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Published on: May 18, 2021
A Universal and Straightforward Approach to Include Penetration Effects in Electrostatic Interaction Energy
Sławomir A Bojarowski1, Prashant Kumar1, Paulina M Dominiak2
1Department of Chemistry, Biological and Chemical Research Centre, University of Warsaw, ul. Żwirki i Wigury 101, 02-089, Warszawa, Poland.
We introduce the augmented promolecule (aug-PROmol) model to improve charge penetration treatment in classical force fields. This simple yet effective method accurately approximates electrostatic energy without extra parametrization.
Area of Science:
- Computational Chemistry
- Molecular Modeling
- Quantum Chemistry
Background:
- Classical force fields often lack explicit treatment of charge penetration, limiting accuracy in electrostatic interactions.
- Accurate modeling of electrostatic interactions is crucial for understanding molecular behavior and chemical processes.
Purpose of the Study:
- To develop a novel charge-distribution model, augmented promolecule (aug-PROmol), to address the limitations of classical force fields regarding charge penetration.
- To evaluate the performance of the aug-PROmol model in approximating electrostatic energy.
Main Methods:
- The aug-PROmol model is based on the superposition of spherical atomic electron densities derived from SCF energy-optimized atomic orbitals.
- Atomic densities are rescaled using partial point charges obtained from molecular electrostatic potential fits.
- The model was validated using the S66 benchmark dataset, including nonequilibrium geometries.
Main Results:
- The aug-PROmol model requires no additional parametrization beyond point charges.
- It demonstrates good agreement in approximating electrostatic energy, with a Root Mean Square Error (RMSE) of 0.76 kcal/mol when compared to DFT-SAPT (B3LYP/aug-cc-pVTZ).
Conclusions:
- The aug-PROmol model offers a computationally efficient and accurate approach to incorporating charge penetration effects in molecular simulations.
- This method provides a valuable tool for enhancing the predictive power of classical force fields in computational chemistry.
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