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

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.

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Cited by in corpus (7)

• Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library
• Linear scaling DFT calculations for large Tungsten systems using an optimized local basis
• Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding
• Lifetime effects and satellites in the photoelectron spectrum of tungsten metal
• Thermal and Oxidation Stability of Ti${}_x$W${}_{1-x}$ Diffusion Barriers Investigated by Soft and Hard X-ray Photoelectron Spectroscopy
• Pseudo-Fragment Approach for Extended Systems Derived from Linear-Scaling DFT
• Complexity Reduction in Density Functional Theory: Locality in Space and Energy