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Solving the Schr\"odinger equation is key to many quantum mechanical properties.
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
Yaolong Zhang, Ce Hu, and Bin Jiang · 1948
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Ground State of Liquid He4
W. L. McMillan · 1965
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Monte Carlo simulation of a many-fermion study
D. Ceperley, G. V. Chester, and M. H. Kalos · 1977
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Accurately solving the electronic Schrödinger equation of atoms and molecules using explicitly correlated (r12-)MR-CI: The ground state potential energy curve of N2
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An accurate analytic potential function for ground-state N2 from a direct-potential-fit analysis of spectroscopic data
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Computational Investigation of the Conrotatory and Disrotatory Isomerization Channels of Bicyclo[1.1.0]butane to Buta-1,3-diene: A Completely Renormalized Coupled-Cluster Study
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H4+: What do we know about it?
Alexander Alijah and António J. C. Varandas · 2008
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Deep learning via Hessian-free optimization
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Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Jörg Behler · 2011
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Multireference Nature of Chemistry: The Coupled-Cluster View
Dmitry I. Lyakh, Monika Musiał, Victor F. Lotrich, and Rodney J. Bartlett · 2012
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Optimizing large parameter sets in variational quantum Monte Carlo
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Combining active-space coupled-cluster methods with moment energy corrections via the CC( P ; Q ) methodology, with benchmark calculations for biradical transition states
Jun Shen and Piotr Piecuch · 2012
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Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory
Attila Szabo and Neil S. Ostlund · 2012
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On representing chemical environments
Albert P. Bartók, Risi Kondor, and Gábor Csányi · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2014
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Solving the Quantum Many-Body Problem with Artificial Neural Networks
Giuseppe Carleo and Matthias Troyer · 2017
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On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Towards the Solution of the Many-Electron Problem in Real Materials: Equation of State of the Hydrogen Chain with State-of-the-Art Many-Body Methods
Mario Motta, David M. Ceperley, Garnet Kin-Lic Chan, John A. Gomez, Emanuel Gull, Sheng Guo, Carlos A. Jiménez-Hoyos, Tran Nguyen Lan, Jia Li, Fengjie Ma, Andrew J. Millis, Nikolay V. Prokof’ev, Ushnish Ray, Gustavo E. Scuseria, Sandro Sorella, Edwin M. Stoudenmire, Qiming Sun, Igor S. Tupitsyn, Steven R. White, Dominika Zgid, Shiwei Zhang, and Simons Collaboration on the Many-Electron Problem · 2017
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JAX: Composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
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Ab initio solution of the many-electron Schrödinger equation with deep neural networks
David Pfau, James S. Spencer, Alexander G. D. G. Matthews, and W. M. C. Foulkes · 2020
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OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
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Better, Faster Fermionic Neural Networks
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Quantum Deep Field: Data-Driven Wave Function, Electron Density Generation, and Atomization Energy Prediction and Extrapolation with Machine Learning
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SchNet – A deep learning architecture for molecules and materials
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The Hippocratic License 2.1: An Ethical License for Open Source
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Directional Message Passing for Molecular Graphs
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Analyzing Learned Molecular Representations for Property Prediction
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Scalable variational Monte Carlo with graph neural ansatz
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SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
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