1932

Abstract

Interaction analysis techniques, including the many-body expansion (MBE), symmetry-adapted perturbation theory, and energy decomposition analysis, allow for an intuitive understanding of complex molecular interactions. We review these methods by first providing a historical context for the study of many-body interactions and discussing how nonadditivities emerge from Hamiltonians containing strictly pairwise-additive interactions. We then elaborate on the synergy between these interaction analysis techniques and the development of advanced force fields aimed at accurately reproducing the Born–Oppenheimer potential energy surface. In particular, we focus on ab initio–based force fields that aim to explicitly reproduce many-body terms and are fitted to high-level electronic structure results. These force fields generally incorporate many-body effects through () parameterization of distributed multipoles, () explicit fitting of the MBE, () inclusion of many-atom features in a neural network, and () coarse-graining of many-body terms into an effective two-body term. We also discuss the emerging use of the MBE to improve the accuracy and speed of ab initio molecular dynamics.

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2023-04-24
2024-04-27
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Literature Cited

  1. 1.
    Nilsson A, Pettersson LGM. 2015. The structural origin of anomalous properties of liquid water. Nat. Commun. 6:8998
    [Google Scholar]
  2. 2.
    Russo J, Akahane K, Tanaka H. 2018. Water-like anomalies as a function of tetrahedrality. PNAS 115:E3333–41
    [Google Scholar]
  3. 3.
    Poole PH, Sciortino F, Essmann U, Stanley HE. 1992. Phase behaviour of metastable water. Nature 360:324–28
    [Google Scholar]
  4. 4.
    Sellberg JA, Huang C, McQueen TA, Loh ND, Laksmono H et al. 2014. Ultrafast X-ray probing of water structure below the homogeneous ice nucleation temperature. Nature 510:381–84
    [Google Scholar]
  5. 5.
    Kim KH, Spah A, Pathak H, Perakis F, Mariedahl D et al. 2017. Maxima in the thermodynamic response and correlation functions of deeply supercooled water. Science 358:1589–93
    [Google Scholar]
  6. 6.
    Niskanen J, Fondell M, Sahle CJ, Eckert S, Jay RM et al. 2019. Compatibility of quantitative X-ray spectroscopy with continuous distribution models of water at ambient conditions. PNAS 116:4058–63
    [Google Scholar]
  7. 7.
    Smolentsev N, Smit WJ, Bakker HJ, Roke S. 2017. The interfacial structure of water droplets in a hydrophobic liquid. Nat. Commun. 8:15548
    [Google Scholar]
  8. 8.
    Chaplin M. 2020. Anomalous properties of water. Work. Pap., London South Bank Univ. London: https://water.lsbu.ac.uk/water/water_anomalies.html
  9. 9.
    Marx D, Hutter J. 2009. Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods Cambridge, UK: Cambridge Univ. Press
  10. 10.
    Iftimie R, Minary P, Tuckerman ME. 2005. Ab initio molecular dynamics: concepts, recent developments, and future trends. PNAS 102:6654–59
    [Google Scholar]
  11. 11.
    Hehre WJ, Radom L, Schleyer PVR, Pople JA. 1986. Ab Initio Molecular Orbital Theory New York: Wiley
  12. 12.
    Crittenden DL. 2013. A hierarchy of static correlation models. J. Phys. Chem. A 117:3852–60
    [Google Scholar]
  13. 13.
    Del Ben M, Hutter J, VandeVondele J. 2012. Second-order Møller-Plesset perturbation theory in the condensed phase: an efficient and massively parallel Gaussian and plane waves approach. J. Chem. Theory Comput. 8:4177–88
    [Google Scholar]
  14. 14.
    Bernal JD, Fowler RH. 1933. A theory of water and ionic solution, with particular reference to hydrogen and hydroxyl ions. J. Chem. Phys. 1:515–48
    [Google Scholar]
  15. 15.
    Barker JA, Watts RO. 1969. Structure of water: a Monte Carlo calculation. Chem. Phys. Lett. 3:144–45
    [Google Scholar]
  16. 16.
