COSMO Lab
@labcosmo.bsky.social
Computational Science and Modelling of materials and molecules at the atomic-scale, with machine learning.
A little holiday reading from 🧑🚀: arxiv.org/html/2607.26... , or how Michelangelo used #AI to break equivariant atom-centered descriptors all the way to 7 neighbors (and arbitrary many neighbors when considering a finite, practical level of discretization). 🤯
What will O2, benzene and ozone do on top of a Mendeleev cluster 🤔 ? Thanks to the improved mlip.js webtool from Peter Spackman you can figure it out running #PET-MAD-XS in your browser using webgpu 🚀 . Try it out at @crystalexplorer.net 's amazing MLIP.js peterspackman.github.io/mlip.cpp/
Time to learn the action 🏃 ! Today I'm sharing a paper, just published on @physrevlett.bsky.social, showing how to construct a symplectic #machinelearning predictor of classical mechanics by learning the Hamilton-Jacobi action.
Afternoon action at the @nccr-marvel.bsky.social / @ictp.bsky.social summer school, with the 🧑🚀 team introducing MD with #machinelearning potentials using PET, metatomic and the atomistic-cookbook.org 🧑🍳📖
📢 Chemiscope 1.0 paper is out on #JOSS. If you haven't used ⚗️ 🔭 recently, head to joss.theoj.org/papers/10.21... to read a summary of the new features, to chemiscope.org to try it out, and type `pip install chemiscope` to use it locally.
Chemiscope
Interactive data visualization for materials and molecular databases. Correlate atomic structures and their properties, either online, in a jupyter notebook, or with a portable app.
chemiscope.org
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.
📢 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 ☢️
New recipe just landed on the atomistic-cookbook.org 🧑🍳📖. Thanks to @yairlitman.bsky.social for explaining how to use ipi-code.org to perform ring-polymer instanton calculations of reaction rates that include quantum tunneling effects ⚛️⚡. Check it out 👉 atomistic-cookbook.org/examples/rin...
📢 New #preprint is out! Investigating the many flavors of last-layer #UQ, Moritz and 🧑🚀Matthias propose a practitioners' guide on "how to train a shallow ensemble". TL;DR? for good calibration use NLL, include force, and optimize the backbone, fine-tuning for speed! 📃🔗➡️ arxiv.org/html/2602.15...
How to Train a Shallow Ensemble
arxiv.org
Many #machinelearning potentials are built (or understood) in terms of "atomic cluster expansions" that link directly to a body-ordered energy decomposition that can be computed explicitly with a sequence of electronic structure calculations. But what kind of expansion do they learn in practice? A🧵
Hot off the press on hashtag @aip.bsky.social #JCP, an introduction to the metatensor ecosystem. High-quality 🧑🚀 tools for atomistic hashtag#machinelearning - read on pubs.aip.org/aip/jcp/arti... and check it out at metatensor.org 🧑🍳 📖 recipes here atomistic-cookbook.org/software/met...
metatensor and metatomic: Foundational libraries for interoperable atomistic machine learning
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce
pubs.aip.org
🧑🚀 Filippo, Arslan and Paolo doing some PET talk with our friends at the @epfl-ai-center.bsky.social ai.epfl.ch/a-new-refere...
A New Reference Model for Machine-Learning–Driven Materials Discovery - EPFL AI Center
Researchers at EPFL’s Laboratory of Computational Science and Modeling (COSMO) have reached a significant milestone in material science, reaching the top position on Matbench Discovery, the leading be...
ai.epfl.ch
If you want to learn about materials modeling, from DFT to MD, well marinated in a spicy ML sauce, don't miss out the @ictp.bsky.social / @nccr-marvel.bsky.social college. Details and application instructions here indico.ictp.it/event/11146/. See you in Miramare!
PET continues its victory round of benchmarks and challenges 🥇🥉. And this one has a (bit far-fetched) end goal that would also make it useful! Congrats to Filippo and Cesare (and @marceldotsci.bsky.social who got a honorable mention and will also try further his LOREM model)🚀 dtu.dk/english/news...
