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We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis.
“On Differentiating Eigenvalues and Eigenvectors”
Jan. Magnus · 1985
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“On Differentiating Eigenvalues and Eigenvectors”
Jan. Magnus · 1985
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“Analytical energy gradients for local second-order Møller–Plesset perturbation theory using density fitting approximations”
Martin Schütz, Hans-Joachim Werner, Roland Lindh and Frederick Manby · 2004
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“Fast Hartree-Fock theory using local density fitting approximations”
Robert Polly, Hans-Joachim Werner, Frederick. Manby and Peter. Knowles · 2004
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“Analytical energy gradients for local second-order Møller–Plesset perturbation theory using density fitting approximations”
Martin Schütz, Hans-Joachim Werner, Roland Lindh and Frederick Manby · 2004
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“Fast Hartree-Fock theory using local density fitting approximations”
Robert Polly, Hans-Joachim Werner, Frederick. Manby and Peter. Knowles · 2004
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“Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy”
Florian Weigend and Reinhart Ahlrichs · 2005
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“Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy”
Florian Weigend and Reinhart Ahlrichs · 2005
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“Hartree-Fock exchange fitting basis sets for H to Rn”
Florian Weigend · 2008
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“Hartree-Fock exchange fitting basis sets for H to Rn”
Florian Weigend · 2008
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“A robust and accurate tight-binding quantum chemical method for structures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements (Z=1–86)”
Stefan Grimme, Christoph Bannwarth and Philip Shushkov · 2009
Earlier work this paper cites.
“A robust and accurate tight-binding quantum chemical method for structures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements (Z=1–86)”
Stefan Grimme, Christoph Bannwarth and Philip Shushkov · 2009
Earlier work this paper cites.
“A robust and accurate tight-binding quantum chemical method for structures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements (Z=1–86)”
Stefan Grimme, Christoph Bannwarth and Philip Shushkov · 2009
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Stefan Grimme, Christoph Bannwarth and Philip Shushkov · 2009
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“Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons”
Albert Bartók, Mike Payne, Risi Kondor and Gábor Csányi · 2010
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“Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons”
Albert Bartók, Mike Payne, Risi Kondor and Gábor Csányi · 2010
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“The melatonin conformer space: Benchmark and assessment of wave function and DFT methods for a paradigmatic biological and pharmacological molecule”
Uma Fogueri, Sebastian Kozuch, Amir Karton and Jan Martin · 2013
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“Long-range corrected hybrid density functionals with improved dispersion corrections”
You-Sheng Lin, Guan-De Li, Shan-Ping Mao and Jeng-Da Chai · 2013
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“The melatonin conformer space: Benchmark and assessment of wave function and DFT methods for a paradigmatic biological and pharmacological molecule”
Uma Fogueri, Sebastian Kozuch, Amir Karton and Jan Martin · 2013
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“Long-range corrected hybrid density functionals with improved dispersion corrections”
You-Sheng Lin, Guan-De Li, Shan-Ping Mao and Jeng-Da Chai · 2013
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O Von · 2014
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“Implementation of nuclear gradients of range-separated hybrid density functionals and benchmarking on rotational constants for organic molecules”
Tobias Risthaus, Marc Steinmetz and Stefan Grimme · 2014
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O Von · 2014
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O Von · 2014
Earlier work this paper cites.
“Implementation of nuclear gradients of range-separated hybrid density functionals and benchmarking on rotational constants for organic molecules”
Tobias Risthaus, Marc Steinmetz and Stefan Grimme · 2014
Earlier work this paper cites.
“Quantum chemistry structures and properties of 134 kilo molecules”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O Von · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik 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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“Geometry optimization made simple with translation and rotation coordinates”
Lee-Ping Wang and Chenchen Song · 2016
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Geometry optimization made simple with translation and rotation coordinates”
Lee-Ping Wang and Chenchen Song · 2016
Cited alongside, same era.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Cited alongside, same era.
“ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost”
Justin Smith, Olexandr Isayev and Adrian Roitberg · 2017
Cited alongside, same era.
“Schnet: A continuous-filter convolutional neural network for modeling quantum interactions”
Kristof Schütt et al · 2017
Cited alongside, same era.
“Neural message passing for Quantum chemistry”
Justin Gilmer et al · 2017
Cited alongside, same era.
“Automatic Differentiation in PyTorch”
Adam Paszke et al · 2017
Cited alongside, same era.
“PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges”
Oliver Unke and Markus Meuwly · 2019
Later among the works it cites.
