skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Accurate interatomic force fields via machine learning with covariant kernels

; ;
Publication Date:
Sponsoring Org.:
OSTI Identifier:
Grant/Contract Number:  
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
Journal Name: Physical Review B Journal Volume: 95 Journal Issue: 21; Journal ID: ISSN 2469-9950
American Physical Society
Country of Publication:
United States

Citation Formats

Glielmo, Aldo, Sollich, Peter, and De Vita, Alessandro. Accurate interatomic force fields via machine learning with covariant kernels. United States: N. p., 2017. Web. doi:10.1103/PhysRevB.95.214302.
Glielmo, Aldo, Sollich, Peter, & De Vita, Alessandro. Accurate interatomic force fields via machine learning with covariant kernels. United States. doi:10.1103/PhysRevB.95.214302.
Glielmo, Aldo, Sollich, Peter, and De Vita, Alessandro. Thu . "Accurate interatomic force fields via machine learning with covariant kernels". United States. doi:10.1103/PhysRevB.95.214302.
title = {Accurate interatomic force fields via machine learning with covariant kernels},
author = {Glielmo, Aldo and Sollich, Peter and De Vita, Alessandro},
abstractNote = {},
doi = {10.1103/PhysRevB.95.214302},
journal = {Physical Review B},
number = 21,
volume = 95,
place = {United States},
year = {2017},
month = {6}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1103/PhysRevB.95.214302

Citation Metrics:
Cited by: 24 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
journal, February 2016

Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
  • DOI: 10.1103/PhysRevLett.77.3865

Atomistic modeling of the γ and γ′-phases of the Ni–Al system
journal, April 2004

Technological impact of magnetic hard disk drives on storage systems
journal, January 2003

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012

Machine learning for many-body physics: The case of the Anderson impurity model
journal, October 2014

  • Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole
  • Physical Review B, Vol. 90, Issue 15
  • DOI: 10.1103/PhysRevB.90.155136

Finding Density Functionals with Machine Learning
journal, June 2012

Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations
journal, November 1909

  • Mercer, J.
  • Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 83, Issue 559
  • DOI: 10.1098/rspa.1909.0075

Development of new interatomic potentials appropriate for crystalline and liquid iron
journal, December 2003

Accelerating materials property predictions using machine learning
journal, September 2013

  • Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
  • Scientific Reports, Vol. 3, Issue 1
  • DOI: 10.1038/srep02810

Cramming More Components Onto Integrated Circuits
journal, January 1998

Accuracy and transferability of Gaussian approximation potential models for tungsten
journal, September 2014

Gaussian approximation potentials: A brief tutorial introduction
journal, April 2015

  • Bartók, Albert P.; Csányi, Gábor
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24927

Invariant integration over the unitary group
journal, January 2003

  • Aubert, S.; Lam, C. S.
  • Journal of Mathematical Physics, Vol. 44, Issue 12
  • DOI: 10.1063/1.1622448

Modeling electronic quantum transport with machine learning
journal, June 2014

Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction
journal, October 2012

  • Krejnik, M.; Tyutin, A.
  • IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, Issue 10
  • DOI: 10.1109/TNNLS.2012.2207739

Invariant kernel functions for pattern analysis and machine learning
journal, May 2007

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
  • DOI: 10.1063/1.4930541

A Novel Scheme for Accurate Md Simulations of Large Systems
journal, January 1997

On representing chemical environments
journal, May 2013

Modelling defects in Ni–Al with EAM and DFT calculations
journal, April 2016

  • Bianchini, F.; Kermode, J. R.; De Vita, A.
  • Modelling and Simulation in Materials Science and Engineering, Vol. 24, Issue 4
  • DOI: 10.1088/0965-0393/24/4/045012

Hydrogen-enhanced local plasticity at dilute bulk H concentrations: The role of H–H interactions and the formation of local hydrides
journal, May 2011

Adaptive stochastic methods for sampling driven molecular systems
journal, August 2011

  • Jones, Andrew; Leimkuhler, Ben
  • The Journal of Chemical Physics, Vol. 135, Issue 8
  • DOI: 10.1063/1.3626941

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression
journal, August 2014

  • Stecher, Thomas; Bernstein, Noam; Csányi, Gábor
  • Journal of Chemical Theory and Computation, Vol. 10, Issue 9
  • DOI: 10.1021/ct500438v

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007

A Simplification of the Hartree-Fock Method
journal, February 1951

Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties
journal, September 1998

  • Elstner, M.; Porezag, D.; Jungnickel, G.
  • Physical Review B, Vol. 58, Issue 11, p. 7260-7268
  • DOI: 10.1103/PhysRevB.58.7260

A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers
journal, June 2015

  • Caccin, Marco; Li, Zhenwei; Kermode, James R.
  • International Journal of Quantum Chemistry, Vol. 115, Issue 16
  • DOI: 10.1002/qua.24952

Learning scheme to predict atomic forces and accelerate materials simulations
journal, September 2015

Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
journal, March 2015

Nearsightedness of electronic matter
journal, August 2005

  • Prodan, E.; Kohn, W.
  • Proceedings of the National Academy of Sciences, Vol. 102, Issue 33
  • DOI: 10.1073/pnas.0505436102

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
  • DOI: 10.1002/qua.24836

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
journal, January 2016

  • Shapeev, Alexander V.
  • Multiscale Modeling & Simulation, Vol. 14, Issue 3
  • DOI: 10.1137/15M1054183

Computer simulation of local order in condensed phases of silicon
journal, April 1985

Density Functional and Density Matrix Method Scaling Linearly with the Number of Atoms
journal, April 1996

Kryder's Law
journal, August 2005

Machine learning of accurate energy-conserving molecular force fields
journal, May 2017

  • Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
  • Science Advances, Vol. 3, Issue 5
  • DOI: 10.1126/sciadv.1603015

Machine learning based interatomic potential for amorphous carbon
journal, March 2017

The Wave Mechanics of an Atom with a Non-Coulomb Central Field. Part I. Theory and Methods
journal, January 1928

  • Hartree, D. R.
  • Mathematical Proceedings of the Cambridge Philosophical Society, Vol. 24, Issue 1
  • DOI: 10.1017/S0305004100011919