    Rahman A, Stillinger FH. 1971. Molecular dynamics study of liquid water. J. Chem. Phys. 55:3336–59
    [Google Scholar]
  17. 17.
    Burnham CJ, Xantheas SS. 2002. Development of transferable interaction models for water. I. Prominent features of the water dimer potential energy surface. J. Chem. Phys. 116:1479–92
    [Google Scholar]
  18. 18.
    Burnham CJ, Xantheas SS. 2002. Development of transferable interaction models for water. IV. A flexible, all-atom polarizable potential TTM2-F based on geometry dependent charges derived from an ab initio monomer dipole moment surface. J. Chem. Phys. 116:5115–24
    [Google Scholar]
  19. 19.
    Fanourgakis GS, Xantheas SS. 2006. The flexible, polarizable, Thole-type interaction potential for water (TTM2-F) revisited. J. Phys. Chem. A 110:4100–6
    [Google Scholar]
  20. 20.
    Fanourgakis GS, Xantheas SS. 2008. 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
    [Google Scholar]
  21. 21.
    Bowman JM, Braams BJ, Carter S, Chen C, Czakó G et al. 2010. Ab-initio-based potential energy surfaces for complex molecules and molecular complexes. J. Phys. Chem. Lett. 1:1866–74
    [Google Scholar]
  22. 22.
    Bowman JM, Czakó G, Fu B. 2011. High-dimensional ab initio potential energy surfaces for reaction dynamics calculations. Phys. Chem. Chem. Phys. 13:8094–111
    [Google Scholar]
  23. 23.
    Paesani F, Xantheas SS, Voth GA. 2009. Infrared spectroscopy and hydrogen-bond dynamics of liquid water from centroid molecular dynamics with an ab initio–based force field. J. Phys. Chem. B 113:13118–30
    [Google Scholar]
  24. 24.
    Paesani F, Iuchi S, Voth GA. 2007. Quantum effects in liquid water from an ab initio–based polarizable force field. J. Chem. Phys. 127:074506
    [Google Scholar]
  25. 25.
    Guillot B. 2002. A reappraisal of what we have learnt during three decades of computer simulations on water. J. Mol. Liq. 101:219–60
    [Google Scholar]
  26. 26.
    Salem L. 1961. The forces between polyatomic molecules. II. Short-range repulsive forces. Proc. R. Soc. A 264:379–91
    [Google Scholar]
  27. 27.
    Hankins D, Moskowitz JW, Stillinger FH. 1970. Water molecule interactions. J. Chem. Phys. 53:4544–54
    [Google Scholar]
  28. 28.
    Del Bene JE, Pople JA. 1973. Theory of molecular interactions. III. A comparison of studies of H2O polymers using different molecular-orbital basis sets. J. Chem. Phys. 58:3605–8
    [Google Scholar]
  29. 29.
    Clementi E, Kołos W, Lie GC, Ranghino G. 1980. Nonadditivity of interaction in water trimers. Int. J. Quantum Chem. 17:377–98
    [Google Scholar]
  30. 30.
    Xantheas SS. 1994. Ab initio studies of cyclic water clusters (H2O)n, n = 1–6. II. Analysis of many-body interactions. J. Chem. Phys. 100:7523–34
    [Google Scholar]
  31. 31.
    Richard RM, Lao KU, Herbert JM. 2014. Understanding the many-body expansion for large systems. I. Precision considerations. J. Chem. Phys. 141:014108
    [Google Scholar]
  32. 32.
    Richard RM, Herbert JM. 2012. A generalized many-body expansion and a unified view of fragment-based methods in electronic structure theory. J. Chem. Phys. 137:064113
    [Google Scholar]
  33. 33.
    London F. 1930. Zur Theorie und Systematik der Molekularkräfte. Z. Phys. 63:245–79
    [Google Scholar]
  34. 34.
    van der Waals JD. 1873. Over de Continuiteit van den Gas- en Vloeistoftoestand, Vol. 1 Leiden, Neth: Sijthoff
  35. 35.
