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Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials.
Self-consistent equations including exchange and correlation effects
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Gaussian basis sets for molecular calculations
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The use of global and local molecular parameters for the analysis of the gas-phase basicity of amines
Weitao Yang and Wilfried J Mortier · 1986
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Helix stabilization by glu-… lys+ salt bridges in short peptides of de novo design
Susan Marqusee and Robert L Baldwin · 1987
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Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
Chengteh Lee, Weitao Yang, and Robert G Parr · 1988
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Gaussian-2 theory for molecular energies of first-and second-row compounds
Larry A Curtiss, Krishnan Raghavachari, Gary W Trucks, and John A Pople · 1991
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Jun John Sakurai and Eugene D Commins · 1995
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Attila Szabo and Neil S. Ostlund · 1996
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Multiplicity, invariants, and tensor product decompositions of compact groups
WH Klink and T Ton-That · 1996
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Evaluation of the rotation matrices in the basis of real spherical harmonics
Miguel A Blanco, Manuel Flórez, and Margarita Bermejo · 1997
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Computing redox potentials in solution: Density functional theory as a tool for rational design of redox agents
Mu-Hyun Baik and Richard A Friesner · 2002
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Gelfand-tsetlin bases for classical lie algebras
Alexander I Molev · 2002
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Virtual screening of chemical libraries
Brian K Shoichet · 2004
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Empirical force fields for biological macromolecules: overview and issues
Alexander D MacKerell Jr · 2004
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Benchmark database of accurate (MP2 and CCSD(t) complete basis set limit) interaction energies of small model complexes, DNA base pairs, and amino acid pairs
Petr Jurečka, Jiří Šponer, Jiří Černý, and Pavel Hobza · 2006
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Experimental and computational investigation of unsymmetrical cyanine dyes: understanding torsionally responsive fluorogenic dyes
Gloria L Silva, Volkan Ediz, David Yaron, and Bruce A Armitage · 2007
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Algorithms for computing u (n) clebsch gordan coefficients
S Gliske, W Klink, and T Ton-That · 2007
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Hartree–fock exchange fitting basis sets for h to rn
Florian Weigend · 2008
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2011
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W4-11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles w4 data
Amir Karton, Shauli Daon, and Jan ML Martin · 2011
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Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J Bygrave, Daniel GA Smith, Animashree Anandkumar, Frederick R Manby, and Thomas F Miller III · 2011
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A numerical algorithm for the explicit calculation of su (n) and sl (n, c) clebsch–gordan coefficients
Arne Alex, Matthias Kalus, Alan Huckleberry, and Jan von Delft · 2011
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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 R Fogueri, Sebastian Kozuch, Amir Karton, and Jan ML 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 theory for mathematicians , volume 267
Brian C Hall · 2013
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Representation theory: a first course , volume 129
William Fulton and Joe Harris · 2013
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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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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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The electron is a catalyst
Armido Studer and Dennis P Curran · 2014
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Molecular electronic-structure theory
Trygve Helgaker, Poul Jorgensen, and Jeppe Olsen · 2014
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Implementation of the hungarian algorithm to account for ligand symmetry and similarity in structure-based design
William J Allen and Robert C Rizzo · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Big data meets quantum chemistry approximations: the Δ \Delta -machine learning approach
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2015
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Accurate description of intermolecular interactions involving ions using symmetry-adapted perturbation theory
Ka Un Lao, Rainer Schäffer, Georg Jansen, and John M Herbert · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fast r-cnn
Ross Girshick · 2015
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Perspective: Machine learning potentials for atomistic simulations
Jörg Behler · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Revised damping parameters for the d3 dispersion correction to density functional theory
Daniel GA Smith, Lori A Burns, Konrad Patkowski, and C David Sherrill · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 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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Data-driven advancement of homogeneous nickel catalyst activity for aryl ether cleavage
Manuel Cordova, Matthew D Wodrich, Benjamin Meyer, Boodsarin Sawatlon, and Clemence Corminboeuf · 2020
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FCHL revisited: Faster and more accurate quantum machine learning
Anders S Christensen, Lars A Bratholm, Felix A Faber, and O Anatole von Lilienfeld · 2020
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Transferable multi-level attention neural network for accurate prediction of quantum chemistry properties via multi-task learning
Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, and Jing Ma · 2020
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Evidence for supercritical behaviour of high-pressure liquid hydrogen
Bingqing Cheng, Guglielmo Mazzola, Chris J Pickard, and Michele Ceriotti · 2020
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Assessing conformer energies using electronic structure and machine learning methods
Dakota Folmsbee and Geoffrey Hutchison · 2020
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Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
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
Cited alongside, same era.
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 · 2017
Cited alongside, same era.
Machine learning unifies the modeling of materials and molecules
Albert P Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R Kermode, Gábor Csányi, and Michele Ceriotti · 2017
Cited alongside, same era.
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
Cited alongside, same era.
The BioFragment database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions
Lori A. Burns, John C. Faver, Zheng Zheng, Michael S. Marshall, Daniel G. A. Smith, Kenno Vanommeslaeghe, Alexander D. MacKerell, Kenneth M. Merz, and C. David Sherrill · 2017
Cited alongside, same era.
