The ab initio extension of the dynamical vertex approximation (DΓA) method allows for realistic materials calculations that include non-local correlations beyond GW and dynamical mean-field theory. Here, we discuss the AbinitioDΓA algorithm, its implementation and usage in detail, and make the program package available to the scientific community. Program summaryProgram Title: AbinitioDΓAProgram Files doi:http://dx.doi.org/10.17632/h3k3fg6szb.1Licensing provisions: GNU General Public License v3Programming language:Fortran95 and PythonRequired dependencies:MPI, LAPACK, BLAS, HDF5(≥1.8.12), Python(≥2.7), h5py(≥2.5.0), numpy(≥1.9.1)Optional dependencies:pip, matplotlib(≥1.5.1), scipy(≥0.14.0)Supplementary material: Test case files and step-by-step instructions.Nature of problem: Realistic materials calculations including non-local correlations beyond dynamical mean-field theory (DMFT) as well as non-local interactions. Solving the Bethe–Salpeter equation for multiple orbitals. Determining momentum-resolved susceptibilities in DMFT.Solution method:Ab initio dynamical vertex approximation: starting from the local two-particle vertex and constructing from it the local DMFT correlations, the GW diagrams, and further non-local correlations, e.g., spin fluctuations. Efficient solution of the Bethe–Salpeter equation, avoiding divergencies in the irreducible vertex in the particle-hole channel by reformulating the problem in terms of the full vertex. Parallelization with respect to the bosonic frequency and transferred momentum.Additional comments including restrictions and unusual features: As input, a Hamiltonian derived, e.g., from density functional theory and a DMFT solution thereof is needed including a local two-particle vertex calculated at DMFT self-consistency. Hitherto the AbinitioDΓA program package is restricted to SU(2) symmetric problems. A so-called λ correction or self-consistency is not yet implemented in the AbinitioDΓA code. Susceptibilities are so far only calculated within DMFT, not the dynamical vertex approximation.
Read full abstract