Fetching the paper…
Reading the bibliography…
Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling for tasks such as accurately but inexpensively modeling complex potential energy surfaces to guiding the sampling of complex dynamical systems or forecasting their time evolution.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 1901
Earlier work this paper cites.
Equivariant hamiltonian flows, 2019
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 1909
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Distance geometry: Theory, algorithms, and chemical applications
Timothy F Havel · 1998
Earlier work this paper cites.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2003
Earlier work this paper cites.
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2003
Earlier work this paper cites.
Cmu motion capture database
CMU · 2003
Earlier work this paper cites.
Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Velickovic · 2004
Earlier work this paper cites.
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Jörg Behler · 2011
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 2011
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
Earlier work this paper cites.
Variational inference with normalizing flows, 2016
Danilo Jimenez Rezende and Shakir Mohamed · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, 2017
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
Continuously differentiable exponential linear units, 2017
Jonathan T. Barron · 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.
Density estimation using real nvp, 2017
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Augmented normalizing flows: Bridging the gap between generative flows and latent variable models, 2020
Chin-Wei Huang, Laurent Dinh, and Aaron Courville · 2020
Later among the works it cites.
E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Later among the works it cites.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, 2021
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2021
Later among the works it cites.
Geometric and physical quantities improve E(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik Bekkers, and Max Welling · 2021
Later among the works it cites.
Scalars are universal: Equivariant machine learning, structured like classical physics, 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven M. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Capturing intensive and extensive dft/tddft molecular properties with machine learning
Wiktor Pronobis, Kristof T Schütt, Alexandre Tkatchenko, and Klaus-Robert Müller · 2018
Cited alongside, same era.
Neural relational inference for interacting systems, 2018
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
Cited alongside, same era.
Continuous-time flows for efficient inference and density estimation, 2018
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, and Lawrence Carin · 2018
Cited alongside, same era.
Cormorant: Covariant molecular neural networks, 2019
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
Cited alongside, same era.
PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T. Unke and Markus Meuwly · 2019
Cited alongside, same era.
Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, and Ben Blum-Smith · 2021
Later among the works it cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra, 2021
Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger · 2021
Later among the works it cites.
Learning from protein structure with geometric vector perceptrons, 2021
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, and Ron Dror · 2021
Later among the works it cites.
Linear atomic cluster expansion force fields for organic molecules: beyond rmse
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice EA Allen, Daniel J Cole, Christoph Ortner, and Gábor Csányi · 2021
Later among the works it cites.
Normalizing flows for probabilistic modeling and inference, 2021
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
Later among the works it cites.
Geometrically equivariant graph neural networks: A survey, 2022
Jiaqi Han, Yu Rong, Tingyang Xu, and Wenbing Huang · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2022
Later among the works it cites.
Torchmd-net: Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
Later among the works it cites.
Equivariant graph mechanics networks with constraints
Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2022
Later among the works it cites.
E(n) equivariant normalizing flows, 2022
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, and Max Welling · 2022
Later among the works it cites.
Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2041
Closest in time.