A look at the density functional theory zoo with the advanced gmtkn55 database for general main group thermochemistry, kinetics and noncovalent interactions
Lars Goerigk, Andreas Hansen, Christoph Bauer, Stephan Ehrlich, Asim Najibi, and Stefan Grimme · 2017
Cited alongside, same era.
Completing density functional theory by machine learning hidden messages from molecules
Ryo Nagai, Ryosuke Akashi, and Osamu Sugino · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian B Fuchs, Daniel E Worrall, Volker Fischer, and Max Welling · 2020
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Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization
Jon Paul Janet, Sahasrajit Ramesh, Chenru Duan, and Heather J Kulik · 2020
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Quantum machine learning using atom-in-molecule-based fragments selected on the fly
Bing Huang and O. Anatole von Lilienfeld · 2020
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Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
Max Veit, David M Wilkins, Yang Yang, Robert A DiStasio Jr, and Michele Ceriotti · 2020
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On the role of gradients for machine learning of molecular energies and forces
Anders S Christensen and O Anatole von Lilienfeld · 2020
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Deepdft: Neural message passing network for accurate charge density prediction
Peter Bjørn Jørgensen and Arghya Bhowmik · 2020
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Extending the applicability of the ani deep learning molecular potential to sulfur and halogens
Christian Devereux, Justin S Smith, Kate K Davis, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, and Adrian E Roitberg · 2020
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The roles of long-range proton-coupled electron transfer in the directionality and efficiency of [fefe]-hydrogenases
Oliver Lampret, Jifu Duan, Eckhard Hofmann, Martin Winkler, Fraser A Armstrong, and Thomas Happe · 2020
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URL https://doi.org/10.6019/chembl.database.27
CHEMBL database release 27, May 2020 · 2020
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An open source chemical structure curation pipeline using RDKit
A. Patrícia Bento, Anne Hersey, Eloy Félix, Greg Landrum, Anna Gaulton, Francis Atkinson, Louisa J. Bellis, Marleen De Veij, and Andrew R. Leach · 2020
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A wigner-eckart theorem for group equivariant convolution kernels
Leon Lang and Maurice Weiler · 2020
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https://github.com/grimme-lab/xtb
Semiempirical Extended Tight-Binding Program Package , 2020, accessed July 14, 2020 · 2020
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Torchani: A free and open source pytorch-based deep learning implementation of the ani neural network potentials
Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S Smith, and Adrian E Roitberg · 2020
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Spherical message passing for 3d graph networks
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T Schütt, Oliver T Unke, and Michael Gastegger · 2021
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Geometric deep learning of rna structure
Raphael JL Townshend, Stephan Eismann, Andrew M Watkins, Ramya Rangan, Maria Karelina, Rhiju Das, and Ron O Dror · 2021
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Modeling of peptides with classical and novel machine learning force fields: A comparison
David Rosenberger, Justin S Smith, and Angel E Garcia · 2021
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Kohn-sham equations as regularizer: Building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D Cubuk, Patrick Riley, Kieron Burke, et al · 2021
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Pushing the frontiers of density functionals by solving the fractional electron problem
James Kirkpatrick, Brendan McMorrow, David HP Turban, Alexander L Gaunt, James S Spencer, Alexander GDG Matthews, Annette Obika, Louis Thiry, Meire Fortunato, David Pfau, et al · 2021
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An orbital-based representation for accurate quantum machine learning
Konstantin Karandashev and O Anatole von Lilienfeld · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Extended tight-binding quantum chemistry methods
Christoph Bannwarth, Eike Caldeweyher, Sebastian Ehlert, Andreas Hansen, Philipp Pracht, Jakob Seibert, Sebastian Spicher, and Stefan Grimme · 2021
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δ \delta -quantum machine learning for medicinal chemistry
Kenneth Atz, Clemens Isert, Markus N. A. Böcker, José Jiménez-Luna, and Gisbert Schneider · 2021
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Tess E Smidt, Lixin Sun, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, and Boris Kozinsky · 2021
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Artificial intelligence-enhanced quantum chemical method with broad applicability
Peikun Zheng, Roman Zubatyuk, Wei Wu, Olexandr Isayev, and Pavlo O Dral · 2021
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r2scan-3c: A “swiss army knife” composite electronic-structure method
Stefan Grimme, Andreas Hansen, Sebastian Ehlert, and Jan-Michael Mewes · 2021
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A review of the recent progress in battery informatics
Chen Ling · 2022
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Inclusion of more physics leads to less data: Learning the interaction energy as a function of electron deformation density with limited training data
Kaycee Low, Michelle L Coote, and Ekaterina I Izgorodina · 2022
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Torsionnet: A deep neural network to rapidly predict small-molecule torsional energy profiles with the accuracy of quantum mechanics
Brajesh K. Rai, Vishnu Sresht, Qingyi Yang, Ray Unwalla, Meihua Tu, Alan M. Mathiowetz, and Gregory A. Bakken · 2022
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Machine learning accurate exchange and correlation functionals of the electronic density
Sebastian Dick and Marivi Fernandez-Serra · 2041
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