    Axilrod BM, Teller E. 1943. Interaction of the van der Waals type between three atoms. J. Chem. Phys. 11:299–300
    [Google Scholar]
  36. 36.
    Patkowski K. 2020. Recent developments in symmetry-adapted perturbation theory. Wiley Interdiscip. Rev. Comput. Mol. Sci. 10:e1452
    [Google Scholar]
  37. 37.
    Rackers JA, Ponder JW. 2019. Classical Pauli repulsion: an anisotropic, atomic multipole model. J. Chem. Phys. 150:084104
    [Google Scholar]
  38. 38.
    Garcia J, Podeszwa R, Szalewicz K. 2020. SAPT codes for calculations of intermolecular interaction energies. J. Chem. Phys. 152:184109
    [Google Scholar]
  39. 39.
    Szalewicz K, Bukowski R, Jeziorski B 2005. On the importance of many-body forces in clusters and condensed phase. Theory and Applications of Computational Chemistry C Dykstra, G Frenking, K Kim, G Scuseria 919–62. Amsterdam: Elsevier
    [Google Scholar]
  40. 40.
    Lotrich VF, Szalewicz K. 2000. Perturbation theory of three-body exchange nonadditivity and application to helium trimer. J. Chem. Phys. 112:112–21
    [Google Scholar]
  41. 41.
    Lotrich VF, Szalewicz K. 1997. Three-body contribution to binding energy of solid argon and analysis of crystal structure. Phys. Rev. Lett. 79:1301–4
    [Google Scholar]
  42. 42.
    Lotrich VF, Jankowski P, Szalewicz K. 1998. Symmetry-adapted perturbation theory of three-body nonadditivity in the Ar2HF trimer. J. Chem. Phys. 108:4725–38
    [Google Scholar]
  43. 43.
    Podeszwa R, Szalewicz K. 2007. Three-body symmetry-adapted perturbation theory based on Kohn-Sham description of the monomers. J. Chem. Phys. 126:194101
    [Google Scholar]
  44. 44.
    Podeszwa R, Rice BM, Szalewicz K. 2008. Predicting structure of molecular crystals from first principles. Phys. Rev. Lett. 101:115503
    [Google Scholar]
  45. 45.
    Medders GR, Paesani F. 2013. Many-body convergence of the electrostatic properties of water. J. Chem. Theory Comput. 9:4844–52
    [Google Scholar]
  46. 46.
    Demerdash O, Head-Gordon T. 2016. Convergence of the many-body expansion for energy and forces for classical polarizable models in the condensed phase. J. Chem. Theory Comput. 12:3884–93
    [Google Scholar]
  47. 47.
    Heindel JP, Xantheas SS. 2021. Molecular dynamics driven by the many-body expansion (MBE-MD). J. Chem. Theory Comput. 17:7341–52
    [Google Scholar]
  48. 48.
    Heindel JP, Xantheas SS. 2020. The many-body expansion for aqueous systems revisited. I. Water–water interactions. J. Chem. Theory Comput. 16:6843–55
    [Google Scholar]
  49. 49.
    Heindel JP, Xantheas SS. 2021. The many-body expansion for aqueous systems revisited. II. Alkali metal and halide ion–water interactions. J. Chem. Theory Comput. 17:2200–16
    [Google Scholar]
  50. 50.
    Herman KM, Heindel JP, Xantheas SS. 2021. The many-body expansion for aqueous systems revisited. III. Hofmeister ion–water interactions. Phys. Chem. Chem. Phys. 23:11196–210
    [Google Scholar]
  51. 51.
    Mao Y, Loipersberger M, Horn PR, Das A, Demerdash O et al. 2021. From intermolecular interaction energies and observable shifts to component contributions and back again: a tale of variational energy decomposition analysis. Annu. Rev. Phys. Chem. 72:641–66
    [Google Scholar]
  52. 52.
    Khaliullin RZ, Cobar EA, Lochan RC, Bell AT, Head-Gordon M. 2007. Unravelling the origin of intermolecular interactions using absolutely localized molecular orbitals. J. Phys. Chem. A 111:8753–65
    [Google Scholar]
  53. 53.
