🚨 #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...

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...

Kudos to Divya, @jnigam.bsky.social , Sandra, @ppegolo.bsky.social, Hanna - as well as our Caltec/pySCF collaborators Xing and Garnet. And OFC thanks to our funders @nccr-marvel.bsky.social, @snf-fns-ch.bsky.social and @erc.europa.eu. Comments welcome!

📢 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 🧑‍🚀

🧑‍🍳 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.