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WIREs Comput Mol Sci
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Advanced models for water simulations

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Molecular simulations of water using classical, molecular mechanic potential energy functions have enjoyed a 50‐year history of development, and much has been learned regarding their parameterization and the essential physics that must be captured in order to reproduce water properties across the phase diagram and across system sizes, from the dimer to the condensed phase. While pairwise‐additive force fields using fixed, point charge‐based electrostatics have dominated this history owing to computational cost, their limitations in transferability are being recognized, owing particularly to the lack of many‐body effects, as well as an inherent difficulty in capturing quantum mechanical effects that become important at short intermolecular separation. This has spurred an impressive development of novel functional forms and parameterization schemes to account for such effects, especially the leading many‐body effect of polarization. This review discusses recent efforts in the development of advanced models of water, particularly with regard to important details of their parameterization from quantum mechanical or experimental data, the development of novel functional forms including machine learning‐based models, and algorithms that reduce the computational cost of polarization dramatically, permitting them to potentially become competitive with pairwise‐additive models as the standby of condensed‐phase simulation. These technical developments are appraised based on their ability to impact numerical calculations on water, particularly the condensed phase, and it is hoped that this article provides a clear connection between the essential physics captured by the model and their fitness across a range of environments. WIREs Comput Mol Sci 2018, 8:e1355. doi: 10.1002/wcms.1355

This article is categorized under:

  • Computer and Information Science > Computer Algorithms and Programming
  • Molecular and Statistical Mechanics > Molecular Interactions
  • Software > Simulation Methods
Comparison of the ALMO‐EDA decomposition of the intermolecular energy profile against AMOEBA03 for the water trimer. (a) Total energy and total polarization energy for AMOEBA against the ωB97X‐V DFT benchmark and its decomposition using ALMO‐EDA for polarization. (b) The two‐body polarization energy for one of the three pairs in the trimer. (c) The three‐body polarization as well as the sum of ALMO's three‐body polarization and charge‐transfer terms. The distance coordinate corresponds to displacement from equilibrium from the reference geometries (Reprinted with permission from Ref . Copyright 2017 AIP Publishing).
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Comparison of water properties of iAMOEBA water model against experiment. (a) Arrhenius plot of self‐diffusion constant of liquid water versus temperature, which includes a comparison to AMOEBA03, (b) liquid–vapor coexistence curve, and (c) vapor pressure curve of the iAMOEBA model (Reprinted with permission from Ref . Copyright 2013 American Chemical Society).
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IR spectra of liquid water from experiment (black) and compared to different classical water models (a) using the SPC/Fw, TTM3‐F, and iAMOEBA models. Gray bars represent gas phase vibrational frequencies from experiment. Inset: Magnification of the far IR region (<1000 wavenumber) (Reprinted with permission from Ref . Copyright 2013 American Chemical Society). (b) THz experimental spectra (arbitrary units) of pure bulk water compared to polarizable AMOEBA14 (solid red line) and when polarization interactions are removed (dashed red) (Reprinted with permission from Ref . Copyright 2017 Royal Society of Chemistry).
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Comparisons of the standard preconditioned conjugate gradient SCF solver at 10−6 RMSD convergence and the SCF‐less method for AMOEBA water. (a) Time autocorrelation function of the induced dipoles for oxygen and hydrogen; (b) Oxygen–oxygen radial distribution function; (c) simulation speed‐up in nanoseconds per day for OpenMP scaling as a function of the number of cores for a box of 512 water molecules in the NVT ensemble at 298.0 K (Reprinted with permission from Ref . Copyright 2017 American Chemical Society).
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Steps of the ForceBalance optimization cycle. The initial force field parameters (lower left) are used to perform simulations using molecular dynamics (MD) software (upper left). The objective function is computed as a least‐squares function of the differences between simulation results and reference data (upper right). The optimization method updates the parameters in order to minimize the objective function (bottom right).
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Molecular and Statistical Mechanics > Molecular Interactions
Computer and Information Science > Computer Algorithms and Programming

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