Fetching the paper…
Reading the bibliography…
Graph neural networks that model 3D data, such as point clouds or atoms, are typically desired to be $SO(3)$ equivariant, i.e., equivariant to 3D rotations.
Introduction to the theory of fourier’s series
Bocher, M · 1906
Earlier work this paper cites.
An undulatory theory of the mechanics of atoms and molecules
Schrödinger, E · 1926
Earlier work this paper cites.
Inhomogeneous electron gas
Hohenberg, P. and Kohn, W · 1964
Earlier work this paper cites.
Self-consistent equations including exchange and correlation effects
Kohn, W. and Sham, L. J · 1965
Earlier work this paper cites.
Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
Halgren, T · 1996
Earlier work this paper cites.
Representations and invariants of the classical groups
Goodman, R. and Wallach, N. R · 2000
Earlier work this paper cites.
Clebsch-gordan coefficient
Weisstein, E. W · 2003
Earlier work this paper cites.
A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
Earlier work this paper cites.
The alchemy of air: a Jewish genius, a doomed tycoon, and the scientific discovery that fed the world but fueled the rise of Hitler
Hager, T · 2009
Earlier work this paper cites.
Spherical tensor calculus for local adaptive filtering
Reisert, M. and Burkhardt, H · 2009
Earlier work this paper cites.
Measurement of areas on a sphere using fibonacci and latitude–longitude lattices
González, Á · 2010
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Perspective: Machine learning potentials for atomistic simulations
Behler, J · 2016
Earlier work this paper cites.
Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
Earlier work this paper cites.
Steerable CNNs
Cohen, T. S. and Welling, M · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
Cited alongside, same era.
Towards exact molecular dynamics simulations with machine-learned force fields
Chmiela, S., Sauceda, H. E., Müller, K.-R., and Tkatchenko, A · 2018
Cited alongside, same era.
Spherical CNNs
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
Cited alongside, same era.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K · 2018
Cited alongside, same era.
Introduction to quantum mechanics
Griffiths, D. J. and Schroeter, D. F · 2018
Cited alongside, same era.
Clebsch–gordan nets: a fully fourier space spherical convolutional neural network
Kondor, R., Lin, Z., and Trivedi, S · 2018
Cited alongside, same era.
A functional approach to rotation equivariant non-linearities for tensor field networks
Poulenard, A. and Guibas, L. J · 2021
Later among the works it cites.
E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Later among the works it cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O., and Gastegger, M · 2021
Later among the works it cites.
Rotation invariant graph neural networks using spin convolutions
Shuaibi, M., Kolluru, A., Das, A., Grover, A., Sriram, A., Ulissi, Z., and Zitnick, C. L · 2021
Later among the works it cites.
Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Batatia, I., Kovacs, D. P., Simm, G., Ortner, C., and Csányi, G · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R · 2018
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Cited alongside, same era.
3d steerable cnns: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W., and Cohen, T. S · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C · 2018
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V., and Welling, M · 2020
Cited alongside, same era.
Optimization of fast algorithms for global quadrature by expansion using target-specific expansions
Wala, M. and Klöckner, A · 2020
Cited alongside, same era.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
Later among the works it cites.
Geometric and physical quantities improve e(3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E. J., and Welling, M · 2022
Later among the works it cites.
Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets
Gasteiger, J., Shuaibi, M., Sriram, A., Günnemann, S., Ulissi, Z. W., Zitnick, C. L., and Das, A · 2022
Later among the works it cites.
e3nn: Euclidean neural networks
Geiger, M. and Smidt, T · 2022
Later among the works it cites.
Adsorbml: Accelerating adsorption energy calculations with machine learning
Lan, J., Palizhati, A., Shuaibi, M., Wood, B. M., Wander, B., Das, A., Uyttendaele, M., Zitnick, C. L., and Ulissi, Z. W · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs
Liu, Y., Wang, L., Liu, M., Lin, Y., Zhang, X., Oztekin, B., and Ji, S · 2022
Later among the works it cites.
Frame averaging for invariant and equivariant network design
Puny, O., Atzmon, M., Smith, E. J., Misra, I., Grover, A., Ben-Hamu, H., and Lipman, Y · 2022
Later among the works it cites.
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al · 2022
Later among the works it cites.
Spherical channels for modeling atomic interactions
Zitnick, C. L., Das, A., Kolluru, A., Lan, J., Shuaibi, M., Sriram, A., Ulissi, Z. W., and Wood, B. M · 2022
Later among the works it cites.
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Liao, Y.-L. and Smidt, T · 2023
Closest in time.
Learning local equivariant representations for large-scale atomistic dynamics
Musaelian, A., Batzner, S., Johansson, A., Sun, L., Owen, C. J., Kornbluth, M., and Kozinsky, B · 2023
Closest in time.
The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts
Tran, R., Lan, J., Shuaibi, M., Wood, B. M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., et al · 2023
Closest in time.