Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations

Abstract We present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to t...

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Autores principales:	Chih-Chuen Lin, Phani Motamarri, Vikram Gavini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/1d1eb2237d6040f5bdc9802bc4be6a54
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id	oai:doaj.org-article:1d1eb2237d6040f5bdc9802bc4be6a54
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spelling	oai:doaj.org-article:1d1eb2237d6040f5bdc9802bc4be6a542021-12-02T14:25:20ZTensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations10.1038/s41524-021-00517-52057-3960https://doaj.org/article/1d1eb2237d6040f5bdc9802bc4be6a542021-04-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00517-5https://doaj.org/toc/2057-3960Abstract We present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L 1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.Chih-Chuen LinPhani MotamarriVikram GaviniNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Chih-Chuen Lin Phani Motamarri Vikram Gavini Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
description	Abstract We present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L 1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.
format	article
author	Chih-Chuen Lin Phani Motamarri Vikram Gavini
author_facet	Chih-Chuen Lin Phani Motamarri Vikram Gavini
author_sort	Chih-Chuen Lin
title	Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
title_short	Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
title_full	Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
title_fullStr	Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
title_full_unstemmed	Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations
title_sort	tensor-structured algorithm for reduced-order scaling large-scale kohn–sham density functional theory calculations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1d1eb2237d6040f5bdc9802bc4be6a54
work_keys_str_mv	AT chihchuenlin tensorstructuredalgorithmforreducedorderscalinglargescalekohnshamdensityfunctionaltheorycalculations AT phanimotamarri tensorstructuredalgorithmforreducedorderscalinglargescalekohnshamdensityfunctionaltheorycalculations AT vikramgavini tensorstructuredalgorithmforreducedorderscalinglargescalekohnshamdensityfunctionaltheorycalculations
_version_	1718391356368355328

Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations

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