marcel ⊙

Great new work by @marceldotsci @spozdn @MicheleCeriotti @lab_COSMO @nccr_marvel @EPFL_en - 'Probing the effects of broken symmetries in #machinelearning' - iopscience.iop.org/article/10.108… #compchem #materials #statphys #molecules #compphys #simulation #atomistic

new work! we follow up on the topic of testing which physical priors matter in practice. this time, it seems that predicting non-conservative forces, which has a 2x-3x speedup, leads to serious problems in simulation. we run some tests and discuss mitigations!

🚀 After two+ years of intense research, we’re thrilled to introduce Skala — a scalable deep learning density functional that hits chemical accuracy on atomization energies and matches hybrid-level accuracy on main group chemistry — all at the cost of semi-local DFT. ⚛️🔥🧪🧬

I am truly excited to share our latest work with @MScherbela, @GrohsPhilipp, and @guennemann on "Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems"! arxiv.org/abs/2504.06087

Excited to announce Orb-v3, a new family of universal Neural Network Potentials from me and my team at @OrbMaterials, led by Ben Rhodes and @sanderhaute! These new potentials span the Pareto frontier of models for computational chemistry.

Guess what? By learning from energies and forces, machine learning interatomic potentials can now infer electrical responses like polarization and BECs! This means we can perform MLIP MD simulations under electric fields! arxiv.org/pdf/2504.05169

Simulating full quantum mechanical ground- and excited state surfaces with deep quantum Monte Carlo by Zeno Schätzle, Bernat Szabo and Alice Cuzzocrea. arxiv.org/abs/2503.19847 🧵⬇️

1/5: Our new materials foundation model, HIENet, combines invariant and equivariant message passing layers to achieve SOTA performance and efficiency on materials benchmarks and downstream tasks. arxiv.org/abs/2503.05771

We have a new paper on diffusion!📄 Faster diffusion models with total variance/signal-to-noise ratio disentanglement! ⚡️ Our new work shows how to generate stable molecules in sometimes as little 8 steps and match EDM’s image quality with a uniform time grid. 🧵

1/ Machine learning force fields are hot right now 🔥: models are getting bigger + being trained on more data. But how do we balance size, speed, and specificity? We introduce a method for doing model distillation on large-scale MLFFs into fast, specialized MLFFs!

We just shared our latest work with @CecClementi and @FrankNoeBerlin! arxiv.org/abs/2502.13797 RANGE addresses the "short-sightedness" of MPNNs via virtual aggregations, boosting the accuracy of MLFFs at long-range with linear time-scaling. Next-gen force-fields are here! 🧠🚀