I've worked on the CS side of new material discovery.
The tricky thing is that you don't actually have that much data so ML is not even close to plug and play. If you want to get results you end up needing to pair ML with a lot of theory and some tricky algorithms to help narrow the search space and even then that space is huge.
Progress is being made but I think we're still at least 5-10 years from CS providing a real inflection in materials discovery.
The positive view here is that we are 5-10 years from being able to search for materials computationally using AI. That would potentially be more valuable than a single RTS.
They are doing this sort of thing (that's more of a research institute). The problem is that you are not looking for the compound but for the exact way to manufacture it assuming that the original sample really is superconducting.
The problem is that this stuff is severely nonlinear, and for the raw formula there are not that many degrees of freedom to try. If you get a structure, there is no guarantee it's stable, or accessible by our synthetic techniques.
Obtaining the training data is also likely to be tricky.
