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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Electrostatically embedded molecules-in-molecules approach and its application to molecular clusters.

Vikrant Tripathy¹, Arjun Saha², Krishnan Raghavachari¹

¹Department of Chemistry, Indiana University, Bloomington, Indiana, USA.

Journal of Computational Chemistry

|February 15, 2021

Summary

We introduce Electrostatically Embedded Molecules-In-Molecules (EE-MIM), a fragment-based quantum chemistry model that accurately captures electrostatic interactions in molecular systems. This method improves energy calculations for fragmented molecules, showing excellent performance with NPA charges.

Keywords:

MIM electrostatic embedding fragmentation molecules-in-molecules multilayer fragmentation protonated ammonia clusters quantum chemistry water clusters

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Area of Science:

Computational Chemistry
Quantum Chemistry
Method Development

Background:

Fragment-based quantum chemistry models like Molecules-In-Molecules (MIM) often neglect electrostatic interactions between molecular fragments.
Accurately accounting for these missing electrostatic interactions is crucial for reliable energy calculations in large molecular systems.

Purpose of the Study:

To introduce and validate a new fragment-based quantum chemistry model, Electrostatically Embedded Molecules-In-Molecules (EE-MIM).
To assess the impact of electrostatic embedding on the accuracy of energy calculations for fragmented molecular systems.
To evaluate the performance of different charge embedding schemes and fragmentation levels.

Main Methods:

Developed the EE-MIM method, incorporating electrostatic embedding using point charges to represent inter-fragment interactions.
Calibrated the model on water clusters (up to 57 molecules) and protonated ammonia clusters (up to 30 molecules).
Compared EE-MIM performance against the original MIM method and analyzed various parameters like charge types and fragmentation strategies.

Main Results:

EE-MIM significantly improves the accuracy of total energy calculations compared to standard MIM by accounting for electrostatic effects.
The use of background charges enhances the performance of both one- and two-layer MIM approaches, particularly for one-layer calculations.
Natural Population Analysis (NPA) charges were found to be the most effective for electrostatic embedding in both water and protonated ammonia clusters.

Conclusions:

EE-MIM provides a computationally efficient and accurate method for calculating energies of large molecular systems by addressing missing electrostatic interactions.
Electrostatic embedding is essential for improving the accuracy of fragment-based quantum chemistry methods.
NPA charges offer the best performance for electrostatic embedding in the tested systems.