    Horn PR, Mao Y, Head-Gordon M. 2016. Probing non-covalent interactions with a second generation energy decomposition analysis using absolutely localized molecular orbitals. Phys. Chem. Chem. Phys. 18:23067–79
    [Google Scholar]
  54. 54.
    Thirman J, Head-Gordon M. 2015. An energy decomposition analysis for second-order Møller–Plesset perturbation theory based on absolutely localized molecular orbitals. J. Chem. Phys. 143:084124
    [Google Scholar]
  55. 55.
    Loipersberger M, Lee J, Mao Y, Das AK, Ikeda K et al. 2019. Energy decomposition analysis for interactions of radicals: theory and implementation at the MP2 level with application to hydration of halogenated benzene cations and complexes between and pyridine and imidazole. J. Phys. Chem. A 123:9621–33
    [Google Scholar]
  56. 56.
    Andrés J, Ayers PW, Boto RA, Carbó-Dorca R, Chermette H et al. 2019. Nine questions on energy decomposition analysis. J. Comput. Chem. 40:2248–83
    [Google Scholar]
  57. 57.
    Stone A. 2013. The Theory of Intermolecular Forces Oxford, UK: Oxford Univ. Press
  58. 58.
    Herbert JM. 2021. Neat, simple, and wrong: debunking electrostatic fallacies regarding noncovalent interactions. J. Phys. Chem. A 125:7125–37
    [Google Scholar]
  59. 59.
    Partridge H, Schwenke DW. 1997. The determination of an accurate isotope dependent potential energy surface for water from extensive ab initio calculations and experimental data. J. Chem. Phys. 106:4618–39
    [Google Scholar]
  60. 60.
    Arismendi-Arrieta DJ, Riera M, Bajaj P, Prosmiti R, Paesani F. 2016. i-TTM model for ab initio–based ion–water interaction potentials. 1. Halide–water potential energy functions. J. Phys. Chem. B 120:1822–32
    [Google Scholar]
  61. 61.
    Riera M, Götz AW, Paesani F. 2016. The i-TTM model for ab initio–based ion–water interaction potentials. II. Alkali metal ion–water potential energy functions. Phys. Chem. Chem. Phys. 18:30334–43
    [Google Scholar]
  62. 62.
    Das AK, Urban L, Leven I, Loipersberger M, Aldossary A et al. 2019. Development of an advanced force field for water using variational energy decomposition analysis. J. Chem. Theory Comput. 15:5001–13
    [Google Scholar]
  63. 63.
    Das AK, Demerdash ON, Head-Gordon T. 2018. Improvements to the AMOEBA force field by introducing anisotropic atomic polarizability of the water molecule. J. Chem. Theory Comput. 14:6722–33
    [Google Scholar]
  64. 64.
    Mao Y, Demerdash O, Head-Gordon M, Head-Gordon T. 2016. Assessing ion–water interactions in the AMOEBA force field using energy decomposition analysis of electronic structure calculations. J. Chem. Theory Comput. 12:5422–37
    [Google Scholar]
  65. 65.
    Demerdash O, Mao Y, Liu T, Head-Gordon M, Head-Gordon T. 2017. Assessing many-body contributions to intermolecular interactions of the AMOEBA force field using energy decomposition analysis of electronic structure calculations. J. Chem. Phys. 147:161721
    [Google Scholar]
  66. 66.
    Mobley DL, Wymer KL, Lim NM, Guthrie JP. 2014. Blind prediction of solvation free energies from the SAMPL4 challenge. J. Comput. Aided Mol. 28:135–50
    [Google Scholar]
  67. 67.
    Das AK, Liu M, Head-Gordon T. 2022. Development of a many-body force field for aqueous alkali metal and halogen ions: an energy decomposition analysis guided approach. J. Chem. Theory Comput. 18:953–67
    [Google Scholar]
  68. 68.
    Lemkul JA, Huang J, Roux B, MacKerell AD Jr. 2016. An empirical polarizable force field based on the classical Drude oscillator model: development history and recent applications. Chem. Rev. 116:4983–5013
    [Google Scholar]
  69. 69.
