The SIESTA method for ab initio order-N materials simulation
Jose M. Soler, Emilio Artacho, Julian D. Gale, Alberto Garcia, Javier Junquera, Pablo Ordejon, Daniel Sanchez-Portal
TL;DR
The paper presents Siesta, an ab initio DFT method designed for order-N scaling in large materials systems by combining a flexible localized LCAO basis with a real-space grid, and by recasting the Kohn-Sham problem using a localization-based energy functional and Wannier-like states. It provides full treatment of norm-conserving pseudopotentials (KB form with NLCC), multiple-zeta basis sets (SZ/DZP), and grid-based evaluation of Hartree and XC potentials, enabling efficient two-center and grid integrals and linear-scaling density updates. The implementation supports non-collinear spin, Brillouin-zone sampling via an auxiliary supercell approach, and optional finite-temperature occupations, while offering extensive features for structural relaxation, MD, phonon analysis, transport, and TDDFT. Collectively, Siesta delivers accurate, scalable first-principles simulations for thousands of atoms, with demonstrated convergence and computational efficiency relative to plane-wave approaches.
Abstract
We have developed and implemented a self-consistent density functional method using standard norm-conserving pseudopotentials and a flexible, numerical LCAO basis set, which includes multiple-zeta and polarization orbitals. Exchange and correlation are treated with the local spin density or generalized gradient approximations. The basis functions and the electron density are projected on a real-space grid, in order to calculate the Hartree and exchange-correlation potentials and matrix elements, with a number of operations that scales linearly with the size of the system. We use a modified energy functional, whose minimization produces orthogonal wavefunctions and the same energy and density as the Kohn-Sham energy functional, without the need of an explicit orthogonalization. Additionally, using localized Wannier-like electron wavefunctions allows the computation time and memory, required to minimize the energy, to also scale linearly with the size of the system. Forces and stresses are also calculated efficiently and accurately, thus allowing structural relaxation and molecular dynamics simulations.
