Extending the accuracy of the SNAP interatomic potential form
Abstract
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.
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1429723
- Alternate Identifier(s):
- OSTI ID: 1430387
- Report Number(s):
- SAND-2017-13691J
Journal ID: ISSN 0021-9606; 659612; TRN: US1802482
- Grant/Contract Number:
- AC04-94AL85000; 17-SC-20-SC; NA0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 148; Journal Issue: 24; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 74 ATOMIC AND MOLECULAR PHYSICS
Citation Formats
Wood, Mitchell A., and Thompson, Aidan P. Extending the accuracy of the SNAP interatomic potential form. United States: N. p., 2018.
Web. doi:10.1063/1.5017641.
Wood, Mitchell A., & Thompson, Aidan P. Extending the accuracy of the SNAP interatomic potential form. United States. https://doi.org/10.1063/1.5017641
Wood, Mitchell A., and Thompson, Aidan P. Wed .
"Extending the accuracy of the SNAP interatomic potential form". United States. https://doi.org/10.1063/1.5017641. https://www.osti.gov/servlets/purl/1429723.
@article{osti_1429723,
title = {Extending the accuracy of the SNAP interatomic potential form},
author = {Wood, Mitchell A. and Thompson, Aidan P.},
abstractNote = {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.},
doi = {10.1063/1.5017641},
journal = {Journal of Chemical Physics},
number = 24,
volume = 148,
place = {United States},
year = {Wed Mar 28 00:00:00 EDT 2018},
month = {Wed Mar 28 00:00:00 EDT 2018}
}
Web of Science
Works referenced in this record:
Energy conserving, linear scaling Born-Oppenheimer molecular dynamics
journal, October 2012
- Cawkwell, M. J.; Niklasson, Anders M. N.
- The Journal of Chemical Physics, Vol. 137, Issue 13
Ab initio calculations of grain boundaries in bcc metals
journal, March 2016
- Scheiber, Daniel; Pippan, Reinhard; Puschnig, Peter
- Modelling and Simulation in Materials Science and Engineering, Vol. 24, Issue 3
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
journal, December 1992
- Rappe, A. K.; Casewit, C. J.; Colwell, K. S.
- Journal of the American Chemical Society, Vol. 114, Issue 25, p. 10024-10035
Gaussian approximation potentials: A brief tutorial introduction
journal, April 2015
- Bartók, Albert P.; Csányi, Gábor
- International Journal of Quantum Chemistry, Vol. 115, Issue 16
Representing potential energy surfaces by high-dimensional neural network potentials
journal, April 2014
- Behler, J.
- Journal of Physics: Condensed Matter, Vol. 26, Issue 18
The ReaxFF reactive force-field: development, applications and future directions
journal, March 2016
- Senftle, Thomas P.; Hong, Sungwook; Islam, Md Mahbubul
- npj Computational Materials, Vol. 2, Issue 1
Charge equilibration for molecular dynamics simulations
journal, April 1991
- Rappe, Anthony K.; Goddard, William A.
- The Journal of Physical Chemistry, Vol. 95, Issue 8
Permutation-invariant distance between atomic configurations
journal, September 2015
- Ferré, Grégoire; Maillet, Jean-Bernard; Stoltz, Gabriel
- The Journal of Chemical Physics, Vol. 143, Issue 10
Computational aspects of many-body potentials
journal, May 2012
- Plimpton, Steven J.; Thompson, Aidan P.
- MRS Bulletin, Vol. 37, Issue 5
An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters
journal, February 2018
- Jindal, Shweta; Bulusu, Satya S.
- Chemical Physics Letters, Vol. 693
Molecular potential energy surfaces by interpolation
journal, June 1994
- Ischtwan, Josef; Collins, Michael A.
- The Journal of Chemical Physics, Vol. 100, Issue 11
Accurate interatomic force fields via machine learning with covariant kernels
journal, June 2017
- Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro
- Physical Review B, Vol. 95, Issue 21
Fast Parallel Algorithms for Short-Range Molecular Dynamics
journal, March 1995
- Plimpton, Steve
- Journal of Computational Physics, Vol. 117, Issue 1
A reactive potential for hydrocarbons with intermolecular interactions
journal, April 2000
- Stuart, Steven J.; Tutein, Alan B.; Harrison, Judith A.
- The Journal of Chemical Physics, Vol. 112, Issue 14
ReaxFF: A Reactive Force Field for Hydrocarbons
journal, October 2001
- van Duin, Adri C. T.; Dasgupta, Siddharth; Lorant, Francois
- The Journal of Physical Chemistry A, Vol. 105, Issue 41
Cohesion
journal, September 1931
- Lennard-Jones, J. E.
- Proceedings of the Physical Society, Vol. 43, Issue 5
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
journal, March 2015
- Thompson, A. P.; Swiler, L. P.; Trott, C. R.
- Journal of Computational Physics, Vol. 285
Application of the modified Shepard interpolation method to the determination of the potential energy surface for a molecule–surface reaction: H2+Pt(111)
journal, February 2004
- Crespos, C.; Collins, M. A.; Pijper, E.
