Beyond-DFT database of spectral function for correlated materials

Recent trends in condensed matter physics greatly rely on the database and data science-driven materials discovery. The existing materials databases, constructed in the spirit of the materials genome initiative, are built almost exclusively by DFT engines and are very often making incorrect predictions for many correlated materials. As for qualitative predictions of excited-state properties usually require beyond-DFT methods, various methods going beyond DFT, such as meta-GGAs, hybrid functionals, GW, & DMFT have been developed to describe the electronic structure of correlated materials, but it is unclear how accurate these methods can be expected to be when applied to a given material. It is thus of pressing interest to compare their accuracy as they apply to different categories of materials, and at the same time, to build up a database for beyond-DFT methods [1]. We discuss a systematic study of these methods on various training sets of moderately and strongly correlated materials starting from elemental metallic systems to Fe-pnictides and chalcogenides, and various perovskites and compare with experimental photoemission data where available.

1. https://jarvis.nist.gov/jarvisbdft/; S. Mandal et al. npj. Comput. Mater. 5, 115 (2019).