Reading List

This page provides references to key paper, especially those for NNPs and datasets implemented within modelforge.

Review Papers

• Kocer E, Ko TW, Behler J. Neural network potentials: A concise overview of methods. Annual review of physical chemistry. 2022 Apr 20;73(1):163-86.
  • https://doi.org/10.1146/annurev-physchem-082720-034254
• Kulik HJ, Hammerschmidt T, Schmidt J, Botti S, Marques MA, Boley M, Scheffler M, Todorović M, Rinke P, Oses C, Smolyanyuk A. Roadmap on machine learning in electronic structure. Electronic Structure. 2022 Aug 19;4(2):023004.
  • http://dx.doi.org/10.1088/2516-1075/ac572f
• Käser S, Vazquez-Salazar LI, Meuwly M, Töpfer K. Neural network potentials for chemistry: concepts, applications and prospects. Digital Discovery. 2023;2(1):28-58.
  • https://doi.org/10.1039/D2DD00102K
• Gastegger M, Marquetand P. Molecular dynamics with neural network potentials. Springer International Publishing; 2020.
  • https://doi.org/10.1007/978-3-030-40245-7_12
  • https://arxiv.org/abs/1812.07676
• Behler J. Four generations of high-dimensional neural network potentials. Chemical Reviews. 2021 Mar 29;121(16):10037-72.
  • https://doi.org/10.1021/acs.chemrev.0c00868

NNP Implementations

• Devereux C, Smith JS, Huddleston KK, Barros K, Zubatyuk R, Isayev O, Roitberg AE. Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens. Journal of Chemical Theory and Computation. 2020 Jun 16;16(7):4192-202.
  • https://doi.org/10.1021/acs.jctc.0c00121
• Anstine D, Zubatyuk R, Isayev O. AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.
  • https://chemrxiv.org/engage/chemrxiv/article-details/662fbc9491aefa6ce1bebb89
• Schütt KT, Kessel P, Gastegger M, Nicoli KA, Tkatchenko A, Müller KR. SchNetPack: A deep learning toolbox for atomistic systems. Journal of chemical theory and computation. 2018 Nov 27;15(1):448-55.
  • https://doi.org/10.1021/acs.jctc.8b00908
• Schütt KT, Hessmann SS, Gebauer NW, Lederer J, Gastegger M. SchNetPack 2.0: A neural network toolbox for atomistic machine learning. The Journal of Chemical Physics. 2023 Apr 14;158(14).
  • https://doi.org/10.1063/5.0138367
  • https://arxiv.org/abs/2212.05517
• Simeon G, De Fabritiis G. Tensornet: Cartesian tensor representations for efficient learning of molecular potentials. Advances in Neural Information Processing Systems. 2024 Feb 13;36.
  • https://proceedings.neurips.cc/paper_files/paper/2023/hash/75c2ec5f98d7b2f50ad68033d2c07086-Abstract-Conference.html
  • https://arxiv.org/abs/2306.06482
• Unke OT, Meuwly M. PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges. Journal of chemical theory and computation. 2019 May 1;15(6):3678-93.
  • https://doi.org/10.1021/acs.jctc.9b00181
  • https://arxiv.org/abs/1902.08408
• Schütt K, Unke O, Gastegger M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 9377-9388). PMLR.
  • https://proceedings.mlr.press/v139/schutt21a.html?ref=https://githubhelp.com
  • https://arxiv.org/abs/2102.03150
• Ko TW, Finkler JA, Goedecker S, Behler J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nature communications. 2021 Jan 15;12(1):398.
  • https://doi.org/10.1038/s41467-020-20427-2
• Tu NT, Rezajooei N, Johnson ER, Rowley CN. A neural network potential with rigorous treatment of long-range dispersion. Digital Discovery. 2023;2(3):718-27.
  • https://doi.org/10.1039/D2DD00150K
• Batatia I, Kovacs DP, Simm G, Ortner C, Csányi G. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems. 2022 Dec 6;35:11423-36.
  • https://proceedings.neurips.cc/paper_files/paper/2022/file/4a36c3c51af11ed9f34615b81edb5bbc-Paper-Conference.pdf
• Thölke P, De Fabritiis G. Torchmd-net: equivariant transformers for neural network based molecular potentials. arXiv preprint arXiv:2202.02541. 2022 Feb 5.
  • https://arxiv.org/abs/2202.02541
• Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel CJ, Ceder G. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence. 2023 Sep;5(9):1031-41.
  • https://www.nature.com/articles/s42256-023-00716-3
• Wang T, He X, Li M, Wang Y, Wang Z, Li S, Shao B, Liu TY. AI2BMD: efficient characterization of protein dynamics with ab initio accuracy. bioRxiv. 2023:2023-07.
  • https://www.biorxiv.org/content/10.1101/2023.07.12.548519v1
• Musaelian A, Batzner S, Johansson A, Sun L, Owen CJ, Kornbluth M, Kozinsky B. Learning local equivariant representations for large-scale atomistic dynamics. Nature Communications. 2023 Feb 3;14(1):579.
  • https://doi.org/10.1038/s41467-023-36329-y
• Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, Molinari N, Smidt TE, Kozinsky B. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature communications. 2022 May 4;13(1):2453.
  • https://doi.org/10.1038/s41467-022-29939-5

Benchmarking and Evaluation

• Dwivedi VP, Joshi CK, Luu AT, Laurent T, Bengio Y, Bresson X. Benchmarking graph neural networks. Journal of Machine Learning Research. 2023;24(43):1-48.
  • https://www.jmlr.org/papers/volume24/22-0567/22-0567.pdf
  • https://arxiv.org/abs/2003.00982
• Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, Spitzer E, Raji ID, Gebru T. Model cards for model reporting. InProceedings of the conference on fairness, accountability, and transparency 2019 Jan 29 (pp. 220-229).
  • https://dl.acm.org/doi/pdf/10.1145/3287560.3287596
  • https://arxiv.org/abs/1810.03993
• Fu X, Wu Z, Wang W, Xie T, Keten S, Gomez-Bombarelli R, Jaakkola T. Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations. arXiv preprint arXiv:2210.07237. 2022 Oct 13.
  • https://arxiv.org/abs/2210.07237