You can fetch the model here github.com/lab-cosmo/upet, as easy as `pip install upet`, and then, for the ASE interface, `from upet.calculator import UPETCalculator;
calculator = UPETCalculator(model="pet-oam-xl", version="1.0.0", device="cuda")` Have fun and go break it!

Ah, a fine example of a `for regressor in sklearn.supervised_learning:` paper. It's an underappreciated genre.

Oh. My. Gawd. ☝️☝️☝️☝️ 👀

Developed in collaboration with the THEOS group with support from the @nccr-marvel.bsky.social, and stored on the #materialscloud here archive.materialscloud.org/records/c4en..., you can also take a look at the data as a #chemiscope 🔭⚛️ chemiscope.materialscloud.io?load=https%3...

However, this seems to damage the transferability of highly-preconditioned models such as MACE - less so for more expressive unconstrained models such as PET. Does this match your experience?

This doesn't matter much as most of the fragments that make up the body-order decomposition as deranged soups of highly-correlated electrons. Models with sufficient expressive power *can* learn if presented with the fragments ...

TL;DR: not really. ML potentials learn whatever they want, as long as it allows them good accuracy on the train set. We note in particular that MACE is strongly preconditioned to learn a fast-decaying body-order expansion, whether it decays fast or not.

With funding from a @snf-fns.ch Sinergia, the @nccr-marvel.bsky.social and @erc.europa.eu, and computing time from @cscsch.bsky.social !

The reconstructed surface contains different sites with different reactivity. Despite the higher stability, for some sites the disordered surface is *more* reactive with water, one of the main contaminants affecting the stability of LPS batteries. Useful to design better stabilization strategies!