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Published on: November 11, 2025

Efficient representation of the linear density-density response function.

Christian Dreßler¹, Arne Scherrer¹, Paul Ahlert¹

¹Institute of Chemistry, Martin-Luther-University Halle-Wittenberg, 06120, Halle (Saale), Germany.

Journal of Computational Chemistry

|August 30, 2019

Summary

This summary is machine-generated.

This study introduces a computationally efficient moment expansion for the molecular linear electronic density-density response function. This method significantly reduces dimensionality, simplifying calculations for molecular systems like water.

Keywords:

density functional perturbation theory density-density response function linear compact operator molecular interaction

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Published on: April 30, 2023

Area of Science:

Computational Chemistry
Quantum Chemistry
Theoretical Chemistry

Background:

The molecular linear electronic density-density response function is crucial for understanding molecular properties.
Calculating this function traditionally requires significant computational resources, including storing numerous eigenfunctions.

Purpose of the Study:

To develop a computationally efficient method for representing the molecular linear electronic density-density response function.
To reduce the dimensionality of the response kernel calculations.

Main Methods:

Derivation of mathematical foundations for a moment expansion representation.
Dimensionality reduction from approximately 10^3 to 10^1.
General formulation applicable to linear operators.

Main Results:

A novel, computationally efficient moment expansion for the response function.
Significant dimensionality reduction, avoiding the need to compute and store numerous eigenfunctions.
Demonstration of the method's applicability to a water molecule under external perturbation.

Conclusions:

The proposed moment expansion offers a practical and efficient approach to calculating molecular response functions.
The general formulation allows for broad applicability across various computational chemistry problems.
This method simplifies complex calculations, making them more accessible.