Density functional theory based neural network force fields from energy decompositions

Yufeng Huang; Jun Kang; William A. Goddard; Lin-Wang Wang

doi:10.1103/physrevb.99.064103

Density functional theory based neural network force fields from energy decompositions

Physical Review B ◽

10.1103/physrevb.99.064103 ◽

2019 ◽

Vol 99 (6) ◽

Cited By ~ 26

Author(s):

Yufeng Huang ◽

Jun Kang ◽

William A. Goddard ◽

Lin-Wang Wang

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Density Functional ◽

Force Fields ◽

Functional Theory

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Investigation of cobalt doped tin dioxide structure and defects: Density functional theory and empirical force fields

Journal of Contemporary Physics (Armenian Academy of Sciences) ◽

10.3103/s1068337217030070 ◽

2017 ◽

Vol 52 (3) ◽

pp. 227-233 ◽

Cited By ~ 1

Author(s):

H. A. Zakaryan ◽

V. M. Aroutiounian

Keyword(s):

Density Functional Theory ◽

Density Functional ◽

Tin Dioxide ◽

Force Fields ◽

Functional Theory

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Identification of the Selective Sites for Electrochemical Reduction of CO to C2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

ACS Energy Letters ◽

10.1021/acsenergylett.8b01933 ◽

2018 ◽

Vol 3 (12) ◽

pp. 2983-2988 ◽

Cited By ~ 17

Author(s):

Yufeng Huang ◽

Yalu Chen ◽

Tao Cheng ◽

Lin-Wang Wang ◽

William A. Goddard

Keyword(s):

Machine Learning ◽

Density Functional Theory ◽

Electrochemical Reduction ◽

Copper Nanoparticles ◽

Density Functional ◽

Force Fields ◽

Reactive Force ◽

Functional Theory ◽

Reactive Force Fields

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Prediction of glucagon receptor antagonist activities of some substituted imidazoles using combined radial basis function neural network and density functional theory

Medicinal Chemistry Research ◽

10.1007/s00044-013-0869-9 ◽

2013 ◽

Vol 23 (6) ◽

pp. 2744-2756

Author(s):

Mohsen Shahlaei ◽

Zohreh Nazari

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Radial Basis Function ◽

Receptor Antagonist ◽

Basis Function ◽

Density Functional ◽

Functional Theory ◽

Glucagon Receptor ◽

Radial Basis ◽

Glucagon Receptor Antagonist

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Density functional theory and empirical derived force fields for the delocalized polaron form of polyaniline

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2004.10.046 ◽

2005 ◽

Vol 61 (7) ◽

pp. 1419-1429 ◽

Cited By ~ 8

Author(s):

Z. Cherrak ◽

P. Lagant ◽

N. Benharrats ◽

A. Semmoud ◽

F. Hamdache ◽

...

Keyword(s):

Density Functional Theory ◽

Density Functional ◽

Force Fields ◽

Functional Theory

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Investigating the Calculation of Anharmonic Vibrational Frequencies Using Force Fields Derived from Density Functional Theory

The Journal of Physical Chemistry A ◽

10.1021/jp301670f ◽

2012 ◽

Vol 116 (17) ◽

pp. 4417-4425 ◽

Cited By ~ 33

Author(s):

Magnus W. D. Hanson-Heine ◽

Michael W. George ◽

Nicholas A. Besley

Keyword(s):

Density Functional Theory ◽

Density Functional ◽

Force Fields ◽

Vibrational Frequencies ◽

Functional Theory

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Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

Chinese Physics B ◽

10.1088/1674-1056/19/7/076401 ◽

2010 ◽

Vol 19 (7) ◽

pp. 076401

Author(s):

Wang Ting-Ting ◽

Li Wen-Long ◽

Chen Zhang-Hui ◽

Miao Ling

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Density Functional Theory ◽

Systematic Error ◽

Density Functional ◽

Density Functional Theory Calculation ◽

Functional Theory ◽

The Density Functional Theory ◽

Combination Approach ◽

Theory Calculation

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A density functional theory parameterised neural network model of zirconia

Molecular Simulation ◽

10.1080/08927022.2017.1420185 ◽

2018 ◽

Vol 44 (8) ◽

pp. 623-630 ◽

Cited By ~ 5

Author(s):

Chen Wang ◽

Akshay Tharval ◽

John R. Kitchin

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Network Model ◽

Neural Network Model ◽

Density Functional ◽

Functional Theory

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Artificial neural network prediction indicators of density functional theory metal hydride models

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2013.06.138 ◽

2013 ◽

Vol 38 (27) ◽

pp. 11920-11929 ◽

Cited By ~ 5

Author(s):

William O. Griffin ◽

Jerry A. Darsey

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Artificial Neural Network ◽

Metal Hydride ◽

Density Functional ◽

Functional Theory ◽

Neural Network Prediction ◽

Network Prediction ◽

Artificial Neural

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Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

The Journal of Chemical Physics ◽

10.1063/1.3206326 ◽

2009 ◽

Vol 131 (7) ◽

pp. 074104 ◽

Cited By ~ 95

Author(s):

Roman M. Balabin ◽

Ekaterina I. Lomakina

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Quantum Chemistry ◽

Density Functional ◽

Accurate Prediction ◽

Network Approach ◽

Neural Network Approach ◽

Functional Theory ◽

Chemistry Data

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Systematic Study of the Properties of CdS Clusters with Carboxylate Ligands Using a Deep Neural Network Potential Developed with Data from Density Functional Theory Calculations

The Journal of Physical Chemistry A ◽

10.1021/acs.jpca.0c06965 ◽

2020 ◽

Vol 124 (50) ◽

pp. 10472-10481

Author(s):

Kiet A. Nguyen ◽

Ruth Pachter ◽

Paul N. Day

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Systematic Study ◽

Density Functional ◽

Deep Neural Network ◽

Density Functional Theory Calculations ◽

Functional Theory ◽

Carboxylate Ligands

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