Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah. From stars to subgraphs: Uplifting any gnn with local structure awareness. arXiv preprint arXiv:2110.03753, 2021.
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein. Improving graph neural network expressivity via subgraph isomorphism counting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):657-668, 2022.
Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, and Karsten Borgwardt. Topological graph neural networks. arXiv preprint arXiv:2102.07835, 2021.
Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lio, and Michael Bronstein. Weisfeiler and lehman go topological: Message passing simplicial networks. In International Conference on Machine Learning, pages 1026-1037. PMLR, 2021.
Bohang Zhang, Shengjie Luo, Liwei Wang, and Di He. Rethinking the expressive power of gnns via graph biconnectivity. arXiv preprint arXiv:2301.09505, 2023.
Valentino Delle Rose, Alexander Kozachinskiy, Cristóbal Rojas, Mircea Petrache, and Pablo Barceló. Three iterations of  $(d - 1)$ -wl test distinguish non isometric clouds of  $d$ -dimensional points. arXiv e-prints, pages arXiv-2303, 2023.
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.
Anders S Christensen, Lars A Bratholm, Felix A Faber, and O Anatole von Lilienfeld. Fchl revisited: Faster and more accurate quantum machine learning. The Journal of chemical physics, 152(4): 044107, 2020.
Xiyuan Wang and Muhan Zhang. Graph neural network with local frame for molecular potential energy surface. arXiv preprint arXiv:2208.00716, 2022.
Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems, 35:11423-11436, 2022.
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.
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.
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. Molecularnet: a benchmark for molecular machine learning. Chemical science, 9(2):513-530, 2018.
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902, 2018.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
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.
Johannes Klicpera, Shankari Giri, Johannes T Margraf, and Stephan Gunnemann. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115, 2020b.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.