modified Shepard interpolation for a molecule–surface reaction: H2+Pt(111) Crespos, C.; Collins, M. A.; Pijper, E. Chemical Physics Letters, Vol. 376, Issue 5-6 https://doi.org/10.1016/s0009-2614(03)01033-9	journal	July 2003
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations Behler, Jörg Physical Chemistry Chemical Physics, Vol. 13, Issue 40 https://doi.org/10.1039/c1cp21668f	journal	January 2011
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24912	journal	April 2015
The Art and Science of an Analytic Potential Brenner, D. W. physica status solidi (b), Vol. 217, Issue 1 https://doi.org/10.1002/(sici)1521-3951(200001)217:1<23::aid-pssb23>3.0.co;2-n	journal	January 2000
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
A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons Brenner, Donald W.; Shenderova, Olga A.; Harrison, Judith A. Journal of Physics: Condensed Matter, Vol. 14, Issue 4 https://doi.org/10.1088/0953-8984/14/4/312	journal	January 2002
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost The University of North Carolina at Chapel Hill University Libraries https://doi.org/10.17615/bhbf-9r93	text	January 2017
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties Von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias Wiley https://doi.org/10.5451/unibas-ep53267	text	January 2015
Machine learning based interatomic potential for amorphous carbon Deringer, Volker; Csanyi, Gabor Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.7577	text	January 2017
Machine learning of molecular electronic properties in chemical compound space Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand ETH Zurich https://doi.org/10.3929/ethz-b-000071765	text	January 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
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
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
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias arXiv https://doi.org/10.48550/arxiv.1307.2918	preprint	January 2013
Gaussian Approximation Potentials: a brief tutorial introduction Bartók, Albert P.; Csányi, Gábor arXiv https://doi.org/10.48550/arxiv.1502.01366	preprint	January 2015
Permutation-invariant distance between atomic configurations Ferre, Gregoire; Maillet, Jean-Bernard; Stoltz, Gabriel arXiv https://doi.org/10.48550/arxiv.1507.02911	text	January 2015
Comparing molecules and solids across structural and alchemical space De, Sandip; Bartók, Albert P.; Csányi, Gábor arXiv https://doi.org/10.48550/arxiv.1601.04077	text	January 2016
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data Chen, Chi; Deng, Zhi; Tran, Richard arXiv https://doi.org/10.48550/arxiv.1706.09122	text	January 2017
Machine learning of molecular electronic properties in chemical compound space Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand IOP Publishing https://doi.org/10.5451/unibas-ep53261	text	January 2013

Cited By (15)

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor Advanced Materials, Vol. 31, Issue 46 https://doi.org/10.1002/adma.201902765	journal	September 2019
A Critical Review of Machine Learning of Energy Materials Chen, Chi; Zuo, Yunxing; Ye, Weike Advanced Energy Materials, Vol. 10, Issue 8 https://doi.org/10.1002/aenm.201903242	journal	January 2020
Physically informed artificial neural networks for atomistic modeling of materials Pun, G. P. Purja; Batra, R.; Ramprasad, R. Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10343-5	journal	May 2019
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression Hernandez, Alberto; Balasubramanian, Adarsh; Yuan, Fenglin npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0249-1	journal	November 2019
Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide Li, Wenwen; Ando, Yasunobu Physical Chemistry Chemical Physics, Vol. 20, Issue 47 https://doi.org/10.1039/c8cp04508a	journal	January 2018
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry Rupp, Matthias; von Lilienfeld, O. Anatole; Burke, Kieron The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5043213	journal	June 2018
Deep learning inter-atomic potential model for accurate irradiation damage simulations Wang, Hao; Guo, Xun; Zhang, Linfeng Applied Physics Letters, Vol. 114, Issue 24 https://doi.org/10.1063/1.5098061	journal	June 2019
Machine learning for interatomic potential models Mueller, Tim; Hernandez, Alberto; Wang, Chuhong The Journal of Chemical Physics, Vol. 152, Issue 5 https://doi.org/10.1063/1.5126336	journal	February 2020
Machine Learning a General-Purpose Interatomic Potential for Silicon Bartók, Ap; Kermode, J.; Bernstein, N. Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.34909	text	January 2018
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor Wiley https://doi.org/10.17863/cam.43999	journalarticle	January 2019
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential Seko, Atsuto; Togo, Atsushi; Tanaka, Isao arXiv https://doi.org/10.48550/arxiv.1901.02118	text	January 2019
Data-driven Material Models for Atomistic Simulation Wood, Mitchell A.; Cusentino, Mary Alice; Wirth, Brian D. arXiv https://doi.org/10.48550/arxiv.1902.09395	text	January 2019
Deep learning inter-atomic potential model for accurate irradiation damage simulations Wang, Hao; Guo, Xun; Zhang, Linfeng arXiv https://doi.org/10.48550/arxiv.1904.00360	text	January 2019
Physically-informed artificial neural networks for atomistic modeling of materials Pun, G. P. Purja; Batra, R.; Ramprasad, R. arXiv https://doi.org/10.48550/arxiv.1808.01696	text	January 2018

Similar Records

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Journal Article · Sun Mar 15 00:00:00 EDT 2015 · Journal of Computational Physics · OSTI ID:1429723

Thompson, Aidan P.; Swiler, Laura P.; Trott, Christian R.; +2 more

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Journal Article · Sun Mar 15 00:00:00 EDT 2015 · Journal of Computational Physics · OSTI ID:1429723

Automated Algorithms for Quantum-Level Accuracy in Atomistic Simulations: LDRD Final Report.

Technical Report · Mon Sep 01 00:00:00 EDT 2014 · OSTI ID:1429723

Thompson, Aidan Patrick; Schultz, Peter Andrew; Crozier, Paul; +6 more

Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS

Title: Extending the accuracy of the SNAP interatomic potential form

Citation Formats

References (41)

Cited By (15)

Similar Records

Related Subjects