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Cited by 166

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Linear Scaling Self-Consistent Field Calculations with Millions of Atoms in the Condensed Phase

Open access
VandeVondele, Joost, Borštnik, Urban, Hutter, Jürg·2012·Journal of Chemical Theory and Computation

Abstract

In this work, the applicability and performance of a linear scaling algorithm is investigated for three-dimensional condensed phase systems. A simple but robust approach based on the matrix sign function is employed together with a thresholding matrix multiplication that does not require a prescribed sparsity pattern. Semiempirical methods and density functional theory have been tested. We demonstrate that self-consistent calculations with 1 million atoms are feasible for simple systems. With this approach, the computational cost of the calculation depends strongly on basis set quality. In the current implementation, high quality calculations for dense systems are limited to a few hundred thousand atoms. We report on the sparsities of the involved matrices as obtained at convergence and for intermediate iterations. We investigate how determining the chemical potential impacts the computational cost for very large systems.

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Metadata

Authors
VandeVondele, J., Borštnik, U., & Hutter, J.
Venue
Journal of Chemical Theory and Computation
Published
2012-03-14
Volume
8
Issue
10
Pages
3565-3573
ISSN
  • 1549-9618 (ISSN-L)
  • 1549-9626
OpenAlex
W2332437841
Cited by
166
References
1
FWCI
6.65
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Linear Scaling Self-Consistent Field Calculations with Millions of Atoms in the Condensed Phase. https://app.cochat.ai/papers/10.1021/ct200897x
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VandeVondele, J., Borštnik, U., & Hutter, J. (2012). Linear Scaling Self-Consistent Field Calculations with Millions of Atoms in the Condensed Phase. Journal of Chemical Theory and Computation. https://doi.org/10.1021/ct200897x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.