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Title: Density functional theory based neural network force fields from energy decompositions


Not provided.

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Publication Date:
Research Org.:
Oak Ridge National Laboratory, Oak Ridge Leadership Computing Facility (OLCF); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
Resource Type:
Journal Article
Journal Name:
Physical Review B
Additional Journal Information:
Journal Volume: 99; Journal Issue: 6; Journal ID: ISSN 2469-9950
American Physical Society (APS)
Country of Publication:
United States

Citation Formats

Huang, Yufeng, Kang, Jun, Goddard, William A., and Wang, Lin-Wang. Density functional theory based neural network force fields from energy decompositions. United States: N. p., 2019. Web. doi:10.1103/PhysRevB.99.064103.
Huang, Yufeng, Kang, Jun, Goddard, William A., & Wang, Lin-Wang. Density functional theory based neural network force fields from energy decompositions. United States. doi:10.1103/PhysRevB.99.064103.
Huang, Yufeng, Kang, Jun, Goddard, William A., and Wang, Lin-Wang. Fri . "Density functional theory based neural network force fields from energy decompositions". United States. doi:10.1103/PhysRevB.99.064103.
title = {Density functional theory based neural network force fields from energy decompositions},
author = {Huang, Yufeng and Kang, Jun and Goddard, William A. and Wang, Lin-Wang},
abstractNote = {Not provided.},
doi = {10.1103/PhysRevB.99.064103},
journal = {Physical Review B},
issn = {2469-9950},
number = 6,
volume = 99,
place = {United States},
year = {2019},
month = {2}

Works referenced in this record:

Machine learning: Trends, perspectives, and prospects
journal, July 2015

Machine learning applications in genetics and genomics
journal, May 2015

  • Libbrecht, Maxwell W.; Noble, William Stafford
  • Nature Reviews Genetics, Vol. 16, Issue 6
  • DOI: 10.1038/nrg3920

Deep learning for computational biology
journal, July 2016

  • Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold
  • Molecular Systems Biology, Vol. 12, Issue 7
  • DOI: 10.15252/msb.20156651

Machine learning in materials informatics: recent applications and prospects
journal, December 2017

  • Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0056-5

Searching for exotic particles in high-energy physics with deep learning
journal, July 2014

  • Baldi, P.; Sadowski, P.; Whiteson, D.
  • Nature Communications, Vol. 5, Issue 1
  • DOI: 10.1038/ncomms5308

Machine learning phases of matter
journal, February 2017

  • Carrasquilla, Juan; Melko, Roger G.
  • Nature Physics, Vol. 13, Issue 5
  • DOI: 10.1038/nphys4035

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
journal, January 1996

  • King, R. D.; Muggleton, S. H.; Srinivasan, A.
  • Proceedings of the National Academy of Sciences, Vol. 93, Issue 1
  • DOI: 10.1073/pnas.93.1.438

journal, March 2006

  • Dudek, Arkadiusz; Arodz, Tomasz; Galvez, Jorge
  • Combinatorial Chemistry & High Throughput Screening, Vol. 9, Issue 3
  • DOI: 10.2174/138620706776055539

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
journal, February 2015

  • Ma, Junshui; Sheridan, Robert P.; Liaw, Andy
  • Journal of Chemical Information and Modeling, Vol. 55, Issue 2
  • DOI: 10.1021/ci500747n

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

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

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

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
journal, June 2017

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

Quantum-chemical insights from deep tensor neural networks
journal, January 2017

  • Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms13890

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
  • DOI: 10.1063/1.3553717

On representing chemical environments
journal, May 2013

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

Shape retrieval using 3D Zernike descriptors
journal, September 2004

First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
journal, August 2017

Neural network and ReaxFF comparison for Au properties: Comparison of ReaxFF and BPNN Potentials
journal, March 2016

  • Boes, Jacob R.; Groenenboom, Mitchell C.; Keith, John A.
  • International Journal of Quantum Chemistry, Vol. 116, Issue 13
  • DOI: 10.1002/qua.25115

First-principles Green-Kubo method for thermal conductivity calculations
journal, July 2017

Effects of the c-Si/a-SiO 2 interfacial atomic structure on its band alignment: an ab initio study
journal, January 2017

  • Zheng, Fan; Pham, Hieu H.; Wang, Lin-Wang
  • Physical Chemistry Chemical Physics, Vol. 19, Issue 48
  • DOI: 10.1039/C7CP05879A

Energy density in density functional theory: Application to crystalline defects and surfaces
journal, March 2011

Charge-Density Patching Method for Unconventional Semiconductor Binary Systems
journal, June 2002

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
journal, April 2011

Learning representations by back-propagating errors
journal, October 1986

  • Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
  • Nature, Vol. 323, Issue 6088
  • DOI: 10.1038/323533a0

Amp: A modular approach to machine learning in atomistic simulations
journal, October 2016

Force and heat current formulas for many-body potentials in molecular dynamics simulations with applications to thermal conductivity calculations
journal, September 2015

Thermal conductivity of thin films: Measurements and understanding
journal, May 1989

  • Cahill, David G.; Fischer, Henry E.; Klitsner, Tom
  • Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, Vol. 7, Issue 3
  • DOI: 10.1116/1.576265

Thermal Conductivity of Amorphous Silicon
journal, May 1996

  • Wada, Hiroshi; Kamijoh, Takeshi
  • Japanese Journal of Applied Physics, Vol. 35, Issue Part 2, No. 5B
  • DOI: 10.1143/JJAP.35.L648

Thermal Conductivity and Specific Heat of Thin-Film Amorphous Silicon
journal, February 2006

Ab Initio Green-Kubo Approach for the Thermal Conductivity of Solids
journal, April 2017

Fast plane wave density functional theory molecular dynamics calculations on multi-GPU machines
journal, October 2013

The analysis of a plane wave pseudopotential density functional theory code on a GPU machine
journal, January 2013