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Posts by Joe Abbott

And also Guillaume talking (very soon at 10:45) in Room 1 about all things metatensor and metatomic - making it easier to develop and use ML models for atomistic simulations πŸ”₯ #psik2025

7 months ago 0 0 0 0

If you are at the #psik2025 and want to know more about the #metatensor ecosystem, don't miss @luthaf.bsky.social talk tomorrow morning 9:45 in room 1

7 months ago 2 1 0 1

Come and see my poster "On the importance of symmetry constraints for learning equivariant quantum mechanical properties" at Psi-k this lunchtime, poster B5.01!

7 months ago 1 0 0 0
A cartoon explaining how mild finite-temperature conditions induce disorder and dynamical reconstruction on the surfaces of lithium thiophosphates

A cartoon explaining how mild finite-temperature conditions induce disorder and dynamical reconstruction on the surfaces of lithium thiophosphates

πŸ“’ Now out on @physrevx.bsky.social energy, journals.aps.org/prxenergy/ab... from πŸ§‘β€πŸš€ @dtisi.bsky.social and Hanna TΓΌrk, our #PET -powered study of the dynamic reconstruction of LPS surfaces, and how it affects their structure, stability and reactivity.

7 months ago 9 4 1 0
metatensor logo

metatensor logo

metatomic logo

metatomic logo

🚨 #machinelearning for #compchem goodies from our πŸ§‘β€πŸš€ 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 πŸ”.

8 months ago 12 8 1 2
Scheme of the GNN architecture of the FlashMD method.

Scheme of the GNN architecture of the FlashMD method.

πŸ“’ Running molecular dynamics with time steps up to 64fs for any atomistic system, from Al(110) to Ala2? Thanks to πŸ§‘β€πŸš€ 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...

10 months ago 37 12 1 1
Polar plot showing the errors of several machine-learning potential of different test sets. Smaller is better here!

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

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

πŸ“’ PET-MAD has just landed! πŸ“’ 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 πŸ§‘β€πŸš€

1 year ago 28 9 1 3