International AI competition aims to speed up the development of materials for the green transition
The Pioneer Center CAPeX at DTU has announced the winners of the first phase (Stage 1) an international competition in partnership with the Novo Nordisk Foundation, and the Danish Centre for AI Innovation (DCAI), on using machine learning models to predict synthesis recipes for novel nanoparticles.
dtu.dk
Congratulations to 🧑🚀 Sergey Pozdnyakov who very deservedly won the @materials-epfl.bsky.social doctoral distinction award. A good time to go check on his papers, if you haven't read them already!
Release candidate 3 of chemiscope 1.0 is out, with class and range based highlighting of points. Try it, break it, report it on github.com/lab-cosmo/ch...
Fantastic news from the @snf-fns.ch, who despite the budget cuts managed to fund six new NCCRs. Looking forward to doing some cool simulations to advance separation science! actu.epfl.ch/news/a-new-n...
A new national research programme recognizes EPFL's expertise
The Swiss Confederation launches six new National Centres of Competence in Research (NCCRs). The NCCR “Separations”, which aims to accelerate research in separation sciences - the quest for chemical a...
actu.epfl.ch
If you got curious by the PET-OAM results a week ago, you can learn more reading up arxiv.org/abs/2601.16195. Including some general considerations on how to train and use safely an unconstrained ML potential.
Not going to make a big deal out of a benchmark table, but PET just got the top spot on matbench-discovery.materialsproject.org. And don't be fooled by the huge parameters count, it's faster and can handle larger structures than eSEN-30M 🚀. Kudos to 🧑🚀 Filippo, Arslan and Paolo!
📢 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...
Hope y'all are getting a great start of 2026. Here we're taking some time to add the 2025 winter card to the archives www.epfl.ch/labs/cosmo/i... 🎅=🧑🚀
📢 New chemiscope.org release just landed! To make it even easier to integrate ⚗️🔭 into your workflow, we added a @streamlit.bsky.social component, so you can run analyses and show you atomistic data in a web app by just writing a few lines of python! try it, break it, report it!
Congrats to 🧑🚀 Sergey Pozdnyakov who received a distinction (best 8% of theses at @materials-epfl.bsky.social) for his PhD thesis "Advancing understanding and practical performance of machine learning interatomic potentials". Поїхали 🚀! infoscience.epfl.ch/entities/pub...
No day goes by without a new universal #ML potential. But how different they really are? Sanggyu and Sofiia tried to give a quantitative answer by comparing the reconstruction errors between their latent-space features. If you are curious, check out the #preprint arxiv.org/html/2512.05...
📢 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...
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...
nature.com
📢 Let us (re)introduce to you our Massive Atomic Diversity dataset for universal MLIPs. MAD includes molecules, clusters, surfaces and plenty of bulk configs, we cover a lot of ground with fewer than 100k structures, using highly consistent DFT settings. Read more 📑 www.nature.com/articles/s41...
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning - Scientific Data
Scientific Data - Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
nature.com
In this blog post, Filippo Bigi, Marcel Langer (@labcosmo.bsky.social) and @micheleceriotti.bsky.social write about the need to balance speed and physical laws when using ML for atomic-scale simulations aihub.org/2025/10/10/m...
Machine learning for atomic-scale simulations: balancing speed and physical laws - ΑΙhub
aihub.org
A primer for non conservative (& rotationally unconstrained) MLIPs, and how to use them safely. Thanks @aihub.org for the space! aihub.org/2025/10/10/m...
Machine learning for atomic-scale simulations: balancing speed and physical laws - ΑΙhub
aihub.org
Looks like @ox.ac.uk forbids their researchers to do any kind of literature search, though it seems that thankfully they can still submit to the arxiv arxiv.org/abs/2510.00027 🤷
Learning Inter-Atomic Potentials without Explicit Equivariance
Accurate and scalable machine-learned inter-atomic potentials (MLIPs) are essential for molecular simulations ranging from drug discovery to new material design. Current state-of-the-art models enforc...
arxiv.org
