3 ms·

You have to put in the structure and then it's expensive to do the calculation. The space of possible structures is extremely large. If you have candidates then

by ivalm 3y ago

You have to put in the structure and then it's expensive to do the calculation. The space of possible structures is extremely large. If you have candidates then you can run through them, but you can't just random search through trillions of trillions of candidates.

m463 3y ago
yet.
- jacquesm 3y ago
  And then you have to synthesize your candidates to ensure that what you think will happen really happens.
KRAKRISMOTT 3y ago
What are all the material science ML companies doing? This would be a perfect technology demo if they can find the target
- jacquesm 3y ago
  https://www.mpg.de/20096180/artificial-intelligence-in-material-design https://www.mpg.de/20096180/artificial-intelligence-in-mater... 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.
- throwawaymaths 3y ago
  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.
- Hermitian909 3y ago
  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.
  - doingtheiroming 3y ago
    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.

by ivalm 3y ago

m463 3y ago

yet.

jacquesm 3y ago
And then you have to synthesize your candidates to ensure that what you think will happen really happens.

KRAKRISMOTT 3y ago

What are all the material science ML companies doing? This would be a perfect technology demo if they can find the target

jacquesm 3y ago
https://www.mpg.de/20096180/artificial-intelligence-in-material-design https://www.mpg.de/20096180/artificial-intelligence-in-mater... 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.
throwawaymaths 3y ago
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.
Hermitian909 3y ago
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.
- doingtheiroming 3y ago
  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.