Microsoft Research Releases Skala-1.1 DFT Model and Expands Integrations
Microsoft Research announced Skala-1.1, an updated deep-learning exchange-correlation functional trained on 2.5x more data than its predecessor, reporting a weighted average error of 2.8 kcal/mol on the GMTKN55 benchmark. Skala is now available in CP2K and is being integrated into Psi4, FHI-aims, ORCA and VASP. Microsoft Research also introduced a living benchmark to track the performance of successive Skala releases across software packages and hardware.
Why it matters: It reports a concrete accuracy improvement and expanded availability for an AI-driven DFT functional used in computational chemistry and materials science.