I think it's possible, it'd just take serious computing power. There are a number of physically guaranteed symmetries which would be silly to make a machine lea

I think it's possible, it'd just take serious computing power. There are a number of physically guaranteed symmetries which would be silly to make a machine learn: a) Permutation symmetry of identical atoms b) Rotational symmetry of system c) Translational symmetry of system I suppose it could learn them approximately, given enough examples, but why bother? I think it'd be kind of like not using a convolutional neural net for recognising digits in photos and just using a bazillion more weights and examples. I'd say you'd start with a completely general learning model which respects the above symmetries and then see where it takes it. However, I don't know how you'd make a transferable N-body potential from a model taught only on some number of atoms. Again, I guess it's kind of like training a CNN handwriting recogniser on 256x256 images and then applying it to arbitrary sized images, which you can only do by assuming locality and translational symmetry of the features.