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Electronic density response to molecular geometric changes from explicit electronic susceptibility calculations.

Arvid Conrad Ihrig¹, Arne Scherrer, Daniel Sebastiani

¹Dahlem Center for Complex Quantum Systems, Physics Department, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.

The Journal of Chemical Physics

|September 14, 2013

Summary

This study introduces a new method to calculate how molecular electron clouds change with geometry. The approach uses linear electronic susceptibility, proving useful for predicting dipole moments from geometric distortions.

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Area of Science:

Computational chemistry
Quantum chemistry
Molecular modeling

Background:

Understanding molecular electronic structure is crucial in chemistry.
Geometric distortions significantly influence molecular properties.
Accurate prediction of electronic response to structural changes is computationally challenging.

Purpose of the Study:

To develop a first-principles method for calculating the response of molecular electronic charge distribution to geometric distortion.
To introduce and utilize the concept of linear electronic susceptibility for this purpose.
To compute dipole moments resulting from geometric changes.

Main Methods:

An explicit representation of linear electronic susceptibility was employed.
The method avoids self-consistency calculations for the electronic response.
Electronic susceptibility's invariance to small geometric changes was investigated.

Main Results:

The linear electronic susceptibility was found to be nearly invariant under small geometric distortions.
Dipole moments were accurately computed from the response density induced by geometry changes.
Results were validated against traditional self-consistent field calculations.

Conclusions:

The presented first-principles approach offers an efficient way to compute electronic responses to geometric distortions.
Linear electronic susceptibility provides a direct link between perturbation and response, simplifying calculations.
This method accurately predicts dipole moments, offering a valuable tool in computational chemistry.