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A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences.
High-precision sampling for brillouin-zone integration in metals
Methfessel, M. & Paxton, A · 1989
Earlier work this paper cites.
Ab initiomolecular dynamics for liquid metals
Kresse, G. & Hafner, J · 1993
Earlier work this paper cites.
Neural network models of potential energy surfaces
Blank, T. B., Brown, S. D., Calhoun, A. W. & Doren, D. J · 1995
Earlier work this paper cites.
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
Earlier work this paper cites.
Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
Perdew, J. P., Burke, K. & Ernzerhof, M · 1996
Earlier work this paper cites.
A stable thin-film lithium electrolyte: Lithium phosphorus oxynitride
Yu, X., Bates, J. B., Jellison, G. E. & Hart, F. X · 1997
Earlier work this paper cites.
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G. & Joubert, D · 1999
Earlier work this paper cites.
Development and testing of a general amber force field
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A · 2004
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
Hunter, J. D · 2007
Earlier work this paper cites.
Vesta: a three-dimensional visualization system for electronic and structural analysis
Momma, K. & Izumi, F · 2008
Earlier work this paper cites.
Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning
Handley, C. M., Hawe, G. I., Kell, D. B. & Popelier, P. L · 2009
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
Earlier work this paper cites.
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O. & Shaw, D. E · 2011
Earlier work this paper cites.
On representing chemical environments
Bartók, A. P., Kondor, R. & Csányi, G · 2013
Earlier work this paper cites.
Systematic parametrization of polarizable force fields from quantum chemistry data
Wang, L.-P., Chen, J. & Van Voorhis, T · 2013
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M. & Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Kokkos: Enabling manycore performance portability through polymorphic memory access patterns
Carter Edwards, H., Trott, C. R. & Sunderland, D · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
Earlier work this paper cites.
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J · 2015
Earlier work this paper cites.
Design and synthesis of the superionic conductor na 10 snp 2 s 12
Richards, W. D. et al · 2016
Earlier work this paper cites.
Moment tensor potentials: A class of systematically improvable interatomic potentials
Shapeev, A. V · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Hendrycks, D. & Gimpel, K · 2016
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
Smith, J. S., Isayev, O. & Roitberg, A. E · 2017
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
Cited alongside, same era.
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A · 2017
Cited alongside, same era.
Study of li atom diffusion in amorphous li3po4 with neural network potential
Li, W., Ando, Y., Minamitani, E. & Watanabe, S · 2017
Cited alongside, same era.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J., Giri, S., Margraf, J. T. & Günnemann, S · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Finzi, M., Stanton, S., Izmailov, P. & Wilson, A. G · 2020
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Relevance of rotationally equivariant convolutions for predicting molecular properties
Miller, B. K., Geiger, M., Smidt, T. E. & No’e, F · 2020
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Plasma synthesis of spherical crystalline and amorphous electrolyte nanopowders for solid-state batteries
Westover, A. S. et al · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V. & Welling, M · 2020
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Loshchilov, I. & Hutter, F · 2017
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Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
Chmiela, S., Sauceda, H. E., Müller, K.-R. & Tkatchenko, A · 2018
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Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
Zhang, L., Han, J., Wang, H., Car, R. & Weinan, E · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N. et al · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W. & Cohen, T. S · 2018
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N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials
Kondor, R · 2018
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2021
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Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Xie, Y., Vandermause, J., Sun, L., Cepellotti, A. & Kozinsky, B · 2021
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Active learning of reactive bayesian force fields: Application to heterogeneous catalysis dynamics of h/pt (2021)
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Linear atomic cluster expansion force fields for organic molecules: beyond rmse
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Unite: Unitary n-body tensor equivariant network with applications to quantum chemistry
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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86 pflops deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy
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Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales
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E (n) equivariant graph neural networks
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Geometric and physical quantities improve e (3) equivariant message passing
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Compressing local atomic neighbourhood descriptors
Darby, J. P., Kermode, J. R. & Csányi, G · 2021
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Gemnet: Universal directional graph neural networks for molecules
Klicpera, J., Becker, F. & Günnemann, S · 2021
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Spherical message passing for 3d graph networks
Liu, Y. et al · 2021
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Simple gnn regularisation for 3d molecular property prediction and beyond
Godwin, J. et al · 2021
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Resistance to fracture in the glassy solid electrolyte lipon
Kalnaus, S., Westover, A. S., Kornbluth, M., Herbert, E. & Dudney, N. J · 2021
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e3nn/e3nn: 2021-05-04 (2021)
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Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
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LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
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