Evidence map›Paper›PMID 40954951›Full record

ArticleJournal of chemical theory and computation2025

GOPAL: A Water Model with Improved Thermodynamic Properties over a Large Pressure and Temperature Range Optimized Using Multistate Reweighting and Configuration Space Mapping.

Himanshu Paliwal, Michael R Shirts

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Himanshu PaliwalDepartment of Chemical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
Michael R ShirtsDepartment of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States.ORCID 0000-0003-3249-1097

Funding

Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug designR01GM132386 · NIGMS · UNIVERSITY OF COLORADO · PI SHIRTS, MICHAEL R · 2020 to 2023
$3.1M
NIGMS NIH HHS R01 GM132386
6 · The paper itself

Abstract

We show how reweighting and configuration mapping algorithms can be used to efficiently optimize molecular models using thermodynamic properties at a large number of state points from molecular simulations. As a proof of concept, we perform a multidimensional, multiobjective parameterization of a rigid water model over a large pressure [1-5000 atm] and temperature [274.15-372.15 K] range to the experimental property surfaces estimated using the IAPWS95 equation of state for water. Over 4000 parameter combinations in a six-dimensional parameter space were explored during the minimization. A similar parameterization with standard techniques would have taken more than 250 CPU years, but with the application of the newly developed techniques, the computational time was reduced to four CPU months. Without the added efficiency of the methods presented here, the optimization could not have simultaneously taken into account the large range of temperature and pressure points used in the fitting. The paper also describes how and why incorporating the thermodynamic properties from the first and second derivatives of Gibbs energy into the objective function helps improve the parameterization process. The resulting water model reproduces liquid phase density within the upper limit of experimental uncertainty of 0.02% over a large range of temperatures and pressures, the most accurate model yet at this low level of theory over the entire range of temperature and pressure, with little loss of fidelity in other properties. We compare the performance of this water model with 11 other rigid water models in predicting a number of other thermodynamic and kinetic properties. This process illustrates the surprising fact that a simple point charge model is able to accurately capture a substantial range of both temperature- and pressure-dependent thermodynamics without substantial deviation from experiment at ambient temperatures and pressures.

Identifiers

PMID40954951
PMCPMC12642512

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.