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Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis
S. H. Vosko, L. Wilk, and M. Nusair · 1980
Earlier work this paper cites.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
D. Weininger · 1988
Earlier work this paper cites.
LYP correlation: Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
C. Lee, W. Yang, and R. G. Parr · 1988
Earlier work this paper cites.
Density-functional thermochemistry. III. the role of exact exchange
A. D. Becke · 1993
Earlier work this paper cites.
Integral approximations for LCAO-SCF calculations
O. Vahtras, J. Almlöf, and M. W. Feyereisen · 1993
Earlier work this paper cites.
Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields
P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch · 1994
Earlier work this paper cites.
Auxiliary basis sets to approximate Coulomb potentials
K. Eichkorn, O. Treutler, H. Öhm, M. Häser, and R. Ahlrichs · 1995
Earlier work this paper cites.
Generalized gradient approximation made simple
J. P. Perdew, K. Burke, and M. Ernzerhof · 1996
Earlier work this paper cites.
Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy
F. Weigend and R. Ahlrichs · 2005
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
J. Behler and M. Parrinello · 2007
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi · 2010
Earlier work this paper cites.
A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
S. Grimme, J. Antony, S. Ehrlich, and H. Krieg · 2010
Earlier work this paper cites.
Mayavi: 3D Visualization of Scientific Data
P. Ramachandran and G. Varoquaux · 2011
Earlier work this paper cites.
Open babel: An open chemical toolbox
N. M. O’Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, and G. R. Hutchison · 2011
Earlier work this paper cites.
Fast and accurate modeling of molecular atomization energies with machine learning
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld · 2012
Earlier work this paper cites.
Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
L. Ruddigkeit, R. Van Deursen, L. C. Blum, and J.-L. Reymond · 2012
Earlier work this paper cites.
The ORCA program system
F. Neese · 2012
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
Earlier work this paper cites.
Massively multitask networks for drug discovery
B. Ramsundar, S. Kearnes, P. Riley, D. Webster, D. Konerding, and V. Pande · 2015
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The chemical space project
J.-L. Reymond · 2015
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
J. You, B. Liu, Z. Ying, V. Pande, and J. Leskovec · 2018
Later among the works it cites.
Learning representations and generative models for 3d point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas · 2018
Later among the works it cites.
Syntax-directed variational autoencoder for structured data
H. Dai, Y. Tian, B. Dai, S. Skiena, and L. Song · 2018
Later among the works it cites.
Learning a generative model for validity in complex discrete structures
D. Janz, J. van der Westhuizen, B. Paige, M. Kusner, and J. M. H. Lobato · 2018
Later among the works it cites.
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A. van den Oord, N. Kalchbrenner, L. Espeholt, K. Kavukcuoglu, O. Vinyals, and A. Graves · 2016
Cited alongside, same era.
Machine learning of accurate energy-conserving molecular force fields
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller · 2017
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Machine learning molecular dynamics for the simulation of infrared spectra
M. Gastegger, J. Behler, and P. Marquetand · 2017
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Solid harmonic wavelet scattering: Predicting quantum molecular energy from invariant descriptors of 3d electronic densities
M. Eickenberg, G. Exarchakis, M. Hirn, and S. Mallat · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Grammar variational autoencoder
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato · 2017
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Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models
G. L. Guimaraes, B. Sanchez-Lengeling, P. L. C. Farias, and A. Aspuru-Guzik · 2017
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M. H. S. Segler, T. Kogej, C. Tyrchan, and M. P. Waller · 2018
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Deep reinforcement learning for de novo drug design
M. Popova, O. Isayev, and A. Tropsha · 2018
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Molecular generative model based on conditional variational autoencoder for de novo molecular design
J. Lim, S. Ryu, J. W. Kim, and W. Y. Kim · 2018
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Machine learning-based screening of complex molecules for polymer solar cells
P. B. Jørgensen, M. Mesta, S. Shil, J. M. García Lastra, K. W. Jacobsen, K. S. Thygesen, and M. N. Schmidt · 2018
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Generating equilibrium molecules with deep neural networks
N. W. A. Gebauer, M. Gastegger, and K. T. Schütt · 2018
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GraphVAE: Towards generation of small graphs using variational autoencoders
M. Simonovsky and N. Komodakis · 2018
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Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
K. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, and R. J. Maurer · 2019
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
E. V. Podryabinkin, E. V. Tikhonov, A. V. Shapeev, and A. R. Oganov · 2019
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Learning multimodal graph-to-graph translation for molecular optimization
W. Jin, K. Yang, R. Barzilay, and T. Jaakkola · 2019
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Molecular geometry prediction using a deep generative graph neural network
E. Mansimov, O. Mahmood, S. Kang, and K. Cho · 2019
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SchNetPack
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K.-R. Müller · 2019
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PyTorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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RDKit: Open-source cheminformatics
RDKit, online · 2019
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NeVAE: A deep generative model for molecular graphs
B. Samanta, D. Abir, G. Jana, P. K. Chattaraj, N. Ganguly, and M. Gomez-Rodriguez · 2019
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