RSPt is a code for electronic structure calculations and its acronym stands for Relativistic Spin Polarized toolkit. RSPt offers a robust and flexible set of tools to calculate total energies, magnetic moments, band structures, Fermi surfaces and densities of states for all elements, and combinations thereof, over a wide range of volumes and structures.
RSPt is based on the Full-Potential Linear Muffin-Tin Orbital (FP-LMTO) method, which allows for very small basis sets and fast calculations, without any restriction on the symmetry of the potential. RSPt accommodates multiple energy sets (i.e. valence and semi-core states) with the same angular quantum numbers but different principal quantum number, which is ideal to provide an accurate description of semi-core states. The code can be used for spin-polarized and/or spin-orbit calculations with several LDA and GGA functionals implemented, as well as up-to-date implementations of beyond DFT methods, such as SIC, DFT+U or DFT+DMFT.
- all-electron implementation of density functional theory (DFT)
- full-potential linear muffin-tin orbital method (FP-LMTO)
- relativistic effects plus spin-orbit coupling (SOC)
- collinear magnetic structures and fixed spin moment calculations
- inter-atomic exchange parameters Jij
- disorder included via virtual crystal approximation (VCA)
- versatile output: dos, bands, fat bands, Fermi surfaces, charge densities...
- three levels of MPI parallelization: inter-k, intra-k and Fourier mesh
- good scalability up to thousands cores
- full self-consistence over self-energy and electron density
- two sets of local orbitals
- Spin-Polarized T-matrix Fluctuation-exchange (SPTF) solver
- Hubbard I approximation
- Exact diagonalization (ED) solver
- Generalized tensor moment decompositions
- self-interaction correction (SIC)
RSPt can be applied to systems including up to 100-150 atoms and can exploit an efficient parallelization up to thousands cores. No material library is needed as all input data is created on the fly. For these reasons, RSPt has also been used to generate the Gurka database of electronic structures, which can be used for the analysis of big data.