📢 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...

📢 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...

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...

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!

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.

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.

📢 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.

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...

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🧵

📢 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...

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...

📢 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.

🧑‍🍳 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.

Happy to share a new #cookbook recipe that shocases several new software developments in the lab, using the good ole' QTIP4P/f water model as an example. atomistic-cookbook.org/examples/wat.... TL;DR - you can now build torch-based interatomic potentials, export them and use them wherever you like!

No, Overleaf, I don't want to use "AI" No, Outlook, I don't want to use "AI" No, Slack, I don't want to use "AI" No, DuckDuckGo, I don't want to use "AI" No, Samsung, I don't want to use "AI" No, my *fucking oven*, I don't want to use "AI"

@marceldotsci.bsky.social getting into very dangerous territory.

Interesting to note, packaging was by far harder than the actual implementation. Kudos to 🧑‍🚀 Filippo, @luthaf.bsky.social, and @cscsch.bsky.social Nick Browning for the perseverance!

Maybe the biggest problem with LLMs as expert systems is that they can't say how confident they are. I'd be a lot more OK with LLMs sometimes giving incorrect answers if they didn't deliver those wrong answers as though they were certain.

Very happy to announce that github.com/lab-cosmo/sp... has reached the 1.0 release milestone. Most notably, you can now `pip install sphericart[torch,jax]` and get it installed without compilation, complete with CUDA support where available. Spherical (& solid) harmonics made fast and easy!

Happy new year everyone! After more than three years of work, and splitting out a whole separate package, I am extremely happy to announce the first full public release of featomic, a package to compute representations for atomistic machine learning! github.com/metatensor/f... 1/n

Hello! I'm posting this both here and on the X-rated site, let's see where it gets more re-posts 😇. We are looking for a research software engineer to help us develop (even) better code for #compchem #atomicscale #machinelearning. Check out the specs and apply! www.epfl.ch/labs/cosmo/i...

I'm getting into the habit of announcing new code releases! So today's release is version 0.3.0 of vesin (luthaf.fr/vesin/), a small library to compute neighbor/pair lists for atomistic systems! This releases adds an option to sort the pairs and better integration with metatensor atomistic models.