- The Journal of Chemical Physics, Vol. 120, Issue 5
Comparing molecules and solids across structural and alchemical space
journal, January 2016
- De, Sandip; Bartók, Albert P.; Csányi, Gábor
- Physical Chemistry Chemical Physics, Vol. 18, Issue 20
Machine learning of molecular electronic properties in chemical compound space
journal, September 2013
- Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand
- New Journal of Physics, Vol. 15, Issue 9
Multi-dimensional potential energy surface determination by modified Shepard interpolation for a molecule–surface reaction: H2+Pt(111)
journal, July 2003
- Crespos, C.; Collins, M. A.; Pijper, E.
- Chemical Physics Letters, Vol. 376, Issue 5-6
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
journal, January 2011
- Behler, Jörg
- Physical Chemistry Chemical Physics, Vol. 13, Issue 40
Perspective: Machine learning potentials for atomistic simulations
journal, November 2016
- Behler, Jörg
- The Journal of Chemical Physics, Vol. 145, Issue 17
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
journal, April 2015
- von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias
- International Journal of Quantum Chemistry, Vol. 115, Issue 16
The Art and Science of an Analytic Potential
journal, January 2000
- Brenner, D. W.
- physica status solidi (b), Vol. 217, Issue 1
Active learning of linearly parametrized interatomic potentials
journal, December 2017
- Podryabinkin, Evgeny V.; Shapeev, Alexander V.
- Computational Materials Science, Vol. 140
A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons
journal, January 2002
- Brenner, Donald W.; Shenderova, Olga A.; Harrison, Judith A.
- Journal of Physics: Condensed Matter, Vol. 14, Issue 4
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
text, January 2017
- ,
- The University of North Carolina at Chapel Hill University Libraries
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
text, January 2015
- Von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias
- Wiley
Machine learning based interatomic potential for amorphous carbon
text, January 2017
- Deringer, Volker; Csanyi, Gabor
- Apollo - University of Cambridge Repository
Machine learning of molecular electronic properties in chemical compound space
text, January 2013
- Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand
- ETH Zurich
Fast and accurate modeling of molecular atomization energies with machine learning
text, January 2012
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- American Physical Society
Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
text, January 2009
- Bartók, Albert P.; Payne, Mike C.; Kondor, Risi
- arXiv
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
text, January 2011
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- arXiv
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
preprint, January 2013
- von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias
- arXiv
Gaussian Approximation Potentials: a brief tutorial introduction
preprint, January 2015
- Bartók, Albert P.; Csányi, Gábor
- arXiv
Permutation-invariant distance between atomic configurations
text, January 2015
- Ferre, Gregoire; Maillet, Jean-Bernard; Stoltz, Gabriel
- arXiv
Comparing molecules and solids across structural and alchemical space
text, January 2016
- De, Sandip; Bartók, Albert P.; Csányi, Gábor
- arXiv
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
text, January 2017
- Chen, Chi; Deng, Zhi; Tran, Richard
- arXiv
Machine learning of molecular electronic properties in chemical compound space
text, January 2013
- Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand
- IOP Publishing
Works referencing / citing this record:
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
journal, September 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Advanced Materials, Vol. 31, Issue 46
A Critical Review of Machine Learning of Energy Materials
journal, January 2020
- Chen, Chi; Zuo, Yunxing; Ye, Weike
- Advanced Energy Materials, Vol. 10, Issue 8
Physically informed artificial neural networks for atomistic modeling of materials
journal, May 2019
- Pun, G. P. Purja; Batra, R.; Ramprasad, R.
- Nature Communications, Vol. 10, Issue 1
Recent advances and applications of machine learning in solid-state materials science
journal, August 2019
- Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
- npj Computational Materials, Vol. 5, Issue 1
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
journal, November 2019
- Hernandez, Alberto; Balasubramanian, Adarsh; Yuan, Fenglin
- npj Computational Materials, Vol. 5, Issue 1
Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide
journal, January 2018
- Li, Wenwen; Ando, Yasunobu
- Physical Chemistry Chemical Physics, Vol. 20, Issue 47
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
journal, June 2018
- Rupp, Matthias; von Lilienfeld, O. Anatole; Burke, Kieron
- The Journal of Chemical Physics, Vol. 148, Issue 24
Deep learning inter-atomic potential model for accurate irradiation damage simulations
journal, June 2019
- Wang, Hao; Guo, Xun; Zhang, Linfeng
- Applied Physics Letters, Vol. 114, Issue 24
Machine learning for interatomic potential models
journal, February 2020
- Mueller, Tim; Hernandez, Alberto; Wang, Chuhong
- The Journal of Chemical Physics, Vol. 152, Issue 5
Machine Learning a General-Purpose Interatomic Potential for Silicon
text, January 2018
- Bartók, Ap; Kermode, J.; Bernstein, N.
- Apollo - University of Cambridge Repository
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.
journalarticle, January 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Wiley
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
text, January 2019
- Seko, Atsuto; Togo, Atsushi; Tanaka, Isao
- arXiv
Data-driven Material Models for Atomistic Simulation
text, January 2019
- Wood, Mitchell A.; Cusentino, Mary Alice; Wirth, Brian D.
- arXiv
Deep learning inter-atomic potential model for accurate irradiation damage simulations
text, January 2019
- Wang, Hao; Guo, Xun; Zhang, Linfeng
- arXiv
Physically-informed artificial neural networks for atomistic modeling of materials
text, January 2018
- Pun, G. P. Purja; Batra, R.; Ramprasad, R.
- arXiv