AI model Skala now available on the open-source simulation platform CP2K
The Skala AI model, developed by Microsoft Research AI for Science, is now available through the CP2K software ecosystem. Thanks to Skala, researchers can perform quantum mechanical simulations of larger molecular systems while achieving a higher level of accuracy. In early 2026, Microsoft’s research division and the CP2K team at the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf started a collaboration to integrate the Skala AI model into CP2K. The global CP2K community is expected to test the AI model’s applicability to a wide variety of problems in chemistry, physics, materials science, and engineering.
For many simulation applications, density functional theory (DFT)—which was awarded the Nobel Prize in 1998—has proven to be crucial. “The Achilles’ heel of DFT is the so-called exchange-correlation functional,” says CASUS Director Prof. Thomas D. Kühne. In science, functionals refer to the search for the function best suited to a given problem. Although the exchange-correlation functionals developed for DFT in recent years have become increasingly sophisticated, the most accurate functionals require so much computation time that they can only be used for systems with a small number of particles.
Additional information:
Prof. Thomas D. Kühne
Director
Center for Advanced Systems Understanding (CASUS) at HZDR
Media contact:
Dr. Martin Laqua
Officer Communications, Press and Public Relations
Center for Advanced Systems Understanding (CASUS) at HZDR
Cross-section of a 3D representation of the exchange-correlation (XC) energy density for a benzene molecule. This was calculated using the Skala model and contributes to the system’s total energy density. The inner ring shows the molecule’s six carbon atoms, while the smaller outer ring shows the six hydrogen atoms. The highest values of the XC energy density are found in a ring-shaped pattern around the individual atoms of the molecule, particularly around the carbon atoms. Source: F. Pöschel/CASUS
Skala is a new exchange-correlation functional developed by Microsoft Research AI for Science. Instead of introducing yet another layer of mathematical formulas, Microsoft took a new approach with Skala: Here, a neural network has learned how electron densities in different regions influence one another. This makes Skala one of the first AI-based exchange-correlation functionals available.
“The results presented by Microsoft Research in 2025 were impressive, particularly the combination of accuracy and computational efficiency for certain DFT calculations,” reports Kühne. “We saw an opportunity to evaluate the approach within CP2K and better understand its applicability to problems relevant to our community. Of course we’re excited to be the first group outside Microsoft that has confirmed the benefits of Skala.”
In a preprint published in mid-August, scientists from CASUS and Microsoft Research present the first results of the collaboration. They demonstrate that the approach taken is indeed promising. “I can confirm that with Skala we’ve achieved a noticeable leap in the accuracy of our simulations for our specific test case,” says lead author Franz Pöschel of CASUS’ Scientific Computing Core.
High level of interest in response to announcement
Microsoft Research AI for Science had announced the upcoming integration of Skala into CP2K in the spring of 2026 at a conference. Since then, the community has been regularly checking in on the progress. “Even though we were pleased by the interest, we felt a certain pressure to deliver,” says Pöschel. “I’m therefore glad to confirm that Skala is now available within CP2K for simulations of molecular systems.”
Dr. Sebastian Ehlert, Senior Researcher at Microsoft Research AI for Science, explains why CP2K is so meaningful to the Skala team: “To increase adoption, Skala should be available where the community already works. CP2K has been a cornerstone of computational chemistry research for many years, making it a natural priority for integration.”
The CP2K software ecosystem is a powerful tool for performing DFT calculations, particularly in the context of dynamic simulations of large systems over long time periods. On the open-source platform, molecules, liquids, solids, and biological systems can be simulated using quantum mechanical methods such as density functional theory and classical molecular dynamics. Thanks to its efficient algorithms and the ability to execute individual computational steps in parallel on specialized computer architectures, CP2K can compute even very large systems with thousands to tens of thousands of atoms. This makes the software particularly well-suited for research into battery materials, catalysts, semiconductors, proteins, and other complex materials. Recently, CP2K was enhanced with features that enable the use of AI-based models. Using training data generated by CP2K, these models can predict molecular energies and forces with high accuracy. This expands the time and length scales of the simulations.
More updates in the works
Prior to release, the teams at Microsoft Research AI for Science and CASUS extensively tested the Skala integration in CP2K. “We want to ensure that Skala delivers consistent accuracy and speed across different programs and settings,” says Ehlert. “Together with the CP2K team, we created a set of integration tests to ensure that Skala delivers numerically correct results. We are especially grateful for the support of the CASUS team with their in-depth experience on numerical verification of computational methods to design this suite of integration tests.”
Skala is not a static approximation: The AI model is constantly being improved and expanded with new capabilities. It will soon enable simulations of periodic solids—such as metals and semiconductors—as well as liquids. CP2K can serve as a platform for rapid and widespread adoption: the recent integration will make it easier to have the latest Skala release in CP2K. “Microsoft Research AI for Science puts high standards on its research partners,” explains Kühne. “We are confident that, following this initial success, our collaboration will lead to many more achievements.”
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Publication
F. Pöschel, J. Pototschnig, F. Stein, A. Knüpfer, T. Vogels, S. Battaglia, S. Ehlert, J. Hutter, T. D. Kühne: Molecular Implementation of the Machine-Learned Skala Exchange-Correlation Functional in CP2K through GauXC, arXiv, 2026 (DOI: 10.48550/arXiv.2608.19033)
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About the Center for Advanced Systems Understanding
CASUS was founded 2019 in Görlitz/Germany and pursues data-intensive interdisciplinary systems research in such diverse disciplines as earth systems research, systems biology or materials research. The goal of CASUS is to create digital images of complex systems of unprecedented fidelity to reality with innovative methods from mathematics, theoretical systems research, simulations as well as data and computer science to give answers to urgent societal questions. The founding partners of CASUS are the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), the Helmholtz Centre for Environmental Research in Leipzig (UFZ), the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden (MPI-CBG), the Technical University of Dresden (TUD) and the University of Wrocław (UWr). CASUS, managed as an institute of the HZDR, is funded by the German Federal Ministry of Research, Technology and Space (BMFTR) and the Saxon State Ministry for Science, Culture and Tourism (SMWK).
Additional information:
Prof. Thomas D. Kühne
Director
Center for Advanced Systems Understanding (CASUS) at HZDR
Media contact:
Dr. Martin Laqua
Officer Communications, Press and Public Relations Center for Advanced Systems Understanding (CASUS) at HZDR