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We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems.
Note on an approximation treatment for many-electron systems
Chr Møller and Milton S Plesset · 1934
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Self-consistent field, with exchange, for beryllium
Douglas Rayner Hartree and W Hartree · 1935
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Inhomogeneous electron gas
Pierre Hohenberg and Walter Kohn · 1964
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Self-consistent equations including exchange and correlation effects
Walter Kohn and Lu Jeu Sham · 1965
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On the correlation problem in atomic and molecular systems. calculation of wavefunction components in ursell-type expansion using quantum-field theoretical methods
Jiří Čížek · 1966
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Hohenberg-kohn theorem for nonlocal external potentials
Thomas L Gilbert · 1975
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Coupled-cluster method for multideterminantal reference states
Bogumil Jeziorski and Hendrik J Monkhorst · 1981
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Quadratic configuration interaction. A general technique for determining electron correlation energies
John A Pople, Martin Head-Gordon, and Krishnan Raghavachari · 1987
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Gaussian basis sets for use in correlated molecular calculations. iii. the atoms aluminum through argon
David E Woon and Thom H Dunning Jr · 1993
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
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Validation of electronic structure methods for isomerization reactions of large organic molecules
Sijie Luo, Yan Zhao, and Donald G Truhlar · 2011
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Generalized gradient approximation that recovers the second-order density-gradient expansion with optimized across-the-board performance
Roberto Peverati, Yan Zhao, and Donald G Truhlar · 2011
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Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole VonLilienfeld · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
Transferability in machine learning for electronic structure via the molecular orbital basis
Matthew Welborn, Lixue Cheng, and Thomas F Miller III · 2018
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Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, and Weinan E · 2018
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Regression clustering for improved accuracy and training costs with molecular-orbital-based machine learning
Lixue Cheng, Nikola B Kovachki, Matthew Welborn, and Thomas F Miller III · 2019
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A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
Lixue Cheng, Matthew Welborn, Anders S Christensen, and Thomas F Miller III · 2019
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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ANI-1: an extensible neural network potential with dft accuracy at force field computational cost
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Deep potential: a general representation of a many-body potential energy surface
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Pyscf: the python-based simulations of chemistry framework
Qiming Sun, Timothy C Berkelbach, Nick S Blunt, George H Booth, Sheng Guo, Zhendong Li, Junzi Liu, James D McClain, Elvira R Sayfutyarova, Sandeep Sharma, et al · 2018
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New basis set exchange: An open, up-to-date resource for the molecular sciences community
Benjamin P Pritchard, Doaa Altarawy, Brett Didier, Tara D Gibson, and Theresa L Windus · 2019
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, and Adrian E Roitberg · 2019
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Active learning of uniformly accurate interatomic potentials for materials simulation
Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, and Weinan E · 2019
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Thermalized (350k) qm7b, gdb-13, water, and short alkane quantum chemistry dataset including mob-ml features
Lixue Cheng, Matthew Welborn, Anders S Christensen, and Thomas F Miller III · 2020
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Machine learning accurate exchange and correlation functionals of the electronic density
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86 pflops deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy
Denghui Lu, Han Wang, Mohan Chen, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Weile Jia, and Linfeng Zhang · 2020
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The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E Roitberg, Olexandr Isayev, and Sergei Tretiak · 2020
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