Learning how the DFT codes work

Question

I am a beginning PhD student studying topological materials and strongly-correlated systems, such as high Tc superconductivity. I am using density functional theory in my work. I am wondering what I need to know in order to master DFT calculations using vasp (and also QE). I currently have some level of understanding of the theoretical basis of DFT (the Kohn-Sham equations, the Hohenberg-Kohn theorem, the Levy-Lieb functional etc.), but I do not have a lot of understanding on the pseudo-potential formalism (e.g. PAW), and the algorithms used in VASP to calculate the electronic ground state (e.g. the blocked Davidson algorithm and the RMM-DIIS algorithms).

I am wondering if it pays to spend time trying to understand how the DFT code actually works and if so, what is the best way to do that. My principal goal is to do DFT calculations to understand topological systems and strongly-correlated material systems. This involves building tight-binding models etc.

Answer 1

You should start off with running DFT calculations and then over time you can learn about theoretical and implementation details that interest you or are important for what you are doing. Based on what you've said, you should have the requisite knowledge to understand what is going on initially; once you've done calculations, you will have a better sense for what additional theoretical topics you will need to cover.

Different people with different research aims will need to spend more or less time on different subtopics. For example, many people don't need to learn much more about PAW than having some vague understanding of the "PAW formalism" section of the VASP wiki, whereas other people will have to go deep into the details and even implement code that must take into account the PAW formalism.

If you will be working on tight binding models and DFT, then Wannier90 likely will end up being useful. Perhaps you could do some DFT tutorials in VASP or QE and then also follow some of the Wannier90 tutorials. Along the way, you'll probably run into questions or things that interest you that can help inform what topics you will spend more time working with.