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

Lin, C-C and Motamarri, P and Gavini, V (2021) Tensor-structured algorithm for reduced-order scaling large-scale Kohn�Sham density functional theory calculations. In: npj Computational Materials, 7 (1).

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Official URL: https://doi.org/10.1038/s41524-021-00517-5


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 L1 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. © 2021, The Author(s).

Item Type: Journal Article
Publication: npj Computational Materials
Publisher: Nature Research
Additional Information: The copyright for this article belongs to Authors
Keywords: Density functional theory; Ground state; Structured programming; Tensors, Exponential convergence; Ground-state energies; Localization technique; Numerical results; Reduced order; Separable approximation; System size; Tensor basis, Hamiltonians
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 16 Jul 2021 08:06
Last Modified: 16 Jul 2021 08:06
URI: http://eprints.iisc.ac.in/id/eprint/68763

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