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Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems.
Robert S Mulliken, “Electronic population analysis on lcao–mo molecular wave functions. i,” The Journal of chemical physics 23
1955
Earlier work this paper cites.
Donald E Williams, “Accelerated convergence of crystal-lattice potential sums,” Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography 27
1971
Earlier work this paper cites.
Fred L Hirshfeld, “Bonded-atom fragments for describing molecular charge densities,” Theoretica chimica acta 44
1977
Earlier work this paper cites.
Anthony K Rappe and William A Goddard III, “Charge equilibration for molecular dynamics simulations,” The Journal of Physical Chemistry 95
1991
Earlier work this paper cites.
David B. Laks, L. G. Ferreira, Sverre Froyen, and Alex Zunger, “Efficient cluster expansion for substitutional systems,” Phys. Rev. B 46
1992
Earlier work this paper cites.
Kenneth B Wiberg and Paul R Rablen, “Comparison of atomic charges derived via different procedures,” Journal of Computational Chemistry 14
1993
Earlier work this paper cites.
J Ilja Siepmann and Michiel Sprik, “Influence of surface topology and electrostatic potential on water/electrode systems,” The Journal of chemical physics 102
1995
Earlier work this paper cites.
John P Perdew, Kieron Burke, and Matthias Ernzerhof, “Generalized gradient approximation made simple,” Physical review letters 77
1996
Earlier work this paper cites.
Xavier Gonze and Changyol Lee, “Dynamical matrices, born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory,” Physical Review B 55
1997
Earlier work this paper cites.
C. Wolverton and Alex Zunger, “First-principles prediction of vacancy order-disorder and intercalation battery voltages in Li x
1998
Earlier work this paper cites.
Emil Prodan and Walter Kohn, “Nearsightedness of electronic matter,” Proceedings of the National Academy of Sciences 102
2005
Earlier work this paper cites.
Yujie Wu, Harald L Tepper, and Gregory A Voth, “Flexible simple point-charge water model with improved liquid-state properties,” The Journal of chemical physics 124
2006
Earlier work this paper cites.
Jörg Behler and Michele Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Physical review letters 98
2007
Earlier work this paper cites.
In Suk Joung and Thomas E Cheatham III, “Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations,” The journal of physical chemistry B 112
2008
Earlier work this paper cites.
Hannes Raebiger, Stephan Lany, and Alex Zunger, “Charge self-regulation upon changing the oxidation state of transition metals in insulators,” Nature 453
2008
Earlier work this paper cites.
Roger H French, V Adrian Parsegian, Rudolf Podgornik, Rick F Rajter, Anand Jagota, Jian Luo, Dilip Asthagiri, Manoj K Chaudhury, Yet-ming Chiang, Steve Granick, et al. , “Long range interactions in nanoscale science,” Reviews of Modern Physics 82
2010
Earlier work this paper cites.
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi, “Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,” Physical review letters 104
2010
Earlier work this paper cites.
Stefan Grimme, Jens Antony, Stephan Ehrlich, and Helge Krieg, “A consistent and accurate ab initio parametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu,” The Journal of chemical physics 132
2010
Earlier work this paper cites.
Aleksandr V Marenich, Steven V Jerome, Christopher J Cramer, and Donald G Truhlar, “Charge model 5: An extension of hirshfeld population analysis for the accurate description of molecular interactions in gaseous and condensed phases,” Journal of chemical theory and computation 8
2012
Earlier work this paper cites.
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L. Chevrier, Kristin A. Persson, and Gerbrand Ceder, “Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis,” Computational Materials Science 68
2013
Earlier work this paper cites.
Alexander V Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” Multiscale Modeling & Simulation 14
2016
Earlier work this paper cites.
Toon Verstraelen, Steven Vandenbrande, Farnaz Heidar-Zadeh, Louis Vanduyfhuys, Veronique Van Speybroeck, Michel Waroquier, and Paul W Ayers, “Minimal basis iterative stockholder: atoms in molecules for force-field development,” Journal of Chemical Theory and Computation 12
2016
Cited alongside, same era.
Yarin Gal and Zoubin Ghahramani, “A theoretically grounded application of dropout in recurrent neural networks,” Advances in neural information processing systems 29
2016
Cited alongside, same era.
