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

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–S...

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Vydáno v:	npj computational materials Ročník 7; číslo 1; s. 1 - 9
Hlavní autoři:	Lin, Chih-Chuen, Motamarri, Phani, Gavini, Vikram
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 12.04.2021 Nature Publishing Group Nature Portfolio
Témata:	639/301/1034/1037 639/301/1034/1038 Algorithms Characterization and Evaluation of Materials Chemistry and Materials Science Computational Intelligence Density functional theory Localization Materials Science Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Scaling Tensors Theoretical
ISSN:	2057-3960, 2057-3960
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2057-3960 2057-3960
DOI:	10.1038/s41524-021-00517-5