Representing local atomic environment using descriptors based on local correlations
Abstract
Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process. To this end, we develop here a framework to generate descriptors based on many-body correlations that can effectively capture intrinsic geometric features of the local environment of an atom. These descriptors are based on the spectrum of two-body, three-body, four-body, and higher order correlations between an atom and its neighbors and are evaluated by calculating the corresponding two-body, three-body, and four-body overlap integrals. They are invariant to global translation, global rotation, reflection, and permutations of atomic indices. By systematically testing the ability to capture the local atomic environment, it is shown that the local correlation descriptors are able to successfully reconstruct structures containing 10-25 atomsmore »
- Authors:
-
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Physics Division
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1502005
- Report Number(s):
- LLNL-JRNL-757813
Journal ID: ISSN 0021-9606; 945702
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Physics
- Additional Journal Information:
- Journal Volume: 149; Journal Issue: 24; Journal ID: ISSN 0021-9606
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Gaussian processes; interatomic potentials; graph theory; machine learning; artificial neural networks
Citation Formats
Samanta, Amit. Representing local atomic environment using descriptors based on local correlations. United States: N. p., 2018.
Web. doi:10.1063/1.5055772.
Samanta, Amit. Representing local atomic environment using descriptors based on local correlations. United States. https://doi.org/10.1063/1.5055772
Samanta, Amit. Wed .
"Representing local atomic environment using descriptors based on local correlations". United States. https://doi.org/10.1063/1.5055772. https://www.osti.gov/servlets/purl/1502005.
@article{osti_1502005,
title = {Representing local atomic environment using descriptors based on local correlations},
author = {Samanta, Amit},
abstractNote = {Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process. To this end, we develop here a framework to generate descriptors based on many-body correlations that can effectively capture intrinsic geometric features of the local environment of an atom. These descriptors are based on the spectrum of two-body, three-body, four-body, and higher order correlations between an atom and its neighbors and are evaluated by calculating the corresponding two-body, three-body, and four-body overlap integrals. They are invariant to global translation, global rotation, reflection, and permutations of atomic indices. By systematically testing the ability to capture the local atomic environment, it is shown that the local correlation descriptors are able to successfully reconstruct structures containing 10-25 atoms which was previously not possible.},
doi = {10.1063/1.5055772},
journal = {Journal of Chemical Physics},
number = 24,
volume = 149,
place = {United States},
year = {2018},
month = {12}
}
Web of Science
Works referenced in this record:
On representing chemical environments
journal, May 2013
- Bartók, Albert P.; Kondor, Risi; Csányi, Gábor
- Physical Review B, Vol. 87, Issue 18
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- Physical Review Letters, Vol. 108, Issue 5
Normalized cuts and image segmentation
journal, January 2000
- Jianbo Shi, ; Malik, J.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, Issue 8
A universal strategy for the creation of machine learning-based atomistic force fields
journal, September 2017
- Huan, Tran Doan; Batra, Rohit; Chapman, James
- npj Computational Materials, Vol. 3, Issue 1
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
journal, June 2018
- Gastegger, M.; Schwiedrzik, L.; Bittermann, M.
- The Journal of Chemical Physics, Vol. 148, Issue 24
Representing potential energy surfaces by high-dimensional neural network potentials
journal, April 2014
- Behler, J.
- Journal of Physics: Condensed Matter, Vol. 26, Issue 18
Learning molecular energies using localized graph kernels
journal, March 2017
- Ferré, Grégoire; Haut, Terry; Barros, Kipton
- The Journal of Chemical Physics, Vol. 146, Issue 11
First-principles interatomic potentials for ten elemental metals via compressed sensing
journal, August 2015
- Seko, Atsuto; Takahashi, Akira; Tanaka, Isao
- Physical Review B, Vol. 92, Issue 5
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
journal, June 2018
- Kamath, Aditya; Vargas-Hernández, Rodrigo A.; Krems, Roman V.
- The Journal of Chemical Physics, Vol. 148, Issue 24
Efficient nonparametric -body force fields from machine learning
journal, May 2018
- Glielmo, Aldo; Zeni, Claudio; De Vita, Alessandro
- Physical Review B, Vol. 97, Issue 18
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
journal, January 2016
- Shapeev, Alexander V.
- Multiscale Modeling & Simulation, Vol. 14, Issue 3
Computational aspects of many-body potentials
journal, May 2012
- Plimpton, Steven J.; Thompson, Aidan P.