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller, “Schnet: A continuous-filter convolutional neural network for modeling quantum interactions,” Advances in neural information processing systems 30
2017
Cited alongside, same era.
Lori A Burns, John C Faver, Zheng Zheng, Michael S Marshall, Daniel GA Smith, Kenno Vanommeslaeghe, Alexander D MacKerell, Kenneth M Merz, and C David Sherrill, “The biofragment database (bfdb): An open-data platform for computational chemistry analysis of noncovalent interactions,” The Journal of chemical physics 147
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky, “E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” Nature communications 13
2022
Later among the works it cites.
Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi, “Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,” Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, Oufan Zhang, Akshaya Das, Christopher J Stein, Farnaz Heidar-Zadeh, Meili Liu, Martin Head-Gordon, Luke Bertels, et al. , “Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces,” Digital Discovery 1
2022
Later among the works it cites.
Genevieve Dusson, Markus Bachmayr, Gábor Csányi, Ralf Drautz, Simon Etter, Cas van der Oord, and Christoph Ortner, “Atomic cluster expansion: Completeness, efficiency and stability,” Journal of Computational Physics 454
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2017
Cited alongside, same era.
Andrew E Sifain, Nicholas Lubbers, Benjamin T Nebgen, Justin S Smith, Andrey Y Lokhov, Olexandr Isayev, Adrian E Roitberg, Kipton Barros, and Sergei Tretiak, “Discovering a transferable charge assignment model using machine learning,” The journal of physical chemistry letters 9
2018
Cited alongside, same era.
Aron Walsh, Alexey A. Sokol, John Buckeridge, David O. Scanlon, and C. Richard A. Catlow, “Oxidation states and ionicity,” Nature Materials 17
2018
Cited alongside, same era.
Brian J Kirby and Pavel Jungwirth, “Charge scaling manifesto: A way of reconciling the inherently macroscopic and microscopic natures of molecular simulations,” The journal of physical chemistry letters 10
2019
Cited alongside, same era.
Andrea Grisafi and Michele Ceriotti, “Incorporating long-range physics in atomic-scale machine learning,” The Journal of chemical physics 151
2019
Cited alongside, same era.
Oliver T Unke and Markus Meuwly, “Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,” Journal of chemical theory and computation 15
2019
Cited alongside, same era.
Ralf Drautz, “Atomic cluster expansion for accurate and transferable interatomic potentials,” Physical Review B 99
2019
Cited alongside, same era.
Sergey N Pozdnyakov, Michael J Willatt, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti, “Incompleteness of atomic structure representations,” Physical Review Letters 125
2020
Cited alongside, same era.
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus, “Deep evidential regression,” Advances in neural information processing systems 33
2020
Cited alongside, same era.
2022
Later among the works it cites.
Jonathan Vandermause, Yu Xie, Jin Soo Lim, Cameron J. Owen, and Boris Kozinsky, “Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt,” Nature Communications 13
2022
Later among the works it cites.
Kevin K Huguenin-Dumittan, Philip Loche, Ni Haoran, and Michele Ceriotti, “Physics-inspired equivariant descriptors of nonbonded interactions,” The Journal of Physical Chemistry Letters 14
2023
Later among the works it cites.
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, and Stephan Günnemann, “Ewald-based long-range message passing for molecular graphs,” in International Conference on Machine Learning (PMLR, 2023) pp. 17544–17563
2023
Later among the works it cites.
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder, “Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,” Nature Machine Intelligence 5
2023
Later among the works it cites.
Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, et al. , “Spice, a dataset of drug-like molecules and peptides for training machine learning potentials,” Scientific Data 10
2023
Later among the works it cites.
Sunny Gupta, Xiaochen Yang, and Gerbrand Ceder, “What dictates soft clay-like lithium superionic conductor formation from rigid salts mixture,” Nature Communications 14
2023
Later among the works it cites.
Albert Zhu, Simon Batzner, Albert Musaelian, and Boris Kozinsky, “Fast uncertainty estimates in deep learning interatomic potentials,” The Journal of Chemical Physics 158
2023
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Yusuf Shaidu, Franco Pellegrini, Emine Küçükbenli, Ruggero Lot, and Stefano de Gironcoli, “Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients,” npj Computational Materials 10
2024
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