Anticipating 🧑‍🚀 Wei Bin's talk at #psik2025 (noon@roomA), 📢 a new #preprint using PET and the MAD dataset to train a universal #ml model for the density of states, giving band gaps for solids, clusters, surfaces and molecules with MAE ~200meV. Go to the talk, or check out arxiv.org/html/2508.17...!

Read the associated publication: pubs.acs.org/doi/abs/10.1...

Many thanks to our collaborators at the Laboratory of Magnetic Resonance (LRM) at EPFL:
Jacob Brian Holmes, Ruben Rodriguez-Madrid, Florian Viscosi and Lyndon Emsley

Funding:
Swiss National Science Foundation, NCCR MARVEL, ERC Horizon

First, we generate a pool of candidate structures and then select the one whose calculated chemical shieldings best match the experimental measurements. Because shielding calculations quickly become the computational bottleneck, machine-learning (ML) models can substantially reduce the cost.

Solid-state NMR spectroscopy is increasingly used for structure determination of organic solids. NMR-based structure determination differs from conventional diffraction methods:

We're introducing ShiftML3, a new ShiftML model for chemical shielding predictions in organic solids.

* ShiftML3 predicts full chemical shielding tensors
* DFT accuracy for 1H, 13C, and 15N
* ASE integration
* GPU integration

Code: github.com/lab-cosmo/Sh...
Install from Pypi: pip install shiftml

🚨 #machinelearning for #compchem goodies from our 🧑‍🚀 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 🔍.

🎉 DFT-accurate, with built-in uncertainty quantification, providing chemical shielding anisotropy - ShiftML3.0 has it all! Building on a successful @nccr-marvel.bsky.social-funded collaboration with LRM🧲⚛️, it just landed on the arXiv arxiv.org/html/2506.13... and on pypi pypi.org/project/shif...

When you combine #machinelearning and #compchem, you need to start worrying at the QM details within your ML architecture. We use our indirect Hamiltonian framework and pySCFAD to explore the enormous design space arxiv.org/abs/2504.01187

📢 PET-MAD has just landed! 📢 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 🧑‍🚀

Happy to share a new #cookbook recipe that shocases several new software developments in the lab, using the good ole' QTIP4P/f water model as an example. atomistic-cookbook.org/examples/wat.... TL;DR - you can now build torch-based interatomic potentials, export them and use them wherever you like!

Feeling a bit lonely here ...