1. Stochastic Optimally Tuned Range-Separated Hybrid Density Functional Theory
- Author
-
Daniel Neuhauser, Roi Baer, Eran Rabani, and Yael Cytter
- Subjects
Density matrix ,Orbital-free density functional theory ,FOS: Physical sciences ,Kohn–Sham equations ,01 natural sciences ,symbols.namesake ,Physics - Chemical Physics ,Quantum mechanics ,Mesoscale and Nanoscale Physics (cond-mat.mes-hall) ,0103 physical sciences ,Statistical physics ,Physical and Theoretical Chemistry ,010306 general physics ,Chemical Physics (physics.chem-ph) ,Condensed Matter - Materials Science ,Condensed Matter - Mesoscale and Nanoscale Physics ,010304 chemical physics ,Chemistry ,Materials Science (cond-mat.mtrl-sci) ,Computational Physics (physics.comp-ph) ,Hybrid functional ,symbols ,Quasiparticle ,Density functional theory ,Exchange operator ,Hamiltonian (quantum mechanics) ,Physics - Computational Physics - Abstract
We develop a stochastic formulation of the optimally-tuned range-separated hybrid density functional theory which enables significant reduction of the computational effort and scaling of the non-local exchange operator at the price of introducing a controllable statistical error. Our method is based on stochastic representations of the Coulomb convolution integral and of the generalized Kohn-Sham density matrix. The computational cost of the approach is similar to that of usual Kohn-Sham density functional theory, yet it provides much more accurate description of the quasiparticle energies for the frontier orbitals. This is illustrated for a series of silicon nanocrystals up to sizes exceeding 3000 electrons. Comparison with the stochastic GW many-body perturbation technique indicates excellent agreement for the fundamental band gap energies, good agreement for the band-edge quasiparticle excitations, and very low statistical errors in the total energy for large systems. The present approach has a major advantage over one-shot GW by providing a self-consistent Hamiltonian which is central for additional post-processing, for example in the stochastic Bethe-Salpeter approach., 7 pages, 3 figures
- Published
- 2015