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Ewald-based methods for Gaussian integral evaluation: application to a new parameterization of GEM.

Robert E Duke1, G Andrés Cisneros2

  • 1Department of Chemistry, University of North Texas, Denton, TX, 76202, USA.

Journal of Molecular Modeling
|September 11, 2019
PubMed
Summary

We present GEM*, a refined computational model for molecular simulations. This study extensively evaluates Particle-Mesh Ewald (PME) and Fast Fourier Poisson (FFP) methods for their accuracy and performance in molecular dynamics simulations.

Keywords:
Fast Fourier PoissonGaussian electrostatic modelParticle mesh EwaldPolarizable force field

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Physical Chemistry

Background:

  • Accurate computational simulation potentials are crucial for molecular modeling.
  • The Gaussian Electrostatic Model (GEM) is a force field developed using molecular densities.
  • GEM's philosophy is rooted in reproducing quantum mechanical (QM) energy decomposition analysis (EDA) components.

Purpose of the Study:

  • To present the latest parameterization of the Gaussian Electrostatic Model (GEM*).
  • To conduct an extensive study on the performance and accuracy of Particle-Mesh Ewald (PME) and Fast Fourier Poisson (FFP) methods for molecular dynamics (MD) simulations using GEM*.
  • To investigate the temperature dependence of bulk properties and the impact of various parameters on PME and FFP methods.

Main Methods:

  • Utilizing fitting of accurate QM molecular densities with auxiliary basis sets (Hermite Gaussians).
  • Employing Particle-Mesh Ewald (PME) and Fast Fourier Poisson (FFP) methods for efficient Gaussian integral evaluation.
  • Performing molecular dynamics (MD) simulations with a hybrid version of the GEM* potential.

Main Results:

  • The latest parameterization of GEM* is presented.
  • Extensive evaluation of PME and FFP methods in GEM*-based MD simulations was performed.
  • Temperature-dependent bulk properties and the influence of parameters on PME/FFP performance were analyzed.

Conclusions:

  • GEM* offers a refined approach for computational simulations.
  • PME and FFP methods demonstrate varying performance and accuracy in GEM*-based MD simulations.
  • Understanding parameter effects is key for optimizing simulation accuracy and efficiency.