COSMO Lab
@lab_COSMO
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Computational Science and Modelling of materials and molecules at the atomic-scale, with machine learning. Join us in the fediverse @[email protected]
Lausanne, Switzerland
Joined February 2017
RT @marceldotsci: ✨preprint alert: "learning long-range representations w/ equivariant messages" in which we get into the fray of long-rang….
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Just to bring some 🧑🚀 science fun to those of us still hanging out in this pit of despair, just wanted to share the exciting news you can now run MD (better, FlashMD⚡️) with up to 64ps timestep for systems as diverse as ala2, Al(110), LiPS, and whatever comes to your mind.
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📢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 #compchem #machinelearning.
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🚨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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🤫 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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RT @PhysRevMater: In this #EdSugg, researchers @lab_COSMO @EPFL_en introduce an improved machine learning method for predicting the electro….
journals.aps.org
Machine learning methods for predicting electronic density of states often assume that the model predictions and targets share the same absolute energy reference. However, this overlooks a subtle...
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RT @__luthaf__: Happy new year everyone! After more than three years of work, and splitting out a whole separate package, I am extremely ha….
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There's some nuance though. For that, head to the preprint or head to the detailed 🧵 at the better place
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Join the dark side of the forces (or not!). TL;DR - Using forces that are not conservative is not a great idea. Latest and greatest, now on the @arxiv , from Filippo Bigi and @marceldotsci.
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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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This is funded by the and will involve working together with @corminboeuf_lab and Anirudh Natarajan's lab. Plus, you'll be working with the one and only 🧑🚀 @__luthaf__ so you really should not sit this one out!.
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Hello! I'm posting this both here and on the better place, let's see where it gets more re-posts 😇. We are looking for a research software engineer to help us develop (even) better code for #atomicscale #machinelearning. Check out the specs and apply!.
epfl.ch
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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!.
Great new work by @marceldotsci @spozdn @MicheleCeriotti @lab_COSMO @nccr_marvel @EPFL_en - 'Probing the effects of broken symmetries in #machinelearning' - #compchem #materials #statphys #molecules #compphys #simulation #atomistic
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Simple, cheap, and surprisingly accurate. What's not to like of uncertainty estimation from repurposed prediction rigidities?.
Great new work by Filippo Bigi @SanggyuChong @MicheleCeriotti @FedericoGrass @lab_COSMO @EPFL_en @UNIMORE_univ - 'A prediction rigidity formalism for low-cost uncertainties in trained #neuralnetworks' - #machinelearning #statphys #Bayesian #compchem #AI
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Hot topics require hot thermostats 🌡️.
New version of i-PI is out: faster and ready for the machine-learning era in atomistic simulations. New features enabling research on the hottest topics in physical chemistry make the code complete!
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RT @marceldotsci: accepted now in @MLSTjournal! final manuscript, scripts, and data coming soon.
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The recipe covers the basics, as well as intermediate topics, such as colored-noise thermostats, and the recovery of dynamical properties from a a thermostatted trajectory using an iterative deconvolution scheme.
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