[23] Roberto Rivelino, Puspitapallab Chaudhuri, and Sylvio Canuto. Quantifying multiple-body interaction terms in H-bonded HCN chains with many-body perturbation/coupled-cluster theories. J. Chem. Phys., 118(23):10593-10601, 2003.
[24] Patricia RP Barreto, Alessandra F Albernaz, Federico Palazzetti, Andrea Lombardi, Gaia Grossi, and Vincenzo Aquilanti. Hyperspherical representation of potential energy surfaces: intermolecular interactions in tetra-atomic and penta-atomic systems. Phys. Scr., 84(2):028111, 2011.
[25] Jiang Wang, Nicholas Charron, Brooke Husic, Simon Olsson, Frank Noé, and Cecilia Clementi. Multi-body effects in a coarse-grained protein force field. J. Chem. Phys., 154(16):164113, 2021.
[26] 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. J. Chem. Theory Comput., 17(12):7696-7711, 2021.
[27] AW Solbrig Jr. Valence force potentials for calculating crystal vibrations in silicon. J. Phys. Chem. Solids, 32(8):1761-1768, 1971.
[28] SL Altmann, A Lapiccirella, KW Lodge, and N Tomassini. A valence force field for the silicon crystal. J. Phys. C: Solid State Phys., 15(27):5581, 1982.
[29] Clifford E Dykstra. Electrostatic interaction potentials in molecular force fields. Chem. Rev., 93(7):2339-2353, 1993.
[30] Jörg Behler and Michele Parrinello. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett., 98(14):146401, 2007.
[31] Risi Kondor, Zhen Lin, and Shubhendu Trivedi. Clebsch-Gordan Nets: a fully fourier space spherical convolutional neural network. Advances in Neural Information Processing Systems, 31, 2018.
[32] 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. Preprint at http://arxiv.org/abs/1802.08219, 2018.
[33] Brandon Anderson, Truong Son Hy, and Risi Kondor. Cormorant: Covariant molecular neural networks. Advances in neural information processing systems, 32, 2019.
[34] 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.
[35] Yi-Lun Liao and Tess Smidt. Equifomer: Equivariant graph attention transformer for 3D atomistic graphs. In The Eleventh International Conference on Learning Representations, 2023.
[36] 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. Nat. Commun., 14(1):579, 2023.
[37] 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.
[38] Ralf Drautz. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B, 99(1):014104, 2019.
[39] Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In International conference on machine learning, pages 1263-1272. PMLR, 2017.
[40] Junmei Wang, Romain M Wolf, James W Caldwell, Peter A Kollman, and David A Case. Development and testing of a general amber force field. J. Comput. Chem., 25(9):1157-1174, 2004.