Guillaume Fraux
@luthaf.bsky.social
The link above is missing a password, pleas use epfl.zoom.us/j/6836877674... instead!
June 13, 2025 at 7:42 AM
The link above is missing a password, pleas use epfl.zoom.us/j/6836877674... instead!
Reposted by Guillaume Fraux
We wouldn't be @labcosmo.bsky.social if we didn't want you to go break it, so head to atomistic-cookbook.org/examples/fla... for a crash-course 🧑🍳📖 recipe, but not before reading the warnings arxiv.org/html/2505.19.... Have fun! #compchem #machinelearning #md @nccr-marvel.bsky.social @erc.europa.eu
Long-stride trajectories with a universal FlashMD model - The Atomistic CookbookContentsMenuExpandLight modeDark modeAuto light/dark, in light modeAuto light/dark, in dark mode
atomistic-cookbook.org
May 27, 2025 at 7:03 AM
We wouldn't be @labcosmo.bsky.social if we didn't want you to go break it, so head to atomistic-cookbook.org/examples/fla... for a crash-course 🧑🍳📖 recipe, but not before reading the warnings arxiv.org/html/2505.19.... Have fun! #compchem #machinelearning #md @nccr-marvel.bsky.social @erc.europa.eu
Reposted by Guillaume Fraux
If you do, the rewards can be very impressive: you can run solvated alanine dipeptide and observe superionic behavior in LiPS with 16fs time step, and watch the Al(110) surface pre-melt in strides of 64fs. And all with the same universal model, no fine-tuning needed!
May 27, 2025 at 7:03 AM
If you do, the rewards can be very impressive: you can run solvated alanine dipeptide and observe superionic behavior in LiPS with 16fs time step, and watch the Al(110) surface pre-melt in strides of 64fs. And all with the same universal model, no fine-tuning needed!
Reposted by Guillaume Fraux
Much as for direct force prediction [ arxiv.org/html/2412.11... ] you better know what you are doing: you've no guarantee of energy conservation, or of equipartition, so you should know your thermostats VERY well. Caveat emptor.
May 27, 2025 at 7:03 AM
Much as for direct force prediction [ arxiv.org/html/2412.11... ] you better know what you are doing: you've no guarantee of energy conservation, or of equipartition, so you should know your thermostats VERY well. Caveat emptor.
Reposted by Guillaume Fraux
Filippo's idea was to use the heavy-duty PET-MAD model [ arxiv.org/html/2503.14... ] to generate a bunch of trajectories of wildly different compounds and use a PET-like architecture to learn (q',p') from (q,p), and and to think A LOT about the many things that could possibly go wrong.
PET-MAD, a universal interatomic potential for advanced materials modeling
arxiv.org
May 27, 2025 at 7:03 AM
Filippo's idea was to use the heavy-duty PET-MAD model [ arxiv.org/html/2503.14... ] to generate a bunch of trajectories of wildly different compounds and use a PET-like architecture to learn (q',p') from (q,p), and and to think A LOT about the many things that could possibly go wrong.
Reposted by Guillaume Fraux
There are reports as early as 2021 [cf. arxiv.org/abs/2111.15176 ] of using neural nets to predict a MD trajectory in large strides, but these were usually limited to a single system in a given thermodynamic state point. Nice, but not life-changing.
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks
Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of the integrator need ...
arxiv.org
May 27, 2025 at 7:03 AM
There are reports as early as 2021 [cf. arxiv.org/abs/2111.15176 ] of using neural nets to predict a MD trajectory in large strides, but these were usually limited to a single system in a given thermodynamic state point. Nice, but not life-changing.
Reposted by Guillaume Fraux
If you don't want to read, but want to cook, guess what? The 🧑🍳📖 #atomistic-cookbook has you covered. Head to atomistic-cookbook.org/examples/lea... to see how to use this, as simple as `mtt train mymodel.yaml`.
Equivariant model for tensorial properties based on scalar features - The Atomistic CookbookContentsMenuExpandLight modeDark modeAuto light/dark, in light modeAuto light/dark, in dark mode
atomistic-cookbook.org
May 9, 2025 at 6:50 AM
If you don't want to read, but want to cook, guess what? The 🧑🍳📖 #atomistic-cookbook has you covered. Head to atomistic-cookbook.org/examples/lea... to see how to use this, as simple as `mtt train mymodel.yaml`.
Reposted by Guillaume Fraux
If you don't have time for the 20-pages appendix, the TL;DR is that approximating tensors is harder than the vector case, but can be made as simple as possible using angular momentum theory. The practical implementation we propose is not as rigorous, but works well and is very fast in practice.
May 9, 2025 at 6:50 AM
If you don't have time for the 20-pages appendix, the TL;DR is that approximating tensors is harder than the vector case, but can be made as simple as possible using angular momentum theory. The practical implementation we propose is not as rigorous, but works well and is very fast in practice.
Reposted by Guillaume Fraux
You can use these safely in MD, by multiple time stepping (a '90s classic: doi.org/10.1063/1.46...) so you get reliable conservative trajectories, for any material, twice as fast! Let us know if it works for you, and even more importantly, if it doesn't! 👉 atomistic-cookbook.org/examples/pet...
Reversible multiple time scale molecular dynamics
The Trotter factorization of the Liouville propagator is used to generate new reversible molecular dynamics integrators. This strategy is applied to derive reve
doi.org
May 7, 2025 at 5:24 AM
You can use these safely in MD, by multiple time stepping (a '90s classic: doi.org/10.1063/1.46...) so you get reliable conservative trajectories, for any material, twice as fast! Let us know if it works for you, and even more importantly, if it doesn't! 👉 atomistic-cookbook.org/examples/pet...
Reposted by Guillaume Fraux
You can read all about it here, arxiv.org/abs/2503.14118, see it in action as a #cookbook recipe 🧑🍳 atomistic-cookbook.org/examples/pet... or rush to install it following the instructions here github.com/lab-cosmo/pe...
March 19, 2025 at 7:23 AM
You can read all about it here, arxiv.org/abs/2503.14118, see it in action as a #cookbook recipe 🧑🍳 atomistic-cookbook.org/examples/pet... or rush to install it following the instructions here github.com/lab-cosmo/pe...
Reposted by Guillaume Fraux
Should be super-easy to adapt to whatever tensor you're trying to learn (and to extend to more sophisticated models than symmetry-adapted regression). Let us know what you cook with it 😋!
March 13, 2025 at 5:30 PM
Should be super-easy to adapt to whatever tensor you're trying to learn (and to extend to more sophisticated models than symmetry-adapted regression). Let us know what you cook with it 😋!
Reposted by Guillaume Fraux
In particular, this recipe relies on metatensor.github.io/featomic to compute the features, and the docs.metatensor.org/latest/torch... backend of #metatensor to export a self-contained ASE-compatible calculator. Easy to use, fast, and accurate.
metatensor.github.io
March 13, 2025 at 5:30 PM
In particular, this recipe relies on metatensor.github.io/featomic to compute the features, and the docs.metatensor.org/latest/torch... backend of #metatensor to export a self-contained ASE-compatible calculator. Easy to use, fast, and accurate.