    Baker CM. 2015. Polarizable force fields for molecular dynamics simulations of biomolecules. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5:241–54
    [Google Scholar]
  70. 70.
    Patel S, Brooks CL III 2006. Fluctuating charge force fields: recent developments and applications from small molecules to macromolecular biological systems. Mol. Simul. 32:231–49
    [Google Scholar]
  71. 71.
    Rick SW, Stuart SJ, Berne BJ. 1994. Dynamical fluctuating charge force fields: application to liquid water. J. Chem. Phys. 101:6141–56
    [Google Scholar]
  72. 72.
    Braams BJ, Bowman JM. 2009. Permutationally invariant potential energy surfaces in high dimensionality. Int. Rev. Phys. Chem. 28:577–606
    [Google Scholar]
  73. 73.
    Xie Z, Bowman JM. 2010. Permutationally invariant polynomial basis for molecular energy surface fitting via monomial symmetrization. J. Chem. Theory Comput. 6:26–34
    [Google Scholar]
  74. 74.
    Qu C, Yu Q, Bowman JM 2018. Permutationally invariant potential energy surfaces. Annu. Rev. Phys. Chem. 69:151–75
    [Google Scholar]
  75. 75.
    Wang Y, Huang X, Shepler BC, Braams BJ, Bowman JM. 2011. Flexible, ab initio potential, and dipole moment surfaces for water. I. Tests and applications for clusters up to the 22-mer. J. Chem. Phys. 134:094509
    [Google Scholar]
  76. 76.
    Babin V, Leforestier C, Paesani F. 2013. Development of a “first principles” water potential with flexible monomers: dimer potential energy surface, VRT spectrum, and second virial coefficient. J. Chem. Theory Comput. 9:5395–403
    [Google Scholar]
  77. 77.
    Burnham CJ, Anick DJ, Mankoo PK, Reiter GF. 2008. The vibrational proton potential in bulk liquid water and ice. J. Chem. Phys. 128:154519
    [Google Scholar]
  78. 78.
    Mallory JD, Mandelshtam VA. 2016. Diffusion Monte Carlo studies of MB-Pol (H2O)2–6 and (D2O)2–6 clusters: structures and binding energies. J. Chem. Phys. 145:064308
    [Google Scholar]
  79. 79.
    Reddy SK, Straight SC, Bajaj P, Huy Pham C, Riera M et al. 2016. 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
    [Google Scholar]
  80. 80.
    Reddy SK, Moberg DR, Straight SC, Paesani F. 2017. Temperature-dependent vibrational spectra and structure of liquid water from classical and quantum simulations with the MB-Pol potential energy function. J. Chem. Phys. 147:244504
    [Google Scholar]
  81. 81.
    Homayoon Z, Conte R, Qu C, Bowman JM. 2015. Full-dimensional, high-level ab initio potential energy surfaces for H2(H2O) and H2(H2O)2 with application to hydrogen clathrate hydrates. J. Chem. Phys. 143:084302
    [Google Scholar]
  82. 82.
    Qu C, Bowman JM. 2019. Assessing the importance of the H2–H2O–H2O three-body interaction on the vibrational frequency shift of H2 in the sII clathrate hydrate and comparison with experiment. J. Phys. Chem. A 123:329–35
    [Google Scholar]
  83. 83.
    Riera M, Hirales A, Ghosh R, Paesani F. 2020. Data-driven many-body models with chemical accuracy for CH4/H2O mixtures. J. Phys. Chem. B 124:11207–21
    [Google Scholar]
  84. 84.
    Riera M, Yeh EP, Paesani F. 2020. Data-driven many-body models for molecular fluids: CO2/H2O mixtures as a case study. J. Chem. Theory Comput. 16:2246–57
    [Google Scholar]
  85. 85.
    Bajaj P, Götz AW, Paesani F. 2016. Toward chemical accuracy in the description of ion–water interactions through many-body representations. I. Halide–water dimer potential energy surfaces. J. Chem. Theory Comput. 12:2698–705
    [Google Scholar]
  86. 86.
