Using a universal machine-learning potential, researchers mapped how halogen and metal substitution impact the structure, phase stability, and lithium ion conductivity of halide solid electrolyte compounds. Read the Physical Review Materials paper: https://go.aps.org/4c85mEn

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...

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...

📢 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

👏 Congratulations to Prof. @micheleceriotti.bsky.social (@labcosmo.bsky.social) and Prof. Roland Logé (LMTM) on their promotion to Full Professor of Materials Science in the School of Engineering! More info: actu.epfl.ch/news/appoint...

📢 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.

Just before my last week in @labcosmo.bsky.social. Our metatensor and metatomic paper is out! A collection of the hard work we’ve done at @labcosmo.bsky.social to make atomistic machine learning easier to use for experts and not alike.

COSMO Lab@labcosmo.bsky.social · last yr.

🚨 #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 🔍.

📢 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...

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🧵

Last week, we continued @labcosmo.bsky.social's study of solid-state electrolytes, focusing on the properties of the LPS surfaces arxiv.org/abs/2504.11553. Many thanks to my office mate Hanna Tuerk, who did a lot of the work

Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces

Lithium thiophosphate (LPS) is a promising solid electrolyte for next-generation lithium-ion batteries due to its superior energy storage, high ionic conductivity, and low-flammability components. Des...

arxiv.org

Find (many) more benchmarks and tests in the preprint arxiv.org/html/2503.14..., try PET-MAD for yourself, and let us know if you manage to break it - we're already working towards PET-MAD-2 😅 #compchem @nccr-marvel.bsky.social @materials-epfl.bsky.social @erc.europa.eu @cscsch.bsky.social

@labcosmo.bsky.social continuing its strong performance with Davide Tisi's talk on transport mechanisms in solid-state electrolytes!

📢 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 🧑‍🚀