Aja - Steely Dan

(7/N) That being said, I've been seeing a lot of efforts lately that try to add physically inspired long-range models to MP/Attention-based ML potentials and I'm exited about what's to come.

(6/N) Ergo, an ML model with finite cutoff cannot, by design, be accurate for both condensed-phase like systems and still have a physical MBE (and be reliable for dimers, trimers etc.), and hence is not really "universal".

(5/N) Focusing on 2 and 3-body energies, a short-range ML model will immediately break down (i.e. predict zero) for dimers/trimers separated by more than the model's cutoff radius, and hence the MBE of the model becomes non-physical.

(4/N) predict the interaction energy. This may be true, but in comes our trusty many-body expansion of the energy. Often used in QM calculations MBE states that the total (interaction) energy can be written as a sum of 1-body, 2-body, 3-body, etc. terms.

(3/N) We want to compute the interaction energy between this molecule and it's surrounding water. It is clear that long-range effects are important, especially for ionic solutes. Oftentimes the argument is made that screening takes care of this, and a short-ranged ML model can still accurately...

(2/N) I'm not gonna go into detail about I think why this particular dimer binding curve looks the way it does, as a lot of good arguments have been made in the thread(s). Thought experiment: Consider a molecule in a solvent (say, water).

Hi, can I be added please? Working on ML applied to compchem, in particular QM methods and MD simulations.