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Insider Brief
- Microsoft Research AI for Science’s Skala model is now available through CP2K, allowing researchers to run more accurate quantum simulations of larger molecular systems with the help of AI.
- CASUS and Microsoft researchers reported that Skala improved simulation accuracy in their test case while maintaining computational efficiency compared with more demanding conventional DFT approaches.
- The teams plan to expand Skala’s capabilities to simulations of periodic solids and liquids while using CP2K to broaden access and testing across chemistry, physics, materials science and engineering.
PRESS RELEASE — 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. 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 mid-August, scientists from CASUS and Microsoft Research presented 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.”