A hierarchic sparse matrix data structure for large-scale Hartree-Fock/Kohn-Sham calculations
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Updated: Jul 14, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
Emanuel H Rubensson1, Elias Rudberg, Paweł Sałek
1Department of Physics and Chemistry, University of Southern Denmark, Odense M, Denmark. emanuel@theochem.kth.se
Abstract:
A hierarchic sparse matrix data structure for Hartree-Fock/Kohn-Sham calculations is presented. The data structure makes the implementation of matrix manipulations needed for large systems faster, easier, and more maintainable without loss of performance. Algorithms for symmetric matrix square and inverse Cholesky decomposition within the hierarchic framework are also described. The presented data structure is general; in addition to its use in Hartree-Fock/Kohn-Sham calculations, it may also be used in other research areas where matrices with similar properties are encountered. The applicability of the data structure to ab initio calculations is shown with help of benchmarks on water droplets and graphene nanoribbons.
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