[mynameismon]

> In my own work on aluminium alloys I was able to get the same simulations that would have needed hours on the supercomputer to run in seconds on a laptop.

Could you elaborate on this further? How exactly were the simulations sped up? From what I could understand, were the ML models able to effectively approximate the Schrodinger's equation for larger systems?

[dmarchand90]

What you do is you compute a lot of simulations with the expensive method. Then you train using neural neural networks (well any regression method you like).

Then you can use the trained method on new arbitrary structures. If you've done everything right you get good, or good enough results, but much much faster.

At a high level It's the same pipeline as in all ML. But some aspects are different, e.g. unlike image recognition you can generate training data on the fly by running more DFT simulations

[fennecfoxy]

That's pretty cool! It seems like most of ML is just creating a higher dimensional representation of the problem space during training and then exploring that during inference.

I suppose your process would be using ML to get pointed in the "right direction" and then confirming the models theories using the expensive method?

[dmarchand90]

Yeah exactly like this. It is a subtle art of validating in small scale a method you would later use at large scale.