作者:Bowman, Joel M.
Bartlett, R. J. & Musiał, M. Coupled-cluster theory in quantum chemistry. Rev. Mod. Phys. 79, 291–352 (2007).
Gkeka, P. et al. Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems. J. Chem. Theory Comput. 16, 4757–4775 (2020).
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mat. 31, 1902765 (2019).
Manzhos, S., Dawes, R. & Carrington, T. Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces. Int. J. Quantum Chem. 115, 1012–1020 (2014).
Manzhos, S. & Carrington Jr, T. Neural network potential energy surfaces for small molecules and reactions. Chem. Rev. 121, 10187–10217 (2020).
Meuwly, M. Machine learning for chemical reactions. Chem. Rev. 121, 10218–10239 (2021).
Braams, B. J. & Bowman, J. M. Permutationally invariant potential energy surfaces in high dimensionality. Int. Rev. Phys. Chem. 28, 577–606 (2009).
Qu, C., Yu, Q. & Bowman, J. M. Permutationally invariant potential energy surfaces. Annu. Rev. Phys. Chem. 69, 151–175 (2018).
Jiang, B. & Guo, H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. J. Chem. Phys. 139, 054112 (2013).
Jiang, B., Li, J. & Guo, H. Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial–neural network approach. Int. Rev. Phys. Chem. 35, 479–506 (2016).
Shao, K., Chen, J., Zhao, Z. & Zhang, D. H. Fitting potential energy surfaces with fundamental invariant neural network. J. Chem. Phys. 145, 071101 (2016).
Fu, B. & Zhang, D. H. Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces. Natl Sci. Rev. 10, nwad321 (2023).
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Behler, J. Four generations of high-dimensional neural network potentials. Chem. Rev. 121, 10037–10072 (2021).
Chmiela, S., Sauceda, H. E., Müller, K.-R. & Tkatchenko, A. Towards exact molecular dynamics simulations with machine-learned force fields. Nat. Commun. 9, 3887 (2018).
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).
Uteva, E., Graham, R. S., Wilkinson, R. D. & Wheatley, R. J. Interpolation of intermolecular potentials using Gaussian processes. J. Chem. Phys. 147, 161706 (2017).
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet—a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).
Unke, O. T. & Meuwly, M. PhysNet: a neural network for predicting energies, forces, dipole moments, and partial charges. J. Chem. Theory Comput. 15, 3678–3693 (2019).
Zhang, L., Han, J., Wang, H., Car, R. & Weinan, E. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).
Zhang, Y., Hu, C. & Jiang, B. Embedded atom neural network potentials: efficient and accurate machine learning with a physically inspired representation. J. Phys. Chem. Lett. 10, 4962–4967 (2019).
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Batatia, I., Kovacs, D. P., Simm, G., Ortner, C. & Csanyi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neural Inf. Process. Syst. 35, 11423–11436 (2022).
Musaelian, A. et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat. Commun. 14, 579 (2023).
Heidar-Zadeh, F. et al. Information-theoretic approaches to atoms-in-molecules: Hirshfeld family of partitioning schemes. J. Phys. Chem. A. 122, 4219–4245 (2017).
Uhlig, F., Tovey, S. & Holm, C. Emergence of accurate atomic energies from machine learned noble gas potentials. Preprint at https://arxiv.org/abs/2403.00377 (2024).
Konovalov, A., Symons, B. C. & Popelier, P. L. On the many-body nature of intramolecular forces in FFLUX and its implications. J. Comput. Chem. 42, 107–116 (2021).
Symons, B. C. & Popelier, P. L. Application of quantum chemical topology force field FFLUX to condensed matter simulations: liquid water. J. Chem. Theory Comput. 18, 5577–5588 (2022).
Manchev, Y. T. & Popelier, P. L. Modeling many-body interactions in water with Gaussian process regression. J. Phys. Chem. A. 128, 9345–9351 (2024).
Bader, R. F. W. Atoms in molecules. Acc. Chem. Res. 18, 9–15 (1985).
Popelier, P. L. A. The Chemical Bond 271–308 (John Wiley & Sons, 2014).
