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The rational design of molecules with desired properties is a long-standing challenge in chemistry.
Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis
S. H. Vosko, L. Wilk, and M. Nusair · 1980
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
D. Weininger · 1988
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LYP correlation: Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
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Density-functional thermochemistry. III. the role of exact exchange
A. D. Becke · 1993
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Integral approximations for LCAO-SCF calculations
O. Vahtras, J. Almlöf, and M. W. Feyereisen · 1993
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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
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Auxiliary basis sets to approximate Coulomb potentials
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Generalized gradient approximation made simple
J. P. Perdew, K. Burke, and M. Ernzerhof · 1996
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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
F. Weigend and R. Ahlrichs · 2005
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Electrodes with high power and high capacity for rechargeable lithium batteries
K. Kang, Y. S. Meng, J. Breger, C. P. Grey, and G. Ceder · 2006
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Design rules for donors in bulk-heterojunction solar cells—towards 10% energy-conversion efficiency
M. C. Scharber, D. Mühlbacher, M. Koppe, P. Denk, C. Waldauf, A. J. Heeger, and C. J. Brabec · 2006
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A decade of fragment-based drug design: strategic advances and lessons learned
P. J. Hajduk and J. Greer · 2007
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Generalized neural-network representation of high-dimensional potential-energy surfaces
J. Behler and M. Parrinello · 2007
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Rational drug design
S. Mandal, M. Moudgil, and S. K. Mandal · 2009
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Efficient, approximate and parallel hartree–fock and hybrid dft calculations. a ‘chain-of-spheres’ algorithm for the hartree–fock exchange
F. Neese, F. Wennmohs, A. Hansen, and U. Becker · 2009
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Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations
G. Hautier, A. Jain, H. Chen, C. Moore, S. P. Ong, and G. Ceder · 2011
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Open babel: An open chemical toolbox
N. M. O’Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, and G. R. Hutchison · 2011
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Mayavi: 3D Visualization of Scientific Data
P. Ramachandran and G. Varoquaux · 2011
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Fast and accurate modeling of molecular atomization energies with machine learning
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld · 2012
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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
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The ORCA program system
F. Neese · 2012
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Inverse design of high absorption thin-film photovoltaic materials
L. Yu, R. S. Kokenyesi, D. A. Keszler, and A. Zunger · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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The chemical space project
J.-L. Reymond · 2015
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Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes
M. Gastegger, C. Kauffmann, J. Behler, and P. Marquetand · 2016
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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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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 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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Machine learning for molecular and materials science
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh · 2018
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SchNet – A deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
Directional message passing for molecular graphs
J. Klicpera, J. Groß, and S. Günnemann · 2020
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FCHL revisited: Faster and more accurate quantum machine learning
A. S. Christensen, L. A. Bratholm, F. A. Faber, and O. Anatole von Lilienfeld · 2020
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A generative model for molecular distance geometry
G. Simm and J. M. Hernandez-Lobato · 2020
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Torsionnet: A reinforcement learning approach to sequential conformer search
T. Gogineni, Z. Xu, E. Punzalan, R. Jiang, J. Kammeraad, A. Tewari, and P. Zimmerman · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
J. Köhler, L. Klein, and F. Noe · 2020
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Improved protein structure prediction using potentials from deep learning
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. Nelson, A. Bridgland, et al · 2020
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S. Chmiela, H. E. Sauceda, K.-R. Müller, and A. Tkatchenko · 2018
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Inverse design in search of materials with target functionalities
A. Zunger · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
B. Sanchez-Lengeling and A. Aspuru-Guzik · 2018
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Learning protein structure with a differentiable simulator
J. Ingraham, A. Riesselman, C. Sander, and D. Marks · 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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Search for catalysts by inverse design: Artificial intelligence, mountain climbers, and alchemists
J. G. Freeze, H. R. Kelly, and V. S. Batista · 2019
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PhysNet: a neural network for predicting energies, forces, dipole moments, and partial charges
O. T. Unke and M. Meuwly · 2019
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3DMolNet: a generative network for molecular structures
V. Nesterov, M. Wieser, and V. Roth · 2020
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Reinforcement learning for molecular design guided by quantum mechanics
G. Simm, R. Pinsler, and J. M. Hernandez-Lobato · 2020
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Quantum machine learning using atom-in-molecule-based fragments selected on the fly
B. Huang and O. A. von Lilienfeld · 2020
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Machine learning force fields
O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko, and K.-R. Müller · 2021
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Perspective on integrating machine learning into computational chemistry and materials science
J. Westermayr, M. Gastegger, K. T. Schütt, and R. J. Maurer · 2021
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Machine learning meets chemical physics, 2021
M. Ceriotti, C. Clementi, and O. Anatole von Lilienfeld · 2021
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Combining machine learning and computational chemistry for predictive insights into chemical systems
J. A. Keith, V. Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger, K.-R. Müller, and A. Tkatchenko · 2021
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
S. Batzner, T. E. Smidt, L. Sun, J. P. Mailoa, M. Kornbluth, N. Molinari, and B. Kozinsky · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
K. Schütt, O. Unke, and M. Gastegger · 2021
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GeoMol: Torsional geometric generation of molecular 3d conformer ensembles
O.-E. Ganea, L. Pattanaik, C. W. Coley, R. Barzilay, K. F. Jensen, W. H. Green, and T. S. Jaakkola · 2021
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Machine learning based energy-free structure predictions of molecules, transition states, and solids
D. Lemm, G. F. von Rudorff, and O. A. von Lilienfeld · 2021
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Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
M. Stieffenhofer, T. Bereau, and M. Wand · 2021
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Highly accurate protein structure prediction with AlphaFold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, and D. Hassabis · 2021
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Symmetry-aware actor-critic for 3d molecular design
G. N. C. Simm, R. Pinsler, G. Csányi, and J. M. Hernández-Lobato · 2021
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Learning to design drug-like molecules in three-dimensional space using deep generative models
Y. Li, J. Pei, and L. Lai · 2021
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3D-Scaffold: A deep learning framework to generate 3d coordinates of drug-like molecules with desired scaffolds
R. P. Joshi, N. W. A. Gebauer, M. Bontha, M. Khazaieli, R. M. James, J. B. Brown, and N. Kumar · 2021
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E (n) equivariant normalizing flows for molecule generation in 3d
V. G. Satorras, E. Hoogeboom, F. B. Fuchs, I. Posner, and M. Welling · 2021
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RDKit: Open-source cheminformatics
RDKit, online · 2021
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Generating stable molecules using imitation and reinforcement learning
S. A. Meldgaard, J. Köhler, H. L. Mortensen, M.-P. V. Christiansen, F. Noé, and B. Hammer · 2022
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