英语轻松读发新版了,欢迎下载、更新

Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials

2025-04-14 09:53:20 英文原文

作者:Bowman, Joel M.

References

  1. Bartlett, R. J. & Musiał, M. Coupled-cluster theory in quantum chemistry. Rev. Mod. Phys. 79, 291–352 (2007).

    Article  Google Scholar 

  2. 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).

    Article  Google Scholar 

  3. Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mat. 31, 1902765 (2019).

    Article  Google Scholar 

  4. 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).

    Article  Google Scholar 

  5. Manzhos, S. & Carrington Jr, T. Neural network potential energy surfaces for small molecules and reactions. Chem. Rev. 121, 10187–10217 (2020).

    Article  Google Scholar 

  6. Meuwly, M. Machine learning for chemical reactions. Chem. Rev. 121, 10218–10239 (2021).

    Article  Google Scholar 

  7. Braams, B. J. & Bowman, J. M. Permutationally invariant potential energy surfaces in high dimensionality. Int. Rev. Phys. Chem. 28, 577–606 (2009).

    Article  Google Scholar 

  8. Qu, C., Yu, Q. & Bowman, J. M. Permutationally invariant potential energy surfaces. Annu. Rev. Phys. Chem. 69, 151–175 (2018).

    Article  Google Scholar 

  9. Jiang, B. & Guo, H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. J. Chem. Phys. 139, 054112 (2013).

    Article  Google Scholar 

  10. 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).

    Article  Google Scholar 

  11. Shao, K., Chen, J., Zhao, Z. & Zhang, D. H. Fitting potential energy surfaces with fundamental invariant neural network. J. Chem. Phys. 145, 071101 (2016).

    Article  Google Scholar 

  12. Fu, B. & Zhang, D. H. Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces. Natl Sci. Rev. 10, nwad321 (2023).

    Article  Google Scholar 

  13. Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).

    Article  Google Scholar 

  14. Behler, J. Four generations of high-dimensional neural network potentials. Chem. Rev. 121, 10037–10072 (2021).

    Article  Google Scholar 

  15. 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).

    Article  Google Scholar 

  16. 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).

    Article  Google Scholar 

  17. Uteva, E., Graham, R. S., Wilkinson, R. D. & Wheatley, R. J. Interpolation of intermolecular potentials using Gaussian processes. J. Chem. Phys. 147, 161706 (2017).

    Article  Google Scholar 

  18. 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).

    Article  Google Scholar 

  19. 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).

    Article  Google Scholar 

  20. 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).

    Article  Google Scholar 

  21. 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).

    Article  Google Scholar 

  22. Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).

    Article  Google Scholar 

  23. 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).

    Google Scholar 

  24. Musaelian, A. et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat. Commun. 14, 579 (2023).

    Article  Google Scholar 

  25. 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).

    Article  Google Scholar 

  26. 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).

  27. 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).

    Article  Google Scholar 

  28. 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).

    Article  Google Scholar 

  29. Manchev, Y. T. & Popelier, P. L. Modeling many-body interactions in water with Gaussian process regression. J. Phys. Chem. A. 128, 9345–9351 (2024).

    Article  Google Scholar 

  30. Bader, R. F. W. Atoms in molecules. Acc. Chem. Res. 18, 9–15 (1985).

    Article  Google Scholar 

  31. Popelier, P. L. A. The Chemical Bond 271–308 (John Wiley & Sons, 2014).

  32. 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).

    Article  Google Scholar 

  33. 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).

    Article  Google Scholar 

  34. 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).

    Article  Google Scholar 

  35. 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).

    Article  Google Scholar 

  36. Góra, U., Podeszwa, R., Cencek, W. & Szalewicz, K. Interaction energies of large clusters from many-body expansion. J. Chem. Phys. 135, 224102 (2011).

    Article  Google Scholar 

  37. 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).

    Article  Google Scholar 

  38. 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).

    Article  Google Scholar 

  39. 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).

    Article  Google Scholar 

  40. 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).

    Article  Google Scholar 

  41. 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).

    Article  Google Scholar 

  42. 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).

    Article  Google Scholar 

  43. 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).

    Article  Google Scholar 

  44. Zhu, Y.-C. et al. Torsional tunneling splitting in a water trimer. J. Am. Chem. Soc. 144, 21356–21362 (2022).

    Article  Google Scholar 

  45. 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).

    Article  Google Scholar 

  46. 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).

    Article  Google Scholar 

  47. 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).

    Article  Google Scholar 

  48. 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).

    Article  Google Scholar 

  49. 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).

    Article  Google Scholar 

  50. Kapil, V. et al. i-PI 2.0: a universal force engine for advanced molecular simulations. Comput. Phys. Commun. 236, 214–223 (2019).