Gordon, M. S., Fedorov, D. G., Pruitt, S. R. & Slipchenko, L. V. Fragmentation methods: a route to accurate calculations on large systems. Chem. Rev. 112, 632–672 (2012).
Hodges, M. P., Stone, A. J. & Xantheas, S. S. Contribution of many-body terms to the energy for small water clusters: a comparison of ab initio calculations and accurate model potentials. J. Phys. Chem. A 101, 9163–9168 (1997).
Dahlke, E. E. & Truhlar, D. G. Electrostatically embedded many-body correlation energy, with applications to the calculation of accurate second-order Møller–Plesset perturbation theory energies for large water clusters. J. Chem. Theory Comput. 3, 1342–1348 (2007).
Wang, Y. M., Shepler, B. C., Braams, B. J. & Bowman, J. M. Full-dimensional, ab initio potential energy and dipole moment surfaces for water. J. Chem. Phys. 131, 054511 (2009).
Góra, U., Podeszwa, R., Cencek, W. & Szalewicz, K. Interaction energies of large clusters from many-body expansion. J. Chem. Phys. 135, 224102 (2011).
Medders, G. R., Götz, A. W., Morales, M. A., Bajaj, P. & Paesani, F. On the representation of many-body interactions in water. J. Chem. Phys. 143, 104102 (2015).
Yu, Q. & Bowman, J. M. VSCF/VCI vibrational spectroscopy of H7O3+ and H9O4+ using high-level, many-body potential energy surface and dipole moment surfaces. J. Chem. Phys. 146, 121102 (2017).
Heindel, J. P. & Xantheas, S. S. The many-body expansion for aqueous systems revisited: I. Water–water interactions. J. Chem. Theory Comput. 16, 6843–6855 (2020).
Zhu, X., Riera, M., Bull-Vulpe, E. F. & Paesani, F. MB-pol(2023): sub-chemical accuracy for water simulations from the gas to the liquid phase. J. Chem. Theory Comput. 19, 3551–3556 (2023).
Yu, Q. et al. q-AQUA: a many-body CCSD(T) water potential, including 4-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase. J. Phys. Chem. Lett. 13, 5068–5074 (2022).
Qu, C. et al. Interfacing q-AQUA with a polarizable force field: the best of both worlds. J. Chem. Theory Comput. 19, 3446–3459 (2023).
Partridge, H. & Schwenke, D. W. The determination of an accurate isotope dependent potential energy surface for water from extensive ab initio calculations and experimental data. J. Chem. Phys. 106, 4618 (1997).
Zhu, Y.-C. et al. Torsional tunneling splitting in a water trimer. J. Am. Chem. Soc. 144, 21356–21362 (2022).
Fu, B. & Zhang, D. H. Ab initio potential energy surfaces and quantum dynamics for polyatomic bimolecular reactions. J. Chem. Theory Comput. 14, 2289–2303 (2018).
Cheng, B., Engel, E. A., Behler, J., Dellago, C. & Ceriotti, M. Ab initio thermodynamics of liquid and solid water. Proc. Natl Acad. Sci. USA 116, 1110–1115 (2019).
Zhang, Y., Xia, J. & Jiang, B. REANN: a PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. J. Chem. Phys. 156, 114801 (2022).
Zhai, Y., Caruso, A., Bore, S. L., Luo, Z. & Paesani, F. A ‘short blanket’ dilemma for a state-of-the-art neural network potential for water: reproducing experimental properties or the physics of the underlying many-body interactions? J. Chem. Phys. 158, 084111 (2023).
Medders, G. R., Babin, V. & Paesani, F. Development of a ‘first-principles’ water potential with flexible monomers. III. Liquid phase properties. J. Chem. Theory Comput. 10, 2906–2910 (2014).
Kapil, V. et al. i-PI 2.0: a universal force engine for advanced molecular simulations. Comput. Phys. Commun. 236, 214–223 (2019).
Reddy, S. K. et al. On the accuracy of the MB-pol many-body potential for water: interaction energies, vibrational frequencies, and classical thermodynamic and dynamical properties from clusters to liquid water and ice. J. Chem. Phys. 145, 194504 (2016).
Houston, P. L. et al. No headache for PIPs: a PIP potential for aspirin runs much faster and with similar precision than other machine-learned potentials. J. Chem. Theory Comput. 20, 3008–3018 (2024).
