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).
|
PDF
npj_com_mat_07-01_2021.pdf - Published Version Download (1MB) | Preview |
|
|
PDF
41524_2021_517_MOESM1_ESM.pdf - Published Supplemental Material Download (1MB) | Preview |
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 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 |
Actions (login required)
 |
View Item |
