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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 12, 2021
- Source ID
- 10.1038/s41524-021-00517-5
Entities
People
- Chih-chuen Lin
- Phani Motamarri
- Vikram Gavini
Organizations
- Air Force Office of Scientific Research
- Army Research Office