Was it painful?

The OALD for example says a lie is “a statement made by somebody knowing that it is not true”. Ie it implies intent. I don’t think an LLM knows that it says an untruth. So it cannot lie

I mean, when Kepler figured out the laws of planetary motion, he also used old Babylonian astronomical data

Feynman Lectures!

Code and pretrained model are available at github.com/microsoft/on...

Future versions of our Skala functional, bsky.app/profile/jan...., will be trained on increasingly diverse yet steadfastly accurate data, and for multireference systems we'll need every possible tool from the quantum chemistry toolbox, and then some more. With Orbformer, we're making our own tools

Orbformer does this for the first time at scale, having been pretrained on 22k equilibrium and dissociating structures. The resulting model rivals the cost–accuracy ratio of traditional multireference methods and can be systematically converged to chemical accuracy

Traditional ab initio methods run always from scratch—no taking advantage of shared electronic structure patterns between molecules. Deep QMC changes this by first pretraining a large wavefunction model that is then cheaply fine-tuned—amortizing the pretraining cost

Why care? Strong correlation appears whenever bonds snap, radicals roam, or near-degeneracy sets in—combustion, catalysis, photochemistry. Take nitrogenase, an enzyme that can break N₂ and whose active site is a poster child for strong correlation. With Orbformer we focused on bond breaking

Cool work! Is the distillation protocol cheap enough that you could use it with DFT directly as the teacher, skipping the foundation FF entirely?

We’ll definitely release Skala as part of some DFT library! Exact plans being finalized. We’ll get in touch when we’re ready to share details. We’d love Skala to be available in ORCA

..., @marwinsegler.bsky.social, Victor Garcia Satorras, @riannevdberg.bsky.social, @paolagorigiorgi.bsky.social

www.youtube.com/watch?v=Zzt3...

..., @lab-initio.bsky.social, Deniz Gunceler, @megstanley.bsky.social, @wessel.ai, Lin Huang, Xinran Wei, Jose Garrido Torres, Abylay Katbashev, @balintmate.bsky.social, @oumarkaba.bsky.social, Roberto Sordillo, Yingrong Chen, @dbwy-science.bsky.social, Christopher Bishop, Kenji Takeda, ...

This is a highly collaborative team effort across deep learning, quantum chemistry & physics
⚡🧪 #DFT #ChemTwitter #CompChem #AI4Science

👥 The dream team: @chinweih.bsky.social, @giulia-lu.bsky.social, @derkkooi.bsky.social, Thijs Vogels, Sebastian Ehlert, Stephanie Lanius, Klaas Giesbertz, ...

To test Skala’s practical utility, we show it reliably predicts equilibrium geometries and dipole moments. Though only minimal constraints are built into its neural network design, more exact physical constraints emerge naturally as training data grows!

Which data? Trained on ~150k high-accuracy reaction energies, incl. 80k atomization energies, Skala hits an unprecedented 1.06 kcal/mol on atomization energies on W4-17. On GMTKN55 it reaches 3.89 WTMAD-2, matching SOTA hybrid functionals at the cost of semi-local DFT

What makes Skala different? Skala is a deep-learning based XC functional that bypasses expensive hand-designed nonlocal features typically used to achieve higher accuracy, by learning nonlocal representations directly from an unprecedented amount of high-accuracy data

How is DFT done today? Existing XC functionals rely on hand-crafted features from Jacob’s ladder 🪜 that trade accuracy for efficiency. Yet none achieve the chemical accuracy and generality needed for reliable predictions of the outcome of laboratory experiments

Enter Density Functional Theory (DFT), the backbone 𖠣 of computational chemistry. Although DFT can, in principle, calculate the electronic energy exactly, practical applications rely on approximations to the unknown 🔍 exchange-correlation (XC) energy functional