Broadening access to Skala creates a faster path to predictive DFT 

Broadening access to Skala creates a faster path to predictive DFT  - Microsoft Research

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At a glance

Skala 1.1 demonstrates the continuously improving nature of Microsoft Research’s deep-learning DFT approach: trained on 2.5× more data than its predecessor, it delivers substantially higher accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction.

Skala is now available in CP2K and is being integrated into Psi4 , FHI-aims , ORCA and VASP , bringing next-generation DFT accuracy closer to the communities that rely on these codes every day.

Microsoft Research is also introducing a living benchmark that will track the computational performance of successive, increasingly optimized Skala releases to help the community measure and accelerate progress toward ever greater accuracy and efficiency.

Together, these developments mark another milestone toward a future in which computational chemistry simulations are both predictive and integrated in all relevant scientific and industrial workflows.

Bringing density functional theory (DFT) to predictive accuracy is a journey, not a single breakthrough. Since introducing Skala , our deep-learning exchange-correlation functional, we have continued to advance along two complementary fronts: improving accuracy and expanding accessibility across the computational chemistry ecosystem.

Figure 1: Accuracy of Skala-1.1 for thermochemistry, kinetics, and non-covalent interactions. At the computational cost of a meta-GGA functional, Skala 1.1 outperforms the best, most expensive global hybrid functionals, ranking first (earning gold medals) in 32 of the 55 categories of the widely used GMTKN55 benchmark, which spans a broad range of chemical problems.

On the accuracy front, the release of Skala-1.1 (opens in new tab) provides the first demonstration of the continuous-improvement paradigm underlying Skala. Trained on 2.5x more data than the first public version of Skala, the updated model delivers substantially improved performance across key challenges in molecular simulation, including main-group thermochemistry, reaction kinetics, and molecular structure prediction.

But accuracy alone is not enough. DFT is the computational engine behind a vast range of scientific and industrial workflows, spanning chemistry, materials science, catalysis, energy technologies, and drug discovery. To have real-world impact, advanced functionals must be accessible where scientists already perform their calculations. That is why we are also expanding the Skala ecosystem through collaborations with leading electronic-structure software developers.

Today, we are announcing that Skala is available in CP2K and is being integrated into Psi4 , FHI-aims , ORCA and VASP , bringing next-generation DFT accuracy closer to the communities that rely on these codes every day. Alongside these integration efforts, we are introducing a living benchmark that tracks the computational performance of successive, increasingly optimized Skala releases. By providing a transparent and continuously updated reference for implementations across software packages and hardware platforms, this resource will help the community measure and accelerate progress toward ever greater accuracy and efficiency.

Together, these developments mark another milestone toward a future in which computational chemistry simulations are both predictive and accessible across a broader range of relevant scientific and industrial workflows.

Want to learn more about Skala and why DFT plays such an important role in in-silico discovery? Read also our first blog post (opens in new tab) .

Skala as a continuously improving functional

Unlike the traditional “functional zoo”, where new functionals accumulate without replacing older ones, Skala follows a different philosophy: each release is designed to supersede the previous one. As new data, model architectures, and training strategies become available, the model improves while maintaining the same practical computational cost.

Skala-1.1 is the latest demonstration of this approach. It achieves a weighted average error of 2.8 kcal/mol on GMTKN55 , a widely used benchmark suite comprising 55 categories of chemistry, including thermochemistry, reaction barriers, and noncovalent interactions. This level of accuracy surpasses today’s leading global (range-separated) hybrid functionals while retaining the efficiency of a semi-local functional. Beyond energies, Skala-1.1 also provides highly accurate electron densities, dipole moments, and molecular geometries.

These advances were enabled by major expansions of the Microsoft Research Accurate Chemistry Collection (opens in new tab) (MSR-ACC) , our large-scale collection of high-accuracy quantum-chemistry reference data generated with expensive...