GitHub - lab-cosmo/upet: Universal interatomic potentials for advanced materials modeling Universal interatomic potentials for advanced materials modeling - lab-cosmo/upet

Now, this is just a milestone on that path, but it's already something worth sharing, so thanks to arXiv you can read about it arxiv.org/html/2603.02..., and thanks to uPET github.com/lab-cosmo/up... and metatomic, you can try already a universal MLIP trained on it.

📢 We have been working on a new universal atomistic dataset that combines the principles of MAD with a meta-GGA level of theory, so we can all simulate water that does not freeze at 500K 🧊 , and have all our bases covered, with reference data for every isotope with a half-life above 24 hours ☢️

So let us show you just how *universal* #PET-MAD-1.5 can be. This is a movie of a parallel tempering simulation, with replicas from 300K to 3000K, of what we call a "Mendeleev cluster" - one atom each of every element from 1 to 102.

No Install. No Setup. Just Chemical Shift Predictions.
At shiftml.materialscloud.io we host the latest ShiftML3 in the web and you can predict chemical shifts of organic crystals for free in the web!

Zooming in on a large-scale dataset to showcase the new adaptive resolution features in chemiscope

📢 chemiscope.org 1.0.0rc1 just dropped on pypi! We are making (a few) breaking changes to the interfaces, fixing a ton of bugs and introducing some exciting features (you can finally load datasets with > 100k points!). We'd be grateful if you test, break and report 🐛 github.com/lab-cosmo/ch...

ggml : add WebGPU backend · Issue #7773 · ggml-org/llama.cpp I hope that this would be relatively easy to do since AFAIK WebGPU allows us to write kernels in a shader language, so we have experience how to create such backends. There has been some initial wo...

There seems to be some initial work for webGPU with ggml (github.com/ggml-org/lla...), but it looks very recent and not yet usable. Looking forward to it!

Very nice! Is this able to use WebGPU when running the model?

Also, I guess this is limited to 4GiB of memory? Although it might not matter too much, since it would become very slow for systems that require that kind of memory anyway

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling - Nature Communications PET-MAD is a fast and lightweight universal machine-learning potential, trained on a small but diverse dataset, that delivers near-quantum accuracy in atomistic simulations for both organic and inorga...

📢 PET-MAD is here! 📢 It has been for a while for those who read the #arXiv, but now you get it preciously 💸 typeset by @natcomms.nature.com Take home: unconstrained architecture + good train set choices give you fast, accurate and stable universal MLIP that just works™️ www.nature.com/articles/s41...

GitHub - lab-cosmo/shiftml: A python package for the prediction of chemical shieldings of organic solids and beyond. A python package for the prediction of chemical shieldings of organic solids and beyond. - lab-cosmo/shiftml

We're introducing ShiftML3, a new ShiftML model for chemical shielding predictions in organic solids.

* ShiftML3 predicts full chemical shielding tensors
* DFT accuracy for 1H, 13C, and 15N
* ASE integration
* GPU integration

Code: github.com/lab-cosmo/Sh...
Install from Pypi: pip install shiftml

If you are using or you are considering using CP2K, check this out!

Very proud to send Filippo Bigi to Vancouver to give an oral presentation at @icmlconf.bsky.social about our investigation of the use of "dark-side forces" in atomistic simulations. The final version is here openreview.net/forum?id=OEl... and it's worth a read even if you already read the #preprint

🎉 DFT-accurate, with built-in uncertainty quantification, providing chemical shielding anisotropy - ShiftML3.0 has it all! Building on a successful @nccr-marvel.bsky.social-funded collaboration with LRM🧲⚛️, it just landed on the arXiv arxiv.org/html/2506.13... and on pypi pypi.org/project/shif...

The link above is missing a password, pleas use epfl.zoom.us/j/6836877674... instead!

We wouldn't be @labcosmo.bsky.social if we didn't want you to go break it, so head to atomistic-cookbook.org/examples/fla... for a crash-course 🧑‍🍳📖 recipe, but not before reading the warnings arxiv.org/html/2505.19.... Have fun! #compchem #machinelearning #md @nccr-marvel.bsky.social @erc.europa.eu

If you do, the rewards can be very impressive: you can run solvated alanine dipeptide and observe superionic behavior in LiPS with 16fs time step, and watch the Al(110) surface pre-melt in strides of 64fs. And all with the same universal model, no fine-tuning needed!

