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One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality.
Nijboer, B. R. A.; De Wette, F. W. On the Calculation of Lattice Sums. Physica 1957
1957
Earlier work this paper cites.
Williams, D. E. Accelerated Convergence of Crystal-Lattice Potential Sums. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography 1971
1971
Earlier work this paper cites.
Eastwood, R. W. H., J. W. Computer Simulation Using Particles ; CRC Press, 1988
1988
Earlier work this paper cites.
Williams, D. E. Accelerated Convergence Treatment of R-n Lattice Sums. Crystallography Reviews 1989
1989
Earlier work this paper cites.
Darden, T.; York, D.; Pedersen, L. Particle Mesh Ewald: An N ⋅ \cdot log(N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993
1993
Earlier work this paper cites.
Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 1995
1995
Earlier work this paper cites.
Kohn, W. Density Functional and Density Matrix Method Scaling Linearly with the Number of Atoms. Phys. Rev. Lett. 1996
1996
Earlier work this paper cites.
Jackson, J. D. Classical Electrodynamics Third Edition , 3rd ed.; Wiley: New York, 1998
1998
Earlier work this paper cites.
Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications , 2nd ed.; Computational Science Series 1; Academic Press: San Diego, 2002
2002
Earlier work this paper cites.
Heyd, J.; Scuseria, G. E.; Ernzerhof, M. Hybrid Functionals Based on a Screened Coulomb Potential. J. Chem. Phys. 2003
2003
Earlier work this paper cites.
Prodan, E.; Kohn, W. Nearsightedness of Electronic Matter. Proceedings of the National Academy of Sciences 2005
2005
Earlier work this paper cites.
Behler, J.; Parrinello, M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007
2007
Earlier work this paper cites.
Bartók, A. P.; Payne, M. C.; Kondor, R.; Csányi, G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Phys. Rev. Lett. 2010
2010
Earlier work this paper cites.
Israelachvili, J. N. Intermolecular and Surface Forces (Third Edition) , third edition ed.; Academic Press: San Diego, 2011
2011
Earlier work this paper cites.
Rupp, M.; Tkatchenko, A.; Müller, K.-R.; von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Phys. Rev. Lett. 2012
2012
Earlier work this paper cites.
Bartók, A. P.; Kondor, R.; Csányi, G. On Representing Chemical Environments. Phys. Rev. B 2013
2013
Earlier work this paper cites.
Hansen, J.-P., McDonald, I. R., Eds. Theory of Simple Liquids (Fourth Edition) ; Academic Press: Oxford, 2013; p i
2013
Earlier work this paper cites.
Kanduč, M.; Schneck, E.; Netz, R. R. Attraction between Hydrated Hydrophilic Surfaces. Chemical Physics Letters 2014
2014
Cited alongside, same era.
Ambrosetti, A.; Ferri, N.; DiStasio, R. A.; Tkatchenko, A. Wavelike Charge Density Fluctuations and van Der Waals Interactions at the Nanoscale. Science 2016
2016
Cited alongside, same era.
Glielmo, A.; Sollich, P.; De Vita, A. Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels. Phys. Rev. B 2017
2017
Cited alongside, same era.
Brockherde, F.; Vogt, L.; Li, L.; Tuckerman, M. E.; Burke, K.; Müller, K. R. Bypassing the Kohn-Sham Equations with Machine Learning. Nat. Commun. 2017
2017
Cited alongside, same era.
Carleo, G.; Troyer, M. Solving the Quantum Many-Body Problem with Artificial Neural Networks. Science 2017
2017
Cited alongside, same era.
Hermann, J.; Schätzle, Z.; Noé, F. Deep-Neural-Network Solution of the Electronic Schrödinger Equation. Nat. Chem. 2020
2020
Later among the works it cites.
Deringer, V. L.; Caro, M. A.; Csányi, G. A General-Purpose Machine-Learning Force Field for Bulk and Nanostructured Phosphorus. Nat Commun 2020
2020
Later among the works it cites.