- MRS Bulletin, Vol. 37, Issue 5
Erratum: On representing chemical environments [Phys. Rev. B 87 , 184115 (2013)]
journal, July 2017
- Bartók, Albert P.; Kondor, Risi; Csányi, Gábor
- Physical Review B, Vol. 96, Issue 1
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007
- Behler, Jörg; Parrinello, Michele
- Physical Review Letters, Vol. 98, Issue 14
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
journal, January 2012
- Artrith, Nongnuch; Behler, Jörg
- Physical Review B, Vol. 85, Issue 4
Using Force Matching To Determine Reactive Force Fields for Water under Extreme Thermodynamic Conditions
journal, December 2016
- Koziol, Lucas; Fried, Laurence E.; Goldman, Nir
- Journal of Chemical Theory and Computation, Vol. 13, Issue 1
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
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015
- Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan
- The Journal of Physical Chemistry Letters, Vol. 6, Issue 12
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
journal, March 2015
- Li, Zhenwei; Kermode, James R.; De Vita, Alessandro
- Physical Review Letters, Vol. 114, Issue 9
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
journal, June 2003
- Belkin, Mikhail; Niyogi, Partha
- Neural Computation, Vol. 15, Issue 6
Efficient Sampling of Atomic Configurational Spaces
journal, August 2010
- Pártay, Lívia B.; Bartók, Albert P.; Csányi, Gábor
- The Journal of Physical Chemistry B, Vol. 114, Issue 32
Exploring the free energy surface using ab initio molecular dynamics
journal, April 2016
- Samanta, Amit; Morales, Miguel A.; Schwegler, Eric
- The Journal of Chemical Physics, Vol. 144, Issue 16
A fingerprint based metric for measuring similarities of crystalline structures
journal, January 2016
- Zhu, Li; Amsler, Maximilian; Fuhrer, Tobias
- The Journal of Chemical Physics, Vol. 144, Issue 3
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
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
journal, July 2013
- Hansen, Katja; Montavon, Grégoire; Biegler, Franziska
- Journal of Chemical Theory and Computation, Vol. 9, Issue 8
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
journal, January 1998
- Lagarias, Jeffrey C.; Reeds, James A.; Wright, Margaret H.
- SIAM Journal on Optimization, Vol. 9, Issue 1
Adaptive machine learning framework to accelerate ab initio molecular dynamics
journal, December 2014
- Botu, Venkatesh; Ramprasad, Rampi
- International Journal of Quantum Chemistry, Vol. 115, Issue 16
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
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010
- Bartók, Albert P.; Payne, Mike C.; Kondor, Risi
- Physical Review Letters, Vol. 104, Issue 13
Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power
journal, June 2018
- Takahashi, Akira; Seko, Atsuto; Tanaka, Isao
- The Journal of Chemical Physics, Vol. 148, Issue 23
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
journal, May 2005
- Coifman, R. R.; Lafon, S.; Lee, A. B.
- Proceedings of the National Academy of Sciences, Vol. 102, Issue 21
Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
journal, October 2016
- Cui, Jie; Krems, Roman V.
- Journal of Physics B: Atomic, Molecular and Optical Physics, Vol. 49, Issue 22
Order-parameter-aided temperature-accelerated sampling for the exploration of crystal polymorphism and solid-liquid phase transitions
journal, June 2014
- Yu, Tang-Qing; Chen, Pei-Yang; Chen, Ming
- The Journal of Chemical Physics, Vol. 140, Issue 21
ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon
journal, November 2017
- Lindsey, Rebecca K.; Fried, Laurence E.; Goldman, Nir
- Journal of Chemical Theory and Computation, Vol. 13, Issue 12
Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
journal, July 2006
- Nadler, Boaz; Lafon, Stéphane; Coifman, Ronald R.
- Applied and Computational Harmonic Analysis, Vol. 21, Issue 1, p. 113-127
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
journal, April 2018
- Zhang, Linfeng; Han, Jiequn; Wang, Han
- Physical Review Letters, Vol. 120, Issue 14
Extending the accuracy of the SNAP interatomic potential form
journal, June 2018
- Wood, Mitchell A.; Thompson, Aidan P.
- The Journal of Chemical Physics, Vol. 148, Issue 24
Sampling saddle points on a free energy surface
journal, April 2014
- Samanta, Amit; Chen, Ming; Yu, Tang-Qing
- The Journal of Chemical Physics, Vol. 140, Issue 16
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
journal, February 2011
- Behler, Jörg
- The Journal of Chemical Physics, Vol. 134, Issue 7
On Unique Numbering of Atoms and Unique Codes for Molecular Graphs
journal, May 1975
- Randic, Milan
- Journal of Chemical Information and Modeling, Vol. 15, Issue 2