Also there's been some people arguing about the particles in a box situation for a loooong time and the most promising approach currently is diffusion.
Inorganic crystal structure database (and there is one database literally is this name) is way smaller than what we have for proteins. Also by nature, Transformer is hardly useful for crystals because the crystal is repetitive. You don't throw the same sequence over and over to transformer and hope it will work like magic.
My current understanding is that Graph Neural Network is perfect for this job because graph can exactly describe this kind of repetitive nature of crystal.
It's hard enough to get out the hyperfine interaction to the right order of magnitude of a simple metal complex, let alone something like superconductivity, which fundamentally is a many, many body problem...
1. Computation cost is larger. 1 compute task for a small scale ~100 atoms last about 3 days to 1 week on supercomputer.
2. Search space is hugh. For each composition you can have different atomic (or crystal) structure. And here we are talking doping which means introduce impurities into the molecule. Chemical characteristics differs depending on which atom you swap for the impurity. Sometimes you may want to try all places.
3. Depends on initial values. Sometimes the initial value is just bad that the result is totally unusable, then you have tweak a little bit and throw back to supercomputer. This cycle might happen few times for 1 specific formula and structure.
4. Not 100% accurate. Often the resulting numbers are off by a few % or more which is hugh, compare to experimental results. Reason is that the simulation is not full scale, approximation is here and there to reduce computational cost.