It seems to my own naive self that if LK99 is the real deal, we mostly just got lucky finding it.
Obtaining the training data is also likely to be tricky.
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 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.
Once you know the atomic positions you can then do little perturbation simulations to model phonon dispersions or ask electron density questions.
Linear scaling DFT is something way more impactful than room-temperature superconductors. What's next? "Hey, I've used my FTL spaceship to verify the material at those friendly alien's library"?
The fact that there has been no Nobel prize and we didn't spend a week around the web arguing "yes, it works!", "no, didn't work for me", "yes, I verified it!" highly implies that the site is trying to say something different than what we are understanding.
https://www.3ds.com/fileadmin/PRODUCTS-SERVICES/BIOVIA/PDF/b...
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.
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...
People vastly over-estimate what we can simulate at any level of fidelity with any scale below purpose-built stuff running on supercomputers..
If we have the ability to computationally determine these things without any experimental data needed, and we know we're looking for a specific band structure, wouldn't we just do an automated search of possible chemistries to find everything producing said band structure?
Then just whittle down that list to the easiest to produce and most common materials for the first to test... what am I missing?
Google is doing XC-functionals and Alphafold. Facebook catalysts.
Isn't this a plot point in that one Star Trek movie (episode?) where they go back in time and program a current-day computer to do this?
That would also be a fantastic way to pull a hoax, because it will result in 10x more effort spent on your hoax. If it turns out that it was a hoax I think the original researchers will find immense gratitude from Pons and Fleischmann for taking over the top spot for the textbook example of bad science. But for now, as far as I can see the jury is still out, and if anything the paper linked here actually improves the chances of it being real a bit more than it is offset in the other direction by a failed replication attempt.
Edit: s/isomer/polymorph
It is the formation that is different not the molecules. C = C. They are technically allotropes:
https://en.wikipedia.org/wiki/Allotropy
And just like here that's a function of how the bulk carbon got to be formed, under extreme pressure and temperatures or less pressure and temperature. It's the recipe that makes the difference, not the ingredients.
isomer
/'aɪsəmər/
noun
a compound that exists in forms having different arrangements of atoms but the same molecular weight
That raises a question, what's the name for different crystalline structures of the same compound? Is that still an isomer or something different? I'm out of my element here.
So just for an example (I have no idea how to put a tetrahedron in a comment :) ) a chain of C-C-C-C and C-C=C-C (which you likely can not synthesize) would be an isomer but C-C-C-C in one crystal lattice versus C-C-C-C in another crystal lattice would be allotropes.
The carbon in graphite sits in sheets (hence the possibility to form graphene), whereas the carbon in a diamond always forms three dimensional lattices.
edit nah: bad example, sorry, I can't find a good way to visualize this in text the 2nd C-C-C-C chain should have the last C dangling from the 2nd down.
edit2: examples are wrong, I should have used a more complex molecule for the isomer and the carbon can have either three or four other carbons hanging of it for the allotrope. tricky...
allotrope
/'ælə,troʊp/
noun
a structurally different form of an element
https://www.differencebetween.com/difference-between-allotro...
You're looking at the word "polymorph": "(chemistry, geology) Any substance or mineral that forms different types of crystal."
They found a new phenomenon, that took years to explain. They didn't do anything bad.
Semiconductor yield at high end fabs can be lower than 20%. If you tried that process at a new fab and wound up with a complete failure, would you say "well that process just doesn't produce any semiconductors" or would you think you might need to refine the implementation of the process.
/me skeptical either way
Is the solid state theory space similarly underdefined that you can find a theory for every result? Or is this paper significant?
What this means for the more practical minded is that the synthesis of superconducting LK-99 is not trivial and you need to make the appropriate substitutional alloy for this to work.
This is a DFT paper, and a band structure that is usually seen in high Tc superconductors just naturally came out. She also talks about the strong electron-phonon coupling that naturally arose from the structure, which is always necessary for superconductivity.
I am, by far, the most excited I've ever been about this being a RT, ambient pressure superconductor.