Application of Gaussian Electrostatic Model (GEM) Distributed Multipoles in the AMOEBA Force Field

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Force Field Development

Background:

• Accurate molecular electrostatic representation is crucial for molecular simulations.
• The AMOEBA force field utilizes distributed multipoles for electrostatic interactions.
• Existing methods for deriving distributed multipoles can be computationally intensive or lack intrinsic finiteness.

Purpose of the Study:

• To incorporate distributed multipoles derived from the Gaussian Electrostatic Model (GEM) into the AMOEBA force field.
• To reparametrize key molecular systems using the new GEM-based distributed multipoles.
• To validate the performance of the enhanced AMOEBA force field against established benchmarks.

Main Methods:

• Derivation of distributed multipoles using the Gaussian Electrostatic Model (GEM), employing both analytical and numerical fitting procedures.
• Reparametrization of water and alanine di-peptide using GEM-derived distributed multipoles (GEM-DM) at established theoretical levels.
• Fitting of multipoles for the di-methyl imidazolium/chloride (DMIM(+)-Cl(-)) ionic liquid pair.
• Molecular dynamics (MD) simulations to assess bulk properties of the reparametrized water model.

Main Results:

• GEM-DM were successfully integrated into the AMOEBA force field.
• Intermolecular Coulombic interaction results for test systems showed excellent agreement.
• MD simulations of the reparametrized AMOEBA water model with GEM-DM reproduced bulk properties (e.g., liquid density, enthalpy of vaporization) comparable to the original AMOEBA force field.
• A publicly available software package for calculating GEM Hermite coefficients and distributed multipoles was developed.

Conclusions:

• The integration of GEM-DM into the AMOEBA force field provides a robust and accurate method for electrostatic representation.
• The reparametrized models demonstrate reliable performance for molecular simulations.
• The developed computational tools facilitate the application of GEM-DM in future molecular modeling studies.