[29] Ralf Drautz. Atomic cluster expansion for accurate and transferable interatomic potentials. Physical Review B, 99(1):014104, 2019.
[30] Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, and Tie-Yan Liu. Se (3) equivariant graph neural networks with complete local frames. In International Conference on Machine Learning, pages 5583-5608. PMLR, 2022.
[31] Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror. Learning from protein structure with geometric vector perceptrons. In International Conference on Learning Representations, 2020.
[32] Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, 1(1):1-7, 2014.
[33] Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller. Machine learning of accurate energy-conserving molecular force fields. Science advances, 3(5):e1603015, 2017.
[34] Anders S Christensen and O Anatole Von Lilienfeld. On the role of gradients for machine learning of molecular energies and forces. Machine Learning: Science and Technology, 1(4): 045018, 2020.
[35] Kristof Schütt, Oliver Unke, and Michael Gastegger. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In International Conference on Machine Learning, pages 9377-9388. PMLR, 2021.
[36] Yi-Lun Liao and Tess Smidt. Equformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990, 2022.
[37] Johannes Gasteiger, Florian Becker, and Stephan Gunnemann. GemNet: Universal directional graph neural networks for molecules. Advances in Neural Information Processing Systems, 34: 6790-6802, 2021.
[38] Johannes Gasteiger, Janek Groß, and Stephan Gunnemann. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123, 2020.
[39] Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. Se (3)-transformers: 3d roto-translation equivariant attention networks. Advances in Neural Information Processing Systems, 33:1970–1981, 2020.
[40] Brandon Anderson, Truong Son Hy, and Risi Kondor. Cormorant: Covariant molecular neural networks. Advances in neural information processing systems, 32, 2019.
[41] Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson. Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data. In International Conference on Machine Learning, pages 3165-3176. PMLR, 2020.
[42] Tuan Le, Frank Noé, and Djork-Arné Clevert. Equivariant graph attention networks for molecular property prediction. arXiv preprint arXiv:2202.09891, 2022.
[43] Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky. Learning local equivariant representations for large-scale atomistic dynamics. Nature Communications, 14(1):579, 2023.
[44] David Péter Kovács, Cas van der Oord, Jiri Kucera, Alice EA Allen, Daniel J Cole, Christoph Ortner, and Gábor Csányi. Linear atomic cluster expansion force fields for organic molecules: beyond rmse. Journal of chemical theory and computation, 17(12):7696-7711, 2021.
[45] Felix A Faber, Anders S Christensen, Bing Huang, and O Anatole Von Lilienfeld. Alchemical and structural distribution based representation for universal quantum machine learning. The Journal of chemical physics, 148(24):241717, 2018.