Habershon, S., Markland, T. E. & Manolopoulos, D. E. Competing quantum effects in the dynamics of a flexible water model. J. Chem. Phys. 131, 024501 (2009).
Fanourgakis, G. S. & Xantheas, S. S. Development of transferable interaction potentials for water. V. Extension of the flexible, polarizable, Thole-type model potential (TTM3-F, v. 3.0) to describe the vibrational spectra of water clusters and liquid water. J. Chem. Phys. 128, 074506 (2008).
Cheng, B. Cartesian atomic cluster expansion for machine learning interatomic potentials. npj Comput. Mater. 10, 157 (2024).
Conte, R., Qu, C. & Bowman, J. M. Permutationally invariant fitting of many-body, non-covalent interactions with application to three-body methane–water–water. J. Chem. Theory Comput. 11, 1631–1638 (2015).
Mathur, R., Muniz, M. C., Yue, S., Car, R. & Panagiotopoulos, A. Z. First-principles-based machine learning models for phase behavior and transport properties of CO2. J. Phys. Chem. B 127, 4562–4569 (2023).
Houston, P. L. et al. PESPIP: software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials. J. Chem. Phys. 158, 044109 (2023).
Chen, R., Shao, K., Fu, B. & Zhang, D. H. Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms. J. Chem. Phys. 152, 204307 (2020).
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In International Conference on Machine Learning (eds. Precup, D. & Teh, Y. W.) 1263–1272 (PMLR, 2017).
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 8, 13890 (2017).
Qu, C. & Bowman, J. M. Communication: a fragmented, permutationally invariant polynomial approach for potential energy surfaces of large molecules: application to N-methyl acetamide. J. Chem. Phys. 150, 141101 (2019).
Moré, J. J. The Levenberg–Marquardt algorithm: implementation and theory. In Numerical Analysis: Proc. Biennial Conference held at Dundee, June 28–July 1, 1977 (ed. Watson, G. A.) 105–116 (Springer Berlin Heidelberg, 2006).
Anderson, J. B. A random-walk simulation of the Schrödinger equation: \({{{\mathrm{H}}}_{{3}}^{+}}\). J. Chem. Phys. 63, 1499–1503 (1975).
Yu, Q. et al. Data files for developing and testing MB-PIPNet models on water trimer, methane-water cluster, and liquid water. figshare https://doi.org/10.6084/m9.figshare.28510238.v1 (2025).
Yu, Q. Source code and example of MB-PIPNet approach. Zenodo https://doi.org/10.5281/zenodo.14954863 (2025).
Wang, Y. & Bowman, J. M. Rigorous calculation of dissociation energies (D) of the water trimer, (H2O)3 and (D2O)3. J. Chem. Phys. 135, 131101 (2011).
Zhang, Y., Hu, C. & Jiang, B. Accelerating atomistic simulations with piecewise machine-learned ab initio potentials at a classical force field-like cost. Phys. Chem. Chem. Phys. 23, 1815–1821 (2021).
Kovács, D. P., Batatia, I., Arany, E. S. & Csányi, G. Evaluation of the MACE force field architecture: from medicinal chemistry to materials science. J. Chem. Phys. 159, 044118 (2023).
Mills, R. Self-diffusion in normal and heavy water in the range 1–45°. J. Phys. Chem. 77, 685–688 (1973).
Holz, M., Heil, S. R. & Sacco, A. Temperature-dependent self-diffusion coefficients of water and six selected molecular liquids for calibration in accurate 1H NMR PFG measurements. Phys. Chem. Chem. Phys. 2, 4740–4742 (2000).
Skinner, L. B. et al. Benchmark oxygen–oxygen pair-distribution function of ambient water from X-ray diffraction measurements with a wide Q-range. J. Chem. Phys. 138, 074506 (2013).
Skinner, L. B., Benmore, C. J., Neuefeind, J. C. & Parise, J. B. The structure of water around the compressibility minimum. J. Chem. Phys. 141, 214507 (2014).
Soper, A. & Benmore, C. Quantum differences between heavy and light water. Phys. Rev. Lett. 101, 065502 (2008).