“Cormorant: Covariant Molecular Neural Networks”
Brandon Anderson, Truong Hy and Risi Kondor · 2019
Later among the works it cites.
“Directional Message Passing for Molecular Graphs”
Johannes Klicpera, Janek Groß and Stephan Günnemann · 2019
Later among the works it cites.
“Analytical gradients for projection-based wavefunction-in-DFT embedding”
Sebastian Lee, Feizhi Ding, Frederick Manby and Thomas Miller · 2019
Later among the works it cites.
“Strategies for Pre-training Graph Neural Networks”
Weihua Hu et al · 2019
Later among the works it cites.
“GFN2-xTB — An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions”
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“Accurate, large minibatch sgd: Training imagenet in 1 hour”
Priya Goyal et al · 2017
Cited alongside, same era.
“ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost”
Justin Smith, Olexandr Isayev and Adrian Roitberg · 2017
Cited alongside, same era.
“Schnet: A continuous-filter convolutional neural network for modeling quantum interactions”
Kristof Schütt et al · 2017
Cited alongside, same era.
“Neural message passing for Quantum chemistry”
Justin Gilmer et al · 2017
Cited alongside, same era.
“Automatic Differentiation in PyTorch”
Adam Paszke et al · 2017
Cited alongside, same era.
“Accurate, large minibatch sgd: Training imagenet in 1 hour”
Priya Goyal et al · 2017
Cited alongside, same era.
Christoph Bannwarth, Sebastian Ehlert and Stefan Grimme · 2019
Later among the works it cites.
“entos: A Quantum Molecular Simulation Package”
Frederick Manby et al · 2019
Later among the works it cites.
“Set transformer: A framework for attention-based permutation-invariant neural networks”
Juho Lee et al · 2019
Later among the works it cites.
“Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning”
Justin Smith et al · 2019
Later among the works it cites.
“A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules”
Lixue Cheng, Matthew Welborn, Anders Christensen and Thomas Miller · 2019
Later among the works it cites.
“Analyzing learned molecular representations for property prediction”
Kevin Yang et al · 2019
Later among the works it cites.
“PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges”
Oliver Unke and Markus Meuwly · 2019
Later among the works it cites.
“Cormorant: Covariant Molecular Neural Networks”
Brandon Anderson, Truong Hy and Risi Kondor · 2019
Later among the works it cites.
“Directional Message Passing for Molecular Graphs”
Johannes Klicpera, Janek Groß and Stephan Günnemann · 2019
Later among the works it cites.
“Analytical gradients for projection-based wavefunction-in-DFT embedding”
Sebastian Lee, Feizhi Ding, Frederick Manby and Thomas Miller · 2019
Later among the works it cites.
“Strategies for Pre-training Graph Neural Networks”
Weihua Hu et al · 2019
Later among the works it cites.
“GFN2-xTB — An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions”
Christoph Bannwarth, Sebastian Ehlert and Stefan Grimme · 2019
Later among the works it cites.
“entos: A Quantum Molecular Simulation Package”
Frederick Manby et al · 2019
Later among the works it cites.
“Set transformer: A framework for attention-based permutation-invariant neural networks”
Juho Lee et al · 2019
Later among the works it cites.
“OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features”
Zhuoran Qiao et al · 2020
Closest in time.
“Transferable multi-level attention neural network for accurate prediction of quantum chemistry properties via multi-task learning”
Ziteng Liu et al · 2020
Closest in time.
“Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy”
Yixiao Chen, Linfeng Zhang, Han Wang and Weinan E · 2020
Closest in time.
“OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features”
Zhuoran Qiao et al · 2020
Closest in time.
“Semiempirical Extended Tight-Binding Program Package” https://github.com/grimme-lab/xtb , 2020, accessed July 14, 2020
2020
Closest in time.
“OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features”
Zhuoran Qiao et al · 2020
Closest in time.
“Transferable multi-level attention neural network for accurate prediction of quantum chemistry properties via multi-task learning”
Ziteng Liu et al · 2020
Closest in time.
“Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy”
Yixiao Chen, Linfeng Zhang, Han Wang and Weinan E · 2020
Closest in time.
“OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features”
Zhuoran Qiao et al · 2020
Closest in time.
“Semiempirical Extended Tight-Binding Program Package” https://github.com/grimme-lab/xtb , 2020, accessed July 14, 2020
2020
Closest in time.
“Big data meets quantum chemistry approximations: the Δ \Delta -machine learning approach”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O von Lilienfeld · 2087
Closest in time.
“Big data meets quantum chemistry approximations: the Δ \Delta -machine learning approach”
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp and O von Lilienfeld · 2087
Closest in time.