Computing localized representations of the kohn-sham subspace via randomization and refinement

TL;DR

This paper introduces a two-stage randomized and refined method for efficiently computing localized Kohn-Sham subspace representations, significantly reducing computational cost while maintaining accuracy.

Contribution

It develops a novel two-stage approximate column selection strategy that accelerates the SCDM method for localized basis construction in quantum chemistry.

Findings

Two-stage method is over 30 times faster than original SCDM.

Method compares favorably with Wannier90 in efficiency.

Effective for large systems like water molecule supercells.

Abstract

Localized representation of the Kohn-Sham subspace plays an important role in quantum chemistry and materials science. The recently developed selected columns of the density matrix (SCDM) method [J. Chem. Theory Comput. 11, 1463, 2015] is a simple and robust procedure for finding a localized representation of a set of Kohn-Sham orbitals from an insulating system. The SCDM method allows the direct construction of a well conditioned (or even orthonormal) and localized basis for the Kohn-Sham subspace. The SCDM algorithm avoids the use of an optimization procedure and does not depend on any adjustable parameters. The most computationally expensive step of the SCDM method is a column pivoted QR factorization that identifies the important columns for constructing the localized basis set. In this paper, we develop a two stage approximate column selection strategy to find the important columns at much lower computational cost. We demonstrate the effectiveness of this process using the dissociation process of a BH$_3$NH$_3$ molecule, an alkane chain and a supercell with 256 water molecules. Numerical results for the large collection of water molecules show that two stage localization procedure can be more than 30 times faster than the original SCDM algorithm and compares favorably with the popular Wannier90 package.