3 ms·
Not an expert but it just happen that my lab is full of DFT folks so I heard a lot about those everyweek. As people above already answered the questions, I gonn
by akasakahakada 3y ago
Not an expert but it just happen that my lab is full of DFT folks so I heard a lot about those everyweek. As people above already answered the questions, I gonna talk some extras.
1. Computation cost is large. 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.
- fpordeig 3y agoThis looks similar to the protein folding problem. Maybe an AlphaFold-like approach could work?
- fock 3y agonot soo similar really, but yes, alphafold-style generative models could help find realistic structures for a specific composition. However a) data is much worse (I would say so at least...), b) the clever tricks of alphafold centered around strings of aminoacid don't really apply to particles in a box... and c) search space might be even larger if you go to interestingly sized systems. 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.
- akasakahakada 3y agoWe exactly considering this since this year. But there are some major problems that cannot be solved in short term. Inorganic crystal structure database (and there is one database literally 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.
- rsfern 3y agoGNNs and graph transformers are the current state of the art methods for this kind of crystal property prediction task. One drawback is that they don’t seem to capture long range structure all that well, and the current generative models (which are really cool) based on GNNs don’t seem to take advantage of symmetry that well
- ssijak 3y agoBased on your descriptions, if LK99 is real, it sounds like there are closer to infinite than 0 of these materials. If there are so many combinations and somebody randomly stumbled on one, it makes sense that if the number of combinations is asymptotically infinite, that number of superconducting materials is very large.
- azalemeth 3y agoThe other thing is that a lot of the results depend entirely on what you put into them. Sounds lovely and obvious, but really isn't. Which basis set would you use for simulating your molecular object of interest? There are hundreds to choose from, all developed for different purposes and under different assumptions (eg "frozen core", Born Oppenheimer, Slater orbitals, etc). It's very much not trivial to know what you need, because what the system does answers that question, and in order to find out what the system does you need to solve it -- for which you need a basis set! 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...