Broadening access to Skala creates a faster path to predictive DFT

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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 …

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