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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 03/03/2026

This is the result of a massive effort by @cesaremalosso.bsky.social, @filippobigi.bsky.social, @ppegolo.bsky.social, @jwasci.bsky.social, @mahrossi.bsky.social and Arslan Mazitov, as well as all the 🧑‍🚀 working tirelessly on the conceptual and software infrastructure.

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 22/08/2025

🚨 #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 🔍.

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 27/05/2025

📢 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...

Scheme of the GNN architecture of the FlashMD method.

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 09/05/2025

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...

Schematic representation of the lambda-MCoV architecture for predicting tensorial quantities using scalar approximators

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 03/04/2025

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!

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 19/03/2025

📢 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 🧑‍🚀

Polar plot showing the errors of several machine-learning potential of different test sets. Smaller is better here!

Plots showing the evaluation time per atom for several machine-learning potentials as a function of the number of atoms in a simulation. Smaller is better

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Reposted by Paolo Pegolo

COSMO Lab @labcosmo.bsky.social · 13/03/2025

🧑‍🍳 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.

Polarizability of a small molecule, represented as an ellipsoid

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