Accurate and scalable exchange-correlation with deep learning

Hugging Face Daily Papers Papers
deep-learning dft exchange-correlation computational-chemistry microsoft-research

Summary

Microsoft Research releases Skala, a deep-learning exchange-correlation functional for DFT that achieves 2.8 kcal/mol accuracy on GMTKN55 at semi-local cost, outperforming traditional functionals across broad chemistry benchmarks.

Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.
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Cached at: 04/22/26, 10:35 AM

Paper page - Accurate and scalable exchange-correlation with deep learning

Source: https://huggingface.co/papers/2506.14665 Today we’re sharing a major Skala update: new paper and model release.

Skala is a deep-learning exchange-correlation functional for DFT developed at Microsoft Research that currently reaches 2.8 kcal/mol on GMTKN55, wins 32 of its 55 subsets, and leads on accuracy short of double hybrids at semi-local DFT cost.

Why does this matter? Electrons are the glue holding atoms together in molecules and materials. Better XC functionals mean better predictions for reaction energies, barriers, structures, and properties that matter for chemistry, materials, and catalysis.

DFT is the workhorse of computational chemistry, but its predictive power is bottlenecked by the unknown exchange-correlation functional. Climbing Jacob’s ladder usually buys accuracy by paying more compute. The result is a functional zoo—one specialized tool per domain.

Our long-run aim is to make that zoo obsolete. Skala is our bet that one deep-learning functional will be the right choice across any system or property. Today’s results are a major step toward that goal, not the destination.

The core idea: instead of leaning on increasingly expensive hand-designed non-local ingredients, Skala learns non-local electronic representations directly from the electron density with a scalable neural architecture, at semi-local DFT cost.

The model is trained on about 400k accurate energy differences spanning atomization energies, conformers, affinities, reaction pathways, and non-covalent interactions. That’s why Skala performs strongly across main-group chemistry rather than only on one narrow benchmark.

It’s not just about energies, either. We now show accurate dipoles, hybrid-level equilibrium geometries, XC integration costs that stay in the semi-local DFT regime, and practical paths into production codes—directions we partly owe to the feedback of the DFT community.

The model is available now on GitHub, PyPI and conda-forge. We’re already working to bring Skala to major DFT codes, includingPsi4andCP2K, throughGauXCand direct code integrations.

If you try Skala on a system you care about, let us know what you find.

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