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Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
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The art and practice of structure-based drug design: A molecular modeling perspective
Bohacek, R. S., McMartin, C., and Guida, W. C · 1996
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2003
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
Blum, L. C. and Reymond, J.-L · 2009
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J., Giri, S., Margraf, J. T., and Günnemann, S · 2011
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
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Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Agrawal, A. and Choudhary, A · 2016
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Machine-learning prediction of the d-band center for metals and bimetals
Takigawa, I., Shimizu, K.-i., Tsuda, K., and Takakusagi, S · 2016
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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
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Including crystal structure attributes in machine learning models of formation energies via voronoi tessellations
Ward, L., Liu, R., Krishna, A., Hegde, V. I., Agrawal, A., Choudhary, A., and Wolverton, C · 2017
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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
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S., and Kondor, R · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
Unke, O. T. and Meuwly, M · 2019
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., and Sun, X · 2020
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SE(3)-transformers: 3D roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V., and Welling, M · 2020
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Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E · 2020
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Mgcn: Descriptor learning using multiscale gcns
Wang, Y., Ren, J., Yan, D.-M., Guo, J., Zhang, X., and Wonka, P · 2020
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An introduction to electrocatalyst design using machine learning for renewable energy storage
Zitnick, C. L., Chanussot, L., Das, A., Goyal, S., Heras-Domingo, J., Ho, C., Hu, W., Lavril, T., Palizhati, A., Riviere, M., et al · 2020
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Learning 3d representations of molecular chirality with invariance to bond rotations
Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects
Unke, O. T., Chmiela, S., Gastegger, M., Schütt, K. T., Sauceda, H. E., and Müller, K.-R · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Batatia, I., Kovács, D. P., Simm, G. N., Ortner, C., and Csányi, G · 2022
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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
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A universal graph deep learning interatomic potential for the periodic table
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Adams, K., Pattanaik, L., and Coley, C. W · 2021
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Geometric deep learning on molecular representations
Atz, K., Grisoni, F., and Schneider, G · 2021
Cited alongside, same era.
Geometric and physical quantities improve E(3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E., and Welling, M · 2021
Cited alongside, same era.
Graphnorm: A principled approach to accelerating graph neural network training
Cai, T., Luo, S., Xu, K., He, D., Liu, T.-y., and Wang, L · 2021
Cited alongside, same era.
Open catalyst 2020 (oc20) dataset and community challenges
Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., et al · 2021
Cited alongside, same era.
Equivariant point network for 3D point cloud analysis
Chen, H., Liu, S., Chen, W., Li, H., and Hill, R · 2021
Cited alongside, same era.
Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F., and Günnemann, S · 2021
Cited alongside, same era.
Simple gnn regularisation for 3d molecular property prediction & beyond
Godwin, J., Schaarschmidt, M., Gaunt, A., Sanchez-Gonzalez, A., Rubanova, Y., Veličković, P., Kirkpatrick, J., and Battaglia, P · 2021
Cited alongside, same era.
Chen, C. and Ong, S. P · 2022
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PhAST: Physics-aware, scalable, and task-specific GNNs for accelerated catalyst design
Duval, A., Schmidt, V., Miret, S., Bengio, Y., Hernández-García, A., and Rolnick, D · 2022
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So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
Frank, T., Unke, O. T., and Muller, K. R · 2022
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Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets
Gasteiger, J., Shuaibi, M., Sriram, A., Günnemann, S., Ulissi, Z., Zitnick, C. L., and Das, A · 2022
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Equivariance versus augmentation for spherical images
Gerken, J., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., and Persson, D · 2022
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Geometrically equivariant graph neural networks: A survey
Han, J., Rong, Y., Xu, T., and Huang, W · 2022
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On the expressive power of geometric graph neural networks
Joshi, C. K., Bodnar, C., Mathis, S. V., Cohen, T., and Liò, P · 2022
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Kolluru, A., Shuaibi, M., Palizhati, A., Shoghi, N., Das, A., Wood, B., Zitnick, C. L., Kitchin, J. R., and Ulissi, Z. W · 2022
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The open MatSci ML toolkit: A flexible framework for machine learning in materials science
Miret, S., Lee, K. L. K., Gonzales, C., Nassar, M., and Spellings, M · 2022
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Incompleteness of graph convolutional neural networks for points clouds in three dimensions
Pozdnyakov, S. N. and Ceriotti, M · 2022
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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
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Torchmd-net: Equivariant transformers for neural network based molecular potentials
Thölke, P. and De Fabritiis, G · 2022
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ComENet: Towards complete and efficient message passing for 3D molecular graphs
Wang, L., Liu, Y., Lin, Y., Liu, H., and Ji, S · 2022
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Graph neural network with local frame for molecular potential energy surface
Wang, X. and Zhang, M · 2022
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Universal approximations of invariant maps by neural networks
Yarotsky, D · 2022
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