Evidence map›Paper›PMID 31725740›Full record

ArticlePloS one2019

Exploring optimization strategies for improving explicit water models: Rigid n-point model and polarizable model based on Drude oscillator.

Yeyue Xiong, Alexey V Onufriev

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Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Evaluating Polarizable Biomembrane Simulations against Experiments.Journal of chemical theory and computation · 2024
    Article
  2. Structures and Dynamics of DNA Mini-Dumbbells Are Force Field Dependent.Journal of chemical theory and computation · 2023
    Article
  3. Article
  4. Fast Polarizable Water Model for Atomistic Simulations.Journal of chemical theory and computation · 2022
    Article
  5. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yeyue XiongDepartment of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States of America.
Alexey V OnufrievDepartment of Physics, Virginia Tech, Blacksburg, VA, United States of America.ORCID 0000-0002-4930-6612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rigid n-point water models are widely used in atomistic simulations, but have known accuracy drawbacks. Increasing the number of point charges, as well as adding electronic polarizability, are two common strategies for accuracy improvements. Both strategies come at considerable computational cost, which weighs heavily against modest possible accuracy improvements in practical simulations. In an effort to provide guidance for model development, here we have explored the limiting accuracy of "electrostatically globally optimal" n-point water models in terms of their ability to reproduce properties of water dimer-a mimic of the condensed state of water. For a given n, each model is built upon a set of reference multipole moments (e.g. ab initio) and then optimized to reproduce water dimer total dipole moment. The models are then evaluated with respect to the accuracy of reproducing the geometry of the water dimer. We find that global optimization of the charge distribution alone can deliver high accuracy of the water model: for n = 4 or n = 5, the geometry of the resulting water dimer can be almost within 50 of the ab initio reference, which is half that of the experimental error margin. Thus, global optimization of the charge distribution of classical n-point water models can lead to high accuracy models. We also find that while the accuracy improvement in going from n = 3 to n = 4 is substantial, the additional accuracy increase in going from n = 4 to n = 5 is marginal. Next, we have explored accuracy limitations of the standard practice of adding electronic polarizability (via a Drude particle) to a "rigid base"-pre-optimization rigid n-point water model. The resulting model (n = 3) shows a relatively small improvement in accuracy, suggesting that the strategy of merely adding the polarizability to an inferior accuracy water model used as the base cannot fix the defects of the latter. An alternative strategy in which the parameters of the rigid base model are globally optimized along with the polarizability parameter is much more promising: the resulting 3-point polarizable model out-performs even the 5-point optimal rigid model by a large margin. We suggest that future development efforts consider 3- and 4-point polarizable models where global optimization of the "rigid base" is coupled to optimization of the polarizability to deliver globally optimal solutions.

Indexed as

Models, MolecularWaterDimerizationMolecular ConformationWater

Identifiers

PMID31725740
PMCPMC6855648

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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.