[1] Daan Frenkel and Berend Smit. Understanding Molecular Simulation: From Algorithms to Applications, volume 1. Elsevier, 2001.
[2] J. E. Lennard-Jones. Cohesion. Proceedings of the Physical Society, 43(5):461, 1931.
[3] Roberto Car and Michele Parrinello. Unified approach for molecular dynamics and density-functional theory. Physical Review Letters, 55(22):2471, 1985.
[4] Dominik Marx and Jurg Hutter. Ab initio molecular dynamics: Theory and implementation. Modern Methods and Algorithms of Quantum Chemistry, 1(301-449):141, 2000.
[5] Walter Kohn and Lu Jeu Sham. Self-consistent equations including exchange and correlation effects. Physical review, 140(4A):A1133, 1965.
[6] Jörg Behler and Michele Parrinello. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical Review Letters, 98(14):146401, 2007.
[7] Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, and Klaus-Robert Müller. Machine Learning Force Fields. Chemical Reviews, 121(16):10142-10186, 2021.
[8] Jörg Behler and Gábor Csányi. Machine learning potentials for extended systems: a perspective. The European Physical Journal B, 94:1-11, 2021.
[9] Albert P. Bartók, Risi Kondor, and Gábor Csányi. On representing chemical environments. Physical Review B, 87(18):184115, 2013.
[10] Michael J Willatt, Félix Musil, and Michele Ceriotti. Atom-density representations for machine learning. The Journal of Chemical Physics, 150(15):154110, 2019.
[11] Felix Musil, Andrea Grisafi, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti. Physics-inspired structural representations for molecules and materials. Chemical Reviews, 121(16):9759-9815, 2021.
[12] 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.
[13] 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.
[14] Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood. Spherical channels for modeling atomic interactions. Advances in Neural Information Processing Systems, 35:8054-8067, 2022.
[15] 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(1):2453, 2022.
[16] Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E(n) Equivariant Graph Neural Networks, 2022.
[17] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 4171-4186. Association for Computational Linguistics, 2019.