    Riera M, Mardirossian N, Bajaj P, Götz AW, Paesani F. 2017. Toward chemical accuracy in the description of ion–water interactions through many-body representations. Alkali-water dimer potential energy surfaces. J. Chem. Phys. 147:161715
    [Google Scholar]
  87. 87.
    Bull-Vulpe EF, Riera M, Götz AW, Paesani F. 2021. MB-Fit: software infrastructure for data-driven many-body potential energy functions. J. Chem. Phys. 155:124801
    [Google Scholar]
  88. 88.
    Nandi A, Qu C, Bowman JM. 2019. Using gradients in permutationally invariant polynomial potential fitting: a demonstration for CH4 using as few as 100 configurations. J. Chem. Theory Comput. 15:2826–35
    [Google Scholar]
  89. 89.
    Nandi A, Qu C, Houston PL, Conte R, Bowman JM. 2021. Δ-machine learning for potential energy surfaces: a PIP approach to bring a DFT-based PES to CCSD(T) level of theory. J. Chem. Phys. 154:051102
    [Google Scholar]
  90. 90.
    Zhai Y, Caruso A, Gao S, Paesani F. 2020. Active learning of many-body configuration space: application to the Cs+–water MB-nrg potential energy function as a case study. J. Chem. Phys. 152:144103
    [Google Scholar]
  91. 91.
    Heindel JP, Yu Q, Bowman JM, Xantheas SS. 2018. Benchmark electronic structure calculations for H3O+(H2O)n, n = 0–5, clusters and tests of an existing 1,2,3-body potential energy surface with a new 4-body correction. J. Chem. Theory Comput. 14:4553–66
    [Google Scholar]
  92. 92.
    Headrick JM, Diken EG, Walters RS, Hammer NI, Christie RA et al. 2005. Spectral signatures of hydrated proton vibrations in water clusters. Science 308:1765–69
    [Google Scholar]
  93. 93.
    Góra U, Cencek W, Podeszwa R, van der Avoird A, Szalewicz K. 2014. Predictions for water clusters from a first-principles two- and three-body force field. J. Chem. Phys. 140:194101
    [Google Scholar]
  94. 94.
    Behler J. 2016. Perspective: machine learning potentials for atomistic simulations. J. Chem. Phys. 145:170901
    [Google Scholar]
  95. 95.
    Noé F, Tkatchenko A, Müller KR, Clementi C. 2020. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–90
    [Google Scholar]
  96. 96.
    Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. 2018. Machine learning for molecular and materials science. Nature 559:547–55
    [Google Scholar]
  97. 97.
    Behler J. 2021. Four generations of high-dimensional neural network potentials. Chem. Rev. 121:10037–72
    [Google Scholar]
  98. 98.
    Manzhos S, Carrington T Jr. 2020. Neural network potential energy surfaces for small molecules and reactions. Chem. Rev. 121:10187–217
    [Google Scholar]
  99. 99.
    Schran C, Behler J, Marx D. 2019. Automated fitting of neural network potentials at coupled cluster accuracy: protonated water clusters as testing ground. J. Chem. Theory Comput. 16:88–99
    [Google Scholar]
  100. 100.
    Schran C, Brieuc F, Marx D. 2021. Transferability of machine learning potentials: protonated water neural network potential applied to the protonated water hexamer. J. Chem. Phys. 154:051101
    [Google Scholar]
  101. 101.
    Zhang L, Han J, Wang H, Car R, E W 2018. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120:143001
    [Google Scholar]
  102. 102.
    Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. 2018. SchNet—a deep learning architecture for molecules and materials. J. Chem. Phys. 148:241722
    [Google Scholar]
  103. 103.
    Haghighatlari M, Li J, Guan X, Zhang O, Das A et al. 2022. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digit. Discov. 1:333–43
    [Google Scholar]
  104. 104.
    Goscinski A, Fraux G, Imbalzano G, Ceriotti M. 2021. The role of feature space in atomistic learning. Mach. Learn. Sci. Technol. 2:025028
    [Google Scholar]
  105. 105.
