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Reduced Scaling of Optimal Regional Orbital Localization via Sequential Exhaustion of the Single-Particle Space.

Guorong WengMariya RomanovaArsineh ApelianHanbin SongVojtěch Vlček
Published in: Journal of chemical theory and computation (2022)
Wannier functions have become a powerful tool in the electronic structure calculations of extended systems. The generalized Pipek-Mezey Wannier functions exhibit appealing characteristics (e.g., reaching an optimal localization and the separation of the σ-π orbitals) compared with other schemes. However, when applied to giant nanoscale systems, the orbital localization suffers from a large computational cost overhead if one is interested in localized states in a small fragment of the system. Herein, we present a swift, efficient, and robust approach for obtaining regionally localized orbitals of a subsystem within the generalized Pipek-Mezey scheme. The proposed algorithm introduces a reduced work space and sequentially exhausts the entire orbital space until the convergence of the localization functional. It tackles systems with ∼10000 electrons within 0.5 h with no loss in localization quality compared to the traditional approach. Regionally localized orbitals with a higher extent of localization are obtained via judiciously extending the subsystem's size. Exemplifying on large bulk and a 4 nm wide slab of diamond with an NV - center, we demonstrate the methodology and discuss how the choice of the localization region affects the excitation energy of the defect. Furthermore, we show how the sequential algorithm is easily extended to stochastic methodologies that do not provide individual single-particle eigenstates. It is thus a promising tool to obtain regionally localized states for solving the electronic structure problems of a subsystem embedded in giant condensed systems.
Keyphrases
  • density functional theory
  • machine learning
  • deep learning
  • molecular dynamics
  • mass spectrometry
  • high resolution
  • molecular dynamics simulations
  • atomic force microscopy
  • monte carlo