Bibliography#
Every work cited anywhere in the course, in one place. Entries are generated from
book/references.bib.
For a curated, grouped reading list with commentary on why each paper matters and where it is used in the course, see Further reading instead.
Mario Geiger and Tess Smidt. e3nn: Euclidean Neural Networks. arXiv preprint arXiv:2207.09453, 2022. URL: https://arxiv.org/abs/2207.09453.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds. arXiv preprint arXiv:1802.08219, 2018. URL: https://arxiv.org/abs/1802.08219.
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen. 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. In Advances in Neural Information Processing Systems. 2018.
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, and others. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018. URL: https://arxiv.org/abs/1806.01261.
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling. SE(3)-Transformers: 3D roto-translation equivariant attention networks. In Advances in Neural Information Processing Systems. 2020.
Brandon Anderson, Truong Son Hy, and Risi Kondor. Cormorant: Covariant molecular neural networks. In Advances in Neural Information Processing Systems. 2019.
Yi-Lun Liao and Tess Smidt. Equiformer: Equivariant graph attention transformer for 3D atomistic graphs. In International Conference on Learning Representations. 2023. URL: https://arxiv.org/abs/2206.11990.
Andrea Grisafi, David M. Wilkins, Gábor Csányi, and Michele Ceriotti. Symmetry-adapted machine learning for tensorial properties of atomistic systems. Physical Review Letters, 120:036002, 2018. doi:10.1103/PhysRevLett.120.036002.
Jörg Behler and Michele Parrinello. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical Review Letters, 98:146401, 2007. doi:10.1103/PhysRevLett.98.146401.
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. arXiv preprint arXiv:1706.08566, 2017. URL: https://arxiv.org/abs/1706.08566.
Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, and Klaus-Robert Müller. SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148:241722, 2018. doi:10.1063/1.5019779.
Johannes Gasteiger, Janek Groß, and Stephan Günnemann. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123, 2020. URL: https://arxiv.org/abs/2003.03123.
Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115, 2022. URL: https://arxiv.org/abs/2011.14115.
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13:2453, 2022. doi:10.1038/s41467-022-29939-5.
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, 14:579, 2023. doi:10.1038/s41467-023-36329-y.
Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. In Advances in Neural Information Processing Systems. 2022.
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, and others. The design space of E(3)-equivariant atom-centred interatomic potentials. arXiv preprint arXiv:2205.06643, 2022. URL: https://arxiv.org/abs/2205.06643.
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, and others. The design space of E(3)-equivariant atom-centred interatomic potentials. Nature Machine Intelligence, 7:56, 2025. doi:10.1038/s42256-024-00956-x.
Dávid Péter Kovács, Ilyes Batatia, Eszter S. Arany, and Gábor Csányi. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science. The Journal of Chemical Physics, 159:044118, 2023. doi:10.1063/5.0155322.
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning, and others. MACE-OFF: Transferable short range machine learning force fields for organic molecules. Journal of the American Chemical Society, 147:17598, 2025. doi:10.1021/jacs.4c07099.
Ilyes Batatia, Philipp Benner, Yuan Chiang, and others. A foundation model for atomistic materials chemistry. arXiv preprint arXiv:2401.00096, 2024. URL: https://arxiv.org/abs/2401.00096.
Ralf Drautz. Atomic cluster expansion for accurate and transferable interatomic potentials. Physical Review B, 99:014104, 2019. doi:10.1103/PhysRevB.99.014104.
Geneviève Dusson, Markus Bachmayr, Gábor Csányi, Ralf Drautz, Simon Etter, Cas van der Oord, and Christoph Ortner. Atomic cluster expansion: Completeness, efficiency and stability. Journal of Computational Physics, 454:110946, 2022. doi:10.1016/j.jcp.2022.110946.
Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, and Michele Ceriotti. Unified theory of atom-centered representations and message-passing machine-learning schemes. The Journal of Chemical Physics, 156:204115, 2022. doi:10.1063/5.0087042.
Sergey N. Pozdnyakov and Michele Ceriotti. Incompleteness of graph neural networks for points clouds in three dimensions. arXiv preprint arXiv:2201.07136, 2022. URL: https://arxiv.org/abs/2201.07136.
Sanggyu Chong and others. Resolving the body-order paradox of machine-learned interatomic potentials. The Journal of Chemical Physics, 164:064121, 2026. doi:10.1063/5.0303302.
Andrea Grisafi and Michele Ceriotti. Incorporating long-range physics in atomic-scale machine learning. The Journal of Chemical Physics, 151:204105, 2019. doi:10.1063/1.5128375.
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, and Stephan Günnemann. Ewald-based long-range message passing for molecular graphs. In Proceedings of the 40th International Conference on Machine Learning, volume 202, 17544. 2023. URL: https://proceedings.mlr.press/v202/kosmala23a.html.
Bingqing Cheng. Latent Ewald summation for machine learning of long-range interactions. npj Computational Materials, 11:80, 2025. doi:10.1038/s41524-025-01577-7.
Jiří Kolafa and John W. Perram. Cutoff errors in the Ewald summation formulae for point charge systems. Molecular Simulation, 9:351, 1992. doi:10.1080/08927029208049126.
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:e1603015, 2017. doi:10.1126/sciadv.1603015.
Anders S. Christensen and O. Anatole von Lilienfeld. On the role of gradients for machine learning of molecular energies and forces. arXiv preprint arXiv:2007.09593, 2020. URL: https://arxiv.org/abs/2007.09593.