    Article  Google Scholar 

  51. 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).

    Article  Google Scholar 

  52. 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).

    Article  Google Scholar 

  53. 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).

    Article  Google Scholar 

  54. 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).

    Article  Google Scholar 

  55. Cheng, B. Cartesian atomic cluster expansion for machine learning interatomic potentials. npj Comput. Mater. 10, 157 (2024).

    Article  Google Scholar 

  56. 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).

    Article  Google Scholar 

  57. 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).

    Article  Google Scholar 

  58. 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).

    Article  Google Scholar 

  59. 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).

    Article  Google Scholar 

  60. 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).

  61. 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).

    Article  Google Scholar 

  62. 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).

    Article  Google Scholar 

  63. 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).

  64. Anderson, J. B. A random-walk simulation of the Schrödinger equation: \({{{\mathrm{H}}}_{{3}}^{+}}\). J. Chem. Phys. 63, 1499–1503 (1975).

    Article  Google Scholar 

  65. 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).

  66. Yu, Q. Source code and example of MB-PIPNet approach. Zenodo https://doi.org/10.5281/zenodo.14954863 (2025).

  67. 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).

    Article  Google Scholar 

  68. 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).

    Article  Google Scholar 

  69. 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).

    Article  Google Scholar 

  70. Mills, R. Self-diffusion in normal and heavy water in the range 1–45°. J. Phys. Chem. 77, 685–688 (1973).

    Article  Google Scholar 

  71. 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).

    Article  Google Scholar 

  72. 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).

    Article  Google Scholar 

  73. 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).

    Article  Google Scholar 

  74. Soper, A. & Benmore, C. Quantum differences between heavy and light water. Phys. Rev. Lett. 101, 065502 (2008).

    Article  Google Scholar 

Download references

关于《Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials》的评论


暂无评论

发表评论

摘要

The provided text appears to be a partial list of references for a scientific paper or research article that focuses on developing and testing machine learning models (specifically MB-PIPNet) for simulating water clusters and liquid water. Below is an organized summary of the key points from these references: ### Key Papers Cited: 1. **Yu et al.** - Provide data files and source code related to the development and testing of MB-PIPNet models for studying water trimer, methane-water cluster, and liquid water. - Data Files: Available on figshare (DOI: 10.6084/m9.figshare.28510238.v1) - Source Code: Available on Zenodo (DOI: 10.5281/zenodo.14954863) ### Related Papers: - **Wang & Bowman** - Discuss rigorous calculations of dissociation energies for water trimer (H₂O)₃ and deuterated water trimer (D₂O)₃. - Reference: J. Chem. Phys., 135, 131101 (2011) - **Anderson** - Presents a random-walk simulation method for solving the Schrödinger equation, applied to H₃⁺ ion. - Reference: J. Chem. Phys., 63, 1499–1503 (1975) ### Diffusion Studies: - **Mills** - Reports self-diffusion coefficients in normal and heavy water over a range of temperatures from 1 to 45°C. - Reference: J. Phys. Chem., 77, 685–688 (1973) - **Holz et al.** - Provide temperature-dependent diffusion coefficients for water and six selected molecular liquids. - Reference: Phys. Chem. Chem. Phys., 2, 4740–4742 (2000) ### Structural Studies: - **Soper & Benmore** - Investigate quantum differences between heavy and light water using neutron scattering techniques. - Reference: Phys. Rev. Lett., 101, 065502 (2008) - **Skinner et al.** - Report benchmark data for the oxygen-oxygen pair distribution function in ambient water from X-ray diffraction measurements with a wide Q-range. - References: - J. Chem. Phys., 138, 074506 (2013) - J. Chem. Phys., 141, 214507 (2014) ### Methodological Papers: - **Yu et al.** - Development and testing of MB-PIPNet models for studying water clusters and liquid water. - Reference: figshare (DOI: 10.6084/m9.figshare.28510238.v1) - **Qu & Bowman** - Introduce a fragmented, permutationally invariant polynomial approach for potential energy surfaces of large molecules, applied to N-methyl acetamide. - Reference: J. Chem. Phys., 150, 141101 (2019) ### Machine Learning Models: - **Schütt et al.** - Propose deep tensor neural networks for quantum chemical insights. - Reference: Nat. Commun., 8, 13890 (2017) ### Computational Methods and Algorithms: - **Moré** - Discusses the Levenberg-Marquardt algorithm for optimization problems in numerical analysis. - Reference: Springer Berlin Heidelberg (DOI not provided in summary) These references cover various aspects of computational chemistry, diffusivity studies, structural investigations, and machine learning approaches specifically tailored to water systems.