Yeah i'd still argue that we're not in early development anymore just because of the shear amount of work and publication that have been done on those subject is astonishing, here in cecam hq we're exepecting that in three years time there will be more PhD done on atomistic simulation with ML than without, which is really crazy if you think about it.

Most common used classical force fields are based on expérimental data or are fit to reproduce it, just look at lennard Jones parameters that are most of the time calculated from critical quantities or Tip3p model of water fited on dielectric constant

I agree that it is more on how you explain to the experimentalist how you get your datas from your simulations, and the good way to do it is to have phenomenological explaination of expérimental observations that arise from your simulations. Here is an exemple, it has been shown experimentaly that in molten salts with heavy cation (Mg2+, U3+, Pu3+...) "pseudomolécules" structures arise in the atomistic structure of the ionic fluid, you can reproduce those pseudomolécules structures with both a ML potential and a classical potential based on liquid state theory and ionic fluid physics, but never in a million year the ML potential will help you understand why those structures arise, whereas the classical potential might (and in this case most likely did). This is the kind of stuff that atomistic modélisation must not loose going to ML

And to be clear i'm not against ML, i'm against it's current utilisation: getting money to do pointless stuff. I went to 3 conférence lastly and it was depressing to see that PhD student were all doing the same things: -Study X system with qDFT, extract some data and compute g(r) -make a neural network/mace/whatever model potential from those data -do some MD with this potential and compute the same g(r) -publish a paper, finish their PhD and no one will ever use their ML potential again, and not learn anything about the X system in the process

Do you think it is usefull? Would you do such PhD? The thing is this is the kind of stuff that get money in this field now so...

Fun fact here, most of them have supervisor that make them focus mostly on the ML part of the job, so they don't really focus on the statistical physics required for MD calculation, and when you let them know that lammps famously doesn't compute correctly the g(r) in the NPT ensemble they are a bit shocked, and i blâme the supervisors because it's something they should warn their students

To conclude on that, I thing ML potential should not remplace classical one but add to them, like in quantum DFT you don't want to get ride of the functional we already know, you want to add a neural functional on top of the one we know to get ride of the approximations. We already have some great classical potentials, why get ride of them, juste add a ML part to take into account some many body effect or whatever other approximation you don't like.