Nigam, J.; Pozdnyakov, S.; Ceriotti, M. Recursive Evaluation and Iterative Contraction of N -Body Equivariant Features. J. Chem. Phys. 2020
2020
Later among the works it cites.
Hermann, J.; Tkatchenko, A. Density Functional Model for van Der Waals Interactions: Unifying Many-Body Atomic Approaches with Nonlocal Functionals. Phys. Rev. Lett. 2020
2020
Later among the works it cites.
Musil, F.; Grisafi, A.; Bartók, A. P.; Ortner, C.; Csányi, G.; Ceriotti, M. Physics-Inspired Structural Representations for Molecules and Materials. Chem. Rev. 2021
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Faraji, S.; Ghasemi, S. A.; Rostami, S.; Rasoulkhani, R.; Schaefer, B.; Goedecker, S.; Amsler, M. High Accuracy and Transferability of a Neural Network Potential through Charge Equilibration for Calcium Fluoride. Phys. Rev. B 2017
2017
Cited alongside, same era.
Burns, L. A.; Faver, J. C.; Zheng, Z.; Marshall, M. S.; Smith, D. G. A.; Vanommeslaeghe, K.; MacKerell, A. D.; Merz, K. M.; Sherrill, C. D. The BioFragment Database (BFDb): An Open-Data Platform for Computational Chemistry Analysis of Noncovalent Interactions. J. Chem. Phys. 2017
2017
Cited alongside, same era.
Grisafi, A.; Wilkins, D. M.; Csányi, G.; Ceriotti, M. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. Phys. Rev. Lett. 2018
2018
Cited alongside, same era.
Alred, J. M.; Bets, K. V.; Xie, Y.; Yakobson, B. I. Machine Learning Electron Density in Sulfur Crosslinked Carbon Nanotubes. Composites Science and Technology 2018
2018
Cited alongside, same era.
Wilkins, D. M.; Grisafi, A.; Yang, Y.; Lao, K. U.; DiStasio, R. A.; Ceriotti, M. Accurate Molecular Polarizabilities with Coupled Cluster Theory and Machine Learning. PNAS 2019
2019
Cited alongside, same era.
Fabrizio, A.; Grisafi, A.; Meyer, B.; Ceriotti, M.; Corminboeuf, C. Electron Density Learning of Non-Covalent Systems. Chemical Science 2019
2019
Cited alongside, same era.
Schütt, K. T.; Gastegger, M.; Tkatchenko, A.; Müller, K.-R.; Maurer, R. J. Unifying Machine Learning and Quantum Chemistry with a Deep Neural Network for Molecular Wavefunctions. Nat Commun 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
Lewis, A. M.; Grisafi, A.; Ceriotti, M.; Rossi, M. Learning Electron Densities in the Condensed Phase. J. Chem. Theory Comput. 2021
2021
Later among the works it cites.
Niblett, S. P.; Galib, M.; Limmer, D. T. Learning Intermolecular Forces at Liquid–Vapor Interfaces. J. Chem. Phys. 2021
2021
Later among the works it cites.
Ko, T. W.; Finkler, J. A.; Goedecker, S.; Behler, J. A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-Local Charge Transfer. Nat Commun 2021
2021
Later among the works it cites.
Ko, T. W.; Finkler, J. A.; Goedecker, S.; Behler, J. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer. Acc. Chem. Res. 2021
2021
Later among the works it cites.
Nigam, J.; Willatt, M. J.; Ceriotti, M. Equivariant Representations for Molecular Hamiltonians and N-center Atomic-Scale Properties. J. Chem. Phys. 2022
2022
Later among the works it cites.
Zhang, L.; Wang, H.; Muniz, M. C.; Panagiotopoulos, A. Z.; Car, R.; E, W. A Deep Potential Model with Long-Range Electrostatic Interactions. J. Chem. Phys. 2022
2022
Later among the works it cites.
Gao, A.; Remsing, R. C. Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials. Nat Commun 2022
2022
Later among the works it cites.
Peng, Y.; Lin, L.; Ying, L.; Zepeda-Núñez, L. Efficient Long-Range Convolutions for Point Clouds. Journal of Computational Physics 2023
2023
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2023
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