COSMO Lab

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...

Features reconstruction errors between the latent spaces of several universal MLIPs

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...

Oh. My. Gawd. ☝️☝️☝️☝️ 👀

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...

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...

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

📢 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...

Machine learning for atomic-scale simulations: balancing speed and physical laws - ΑΙhub

‪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

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...

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...

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 🤷

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.

📝 We have been told (& been telling) that ML potentials are linked quite directly to the expansion of the atomic energy into pairs, triples, and so on. But is this actually true 🤔? Go read the latest from the 🧑‍🚀 team (w/QM help from Joonho's team at Harvard) to find out more arxiv.org/html/2509.14...

GitHub - lab-cosmo/flashmd: A universal ML model to predict molecular dynamics trajectories with long time steps A universal ML model to predict molecular dynamics trajectories with long time steps - lab-cosmo/flashmd

Bragging time - ⚡ FlashMD⚡ was accepted as a spotlight paper at #NeurIPS25. if you still haven't checked it out, it's already on the #arxiv arxiv.org/abs/2505.19350, the code is at flashmd.org and the 🧑‍🍳📖 is here atomistic-cookbook.org/examples/fla.... Congrats to Filippo, Sanggyu and Augustinus!

Michele Parrinello giving the ICTP Colloquium (he speaks about catalysis) as part of the conference celebrating his 80th birthday. Amazing creativity throughout a long career!

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. Th...

I'm very pleased to say my first preprint, with @graemeday.bsky.social and @micheleceriotti.bsky.social is now online!

This is the main work of my PhD, adapting a similarity kernel to be more suited for exploring molecular CSP landscapes

#compchemsky #chemsky #compchem

doi.org/10.26434/che...

error plots for the PET-MAD-DOS model on different datasets

Anticipating 🧑‍🚀 Wei Bin's talk at #psik2025 (noon@roomA), 📢 a new #preprint using PET and the MAD dataset to train a universal #ml model for the density of states, giving band gaps for solids, clusters, surfaces and molecules with MAE ~200meV. Go to the talk, or check out arxiv.org/html/2508.17...!

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

Reaction energies of pristine and reconstructed surfaces with water

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!

Surface energy diagram of LPS before and after reconstruction

Wulff shape of LPS particles based on the computed surface energies

Reconstructed surfaces become lower in energy, and the surface energy less orientation dependent - and so the Wulff shape of particles become more spherical.

A cartoon explaining how mild finite-temperature conditions induce disorder and dynamical reconstruction on the surfaces of lithium thiophosphates

📢 Now out on @physrevx.bsky.social energy, journals.aps.org/prxenergy/ab... from 🧑‍🚀 @dtisi.bsky.social and Hanna Türk, our #PET -powered study of the dynamic reconstruction of LPS surfaces, and how it affects their structure, stability and reactivity.

If you are at the #psik2025 and want to know more about the #metatensor ecosystem, don't miss @luthaf.bsky.social talk tomorrow morning 9:45 in room 1

Too many 👩‍🚀 and 👨‍🚀 are involved to list them all, but go check the contributors on GH. And this is a good time to thank the @nccr-marvel.bsky.social, @erc.europa.eu and the @snf-fns.ch which have given us the funding to dedicate to these #openscience efforts that don't make papers, but make science!

You can then load it and run from any software implementing an interface - lammps.org, ipi-code.org, ase-lib.org, plumed.org, theory.cm.utexas.edu/eon, radical-ai.github.io/torch-sim, chemiscope.org already do, and we're happy to help if you want an interface in your code.

TL;DR - this is a cross-platform, model-agnostic library to handle atomistic data (handling geometry and property derivatives such as forces and stresses) that lets you package your model into a portable torchscript file.

metatensor logo

metatomic logo

🚨 #machinelearning for #compchem goodies from our 🧑‍🚀 team incoming! After years of work it's time to share. Go check arxiv.org/abs/2508.15704 and/or metatensor.org to learn about #metatensor and #metatomic. What they are, what they do, why you should use them for all of your atomistic ML projects 🔍.

A metatensor.org sticker on top of the mattehorn

Go metatensor.org!

GitHub - lab-cosmo/flashmd at symplectic A universal ML model to predict molecular dynamics trajectories with long time steps - GitHub - lab-cosmo/flashmd at symplectic

You can also try it out (if you like living on the edge, it's still in a branch github.com/lab-cosmo/fl...

Long-time relaxation of the potential energy in glassy GeTe

We can get long-stride geometry-conserving integration by learning the Hamilton-Jacobi action. This fixes for good, doesn't just patch up, the instability of direct MD prediction, although it's not as fast. And work also for serious simulations, like glassy relaxation in deep supercooled GeTe!

Orbits for a periodic 3-body system, showing the stability of a ML long-time integrator

If you are excited about 30x longer time steps in molecular dynamics using FlashMD, but are worried about it not being symplectic, Filippo has something new cooking that should make you even more excited. Head to the #arxiv for a preview arxiv.org/html/2508.01...