Darth Vader dressed as Santa presents a non-conservative forcefield

Much as for direct force prediction [ arxiv.org/html/2412.11... ] you better know what you are doing: you've no guarantee of energy conservation, or of equipartition, so you should know your thermostats VERY well. Caveat emptor.

Filippo's idea was to use the heavy-duty PET-MAD model [ arxiv.org/html/2503.14... ] to generate a bunch of trajectories of wildly different compounds and use a PET-like architecture to learn (q',p') from (q,p), and and to think A LOT about the many things that could possibly go wrong.

Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of the integrator need ...

There are reports as early as 2021 [cf. arxiv.org/abs/2111.15176 ] of using neural nets to predict a MD trajectory in large strides, but these were usually limited to a single system in a given thermodynamic state point. Nice, but not life-changing.

Scheme of the GNN architecture of the FlashMD method.

📢 Running molecular dynamics with time steps up to 64fs for any atomistic system, from Al(110) to Ala2? Thanks to 🧑‍🚀 Filippo Bigi and Sanggyu Chong, with some help from Agustinus Kristiadis, this is not as crazy as it sounds. Let us briefly introduce FlashMD⚡ arxiv.org/html/2505.19...

If you don't want to read, but want to cook, guess what? The 🧑‍🍳📖 #atomistic-cookbook has you covered. Head to atomistic-cookbook.org/examples/lea... to see how to use this, as simple as `mtt train mymodel.yaml`.

If you don't have time for the 20-pages appendix, the TL;DR is that approximating tensors is harder than the vector case, but can be made as simple as possible using angular momentum theory. The practical implementation we propose is not as rigorous, but works well and is very fast in practice.

Schematic representation of the lambda-MCoV architecture for predicting tensorial quantities using scalar approximators

First #preprint from recent 🧑‍🚀 Michelangelo Domina (+ @ppegolo.bsky.social and Filippo) provides theoretical foundations and practical architectures to build scalar-function-based approximations of tensors, in the spirit of the "scalars are universals" paper #compchem arxiv.org/html/2505.05...

You can use these safely in MD, by multiple time stepping (a '90s classic: doi.org/10.1063/1.46...) so you get reliable conservative trajectories, for any material, twice as fast! Let us know if it works for you, and even more importantly, if it doesn't! 👉 atomistic-cookbook.org/examples/pet...

Plot of the potential and total energy trends along a MD trajectory, showing drift for non-conservative forces, and how that is fixed using multiple time stepping.

The PET-MAD universal forcefield mingled with the dark side, and got twice as fast 🚀. Read on, or head to the 🧑‍🍳📖 atomistic-cookbook.org/examples/pet..., if you are curious of what this is all about. #atomistic-cookbook #compchem #machinelearning #mlip🧵

Fast and flexible long-range models for atomistic machine learning Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This lea

📢 For those who missed the #preprint, torch-pme is now published in @aip.bsky.social #JChemPhys. #compchem classics like Ewald and P3M meet #machine learning. Read all about it here pubs.aip.org/aip/jcp/arti...

Header for the atomistic-cookbook.org recipe for the PET-MAD universal potential

You can read all about it here, arxiv.org/abs/2503.14118, see it in action as a #cookbook recipe 🧑‍🍳 atomistic-cookbook.org/examples/pet... or rush to install it following the instructions here github.com/lab-cosmo/pe...

Polar plot showing the errors of several machine-learning potential of different test sets. Smaller is better here!

Plots showing the evaluation time per atom for several machine-learning potentials as a function of the number of atoms in a simulation. Smaller is better

📢 PET-MAD has just landed! 📢 What if I told you that you can match & improve the accuracy of other "universal" #machinelearning potentials training on fewer than 100k atomic structures? And be *faster* with an unconstrained architecture that is conservative with tiny symmetry breaking? Sounds like 🧑‍🚀

Should be super-easy to adapt to whatever tensor you're trying to learn (and to extend to more sophisticated models than symmetry-adapted regression). Let us know what you cook with it 😋!

In particular, this recipe relies on metatensor.github.io/featomic to compute the features, and the docs.metatensor.org/latest/torch... backend of #metatensor to export a self-contained ASE-compatible calculator. Easy to use, fast, and accurate.

Polarizability of a small molecule, represented as an ellipsoid

🧑‍🍳 A new #cookbook recipe to train, export and use a simple but effective linear model for tensorial properties - specifically molecular polarizabilities
atomistic-cookbook.org/examples/pol...
Thanks @ppegolo.bsky.social for the recipe, and the whole 🧑‍🚀 team for the underlying infrastructure.