COSMO Lab @lab_COSMO

Computational Science and Modelling of materials and molecules at the atomic-scale, with machine learning. Join us in the fediverse @[email protected] cosmo.epfl.ch Lausanne, Switzerland Joined February 2017

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marcel ⊙ @marceldotsci

a month ago

✨preprint alert: "learning long-range representations w/ equivariant messages" in which we get into the fray of long-range MLIPs and propose to use equivariant charges w/ classical electrostatics as long-range MP mechanism. we design LOREM, an equivariant MLIP, around this...

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COSMO Lab @lab_COSMO

4 months ago

📢For those still hanging out here, a new #cookbook recipe landed, demonstrating >2x speedup of *conservative* MD for the PET-MAD universal #MLIP using multiple time stepping. Check out the details at the other place, or head to atomistic-cookbook.org/examples/pet-m… #compchem #machinelearning

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COSMO Lab @lab_COSMO

5 months ago

🚨help needed!🚨 Does anyone, ORB-affiliated or not, can share with us the model weights orb-v2-20241011.ckpt? We need urgently to reproduce some results we obtained with that model checkpoint, but it was removed from their huggingface repo because "the model reached EoL" 🤷. 🙏

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COSMO Lab @lab_COSMO

6 months ago

🤫 you can get a better universal #machinelearning potential by training on fewer than 100k structures. too good to be true? head to arxiv:2503.14118, to the atomistic cookbook, or to the better science social if you want to find out more about PET-MAD. 🧑‍🚀 over and out 👋

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Physical Review Materials @PhysRevMater

7 months ago

In this #EdSugg, researchers @lab_COSMO @EPFL_en introduce an improved machine learning method for predicting the electronic density of states of a material, which uses a self-aligning loss function to provide an adaptive energy reference during training go.aps.org/4aEZfFK

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Guillaume Fraux @luthaf

8 months ago

Happy new year everyone! After more than three years of work, and splitting out a whole separate package, I am extremely happy to announce the first full public release of featomic, a package to compute representations for atomistic machine learning! 1/n

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COSMO Lab @lab_COSMO

9 months ago

torch-pme has landed. if you're curious, head to the better place to see why we're so excited about it 👀

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COSMO Lab @lab_COSMO

9 months ago

Let's go!

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COSMO Lab @lab_COSMO

10 months ago

It is time for jack-o-molecule 🎃! Happy chemiscoping chemiscope.org/?load=https%3A…

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COSMO Lab @lab_COSMO

10 months ago

Kind of bittersweet that it's so easy to lean equivariance to the point where it does not matter, but that's the evidence 🤷. Also, if you see similar stuff, there's a good set of benchmarks to stress test your unconstrained model!