Complexity Reduction in Large Quantum Systems: Fragment Identification and Population Analysis via a Local Optimized Minimal Basis

TL;DR

This paper introduces a method within Kohn-Sham DFT to systematically identify and analyze fragments of large quantum systems, enabling more reliable electrostatic multipole calculations with reduced arbitrariness.

Contribution

The authors develop a quantitative fragmentation and population analysis method using a minimal basis within a DFT framework, improving fragment definition and electrostatic property extraction.

Findings

Effective fragment identification with minimal basis functions

Accurate electrostatic multipole extraction from first principles

Reduced arbitrariness in quantum system partitioning

Abstract

We present, within Kohn-Sham Density Functional Theory calculations, a quantitative method to identify and assess the partitioning of a large quantum mechanical system into fragments. We then show how within this framework simple generalizations of other well-known population analyses can be used to extract, from first principles, reliable electrostatic multipoles for the identified fragments. Our approach reduces arbitrariness in the fragmentation procedure, and enables the possibility to assess, quantitatively, whether the corresponding fragment multipoles can be interpreted as observable quantities associated to a system's moiety. By applying our formalism within the code BigDFT, we show that the use of a minimal set of in-situ optimized basis functions allows at the same time a proper fragment definition and an accurate description of the electronic structure.