    Nguyen TT, Székely E, Imbalzano G, Behler J, Csányi G et al. 2018. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. J. Chem. Phys. 148:241725
    [Google Scholar]
  106. 106.
    Abascal JL, Vega C. 2005. A general purpose model for the condensed phases of water: TIP4P/2005. J. Chem. Phys. 123:234505
    [Google Scholar]
  107. 107.
    Berendsen HJC, Grigera JR, Straatsma TP. 1987. The missing term in effective pair potentials. J. Phys. Chem. 91:6269–71
    [Google Scholar]
  108. 108.
    Mackie C, Zech A, Head-Gordon M. 2021. Effective two-body interactions. J. Phys. Chem. A 125:7750–58
    [Google Scholar]
  109. 109.
    Medders GR, Paesani F. 2015. Infrared and Raman spectroscopy of liquid water through “first-principles” many-body molecular dynamics. J. Chem. Theory Comput. 11:1145–54
    [Google Scholar]
  110. 110.
    Loboda O, Ingrosso F, Ruiz-López MF, Reis H, Millot C. 2016. Dipole and quadrupole polarizabilities of the water molecule as a function of geometry. J. Comput. Chem. 37:2125–32
    [Google Scholar]
  111. 111.
    Lao KU, Jia J, Maitra R, Distasio RA Jr. 2018. On the geometric dependence of the molecular dipole polarizability in water: a benchmark study of higher-order electron correlation, basis set incompleteness error, core electron effects, and zero-point vibrational contributions. J. Chem. Phys. 149:204303
    [Google Scholar]
  112. 112.
    Feynman RP, Hibbs AR, Styer DF. 2010. Quantum Mechanics and Path Integrals Mineola, NY: Dover
  113. 113.
    Damm W, Frontera A, Tirado-Rives J, Jorgensen WL. 1997. OPLS all-atom force field for carbohydrates. J. Comput. Chem. 18:1955–70
    [Google Scholar]
  114. 114.
    Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM. 1992. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114:10024–35
    [Google Scholar]
  115. 115.
    Herbert JM. 2019. Fantasy versus reality in fragment-based quantum chemistry. J. Chem. Phys. 151:170901
    [Google Scholar]
  116. 116.
    Barnett RN, Landman U. 1993. Born-Oppenheimer molecular-dynamics simulations of finite systems: structure and dynamics of (H2O)2. Phys. Rev. B 48:2081–97
    [Google Scholar]
  117. 117.
    Car R, Parrinello M. 1985. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett. 55:2471–74
    [Google Scholar]
  118. 118.
    Cui J, Liu H, Jordan KD. 2006. Theoretical characterization of the (H2O)21 cluster: application of an n-body decomposition procedure. J. Phys. Chem. B 110:18872–78
    [Google Scholar]
  119. 119.
    Liu KY, Herbert JM. 2019. Energy-screened many-body expansion: a practical yet accurate fragmentation method for quantum chemistry. J. Chem. Theory Comput. 16:475–87
    [Google Scholar]
  120. 120.
    Dahlke EE, Truhlar DG. 2007. Electrostatically embedded many-body expansion for large systems, with applications to water clusters. J. Chem. Theory Comput. 3:46–53
    [Google Scholar]
  121. 121.
    Liu J, He X, Zhang JZ, Qi LW. 2018. Hydrogen-bond structure dynamics in bulk water: insights from ab initio simulations with coupled cluster theory. Chem. Sci. 9:2065–73
    [Google Scholar]
  122. 122.
    Seritan S, Thompson K, Martínez TJ. 2020. TeraChem cloud: a high-performance computing service for scalable distributed GPU-accelerated electronic structure calculations. J. Chem. Inf. Model. 60:2126–37
    [Google Scholar]
  123. 123.
    Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Kokkila-Schumacher SIL et al. 2020. TeraChem: accelerating electronic structure and ab initio molecular dynamics with graphical processing units. J. Chem. Phys. 152:224110
    [Google Scholar]
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/content/journals/10.1146/annurev-physchem-062422-023532
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  • Article Type: Review Article
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