Microsoft Research· Research / PhiModel update

Broadening access to Skala creates a faster path to predictive DFT

Microsoft Research released Skala-1.1, a deep-learning DFT functional trained on 2.5× more data than the first public version, achieving a weighted average error of 2.8 kcal/mol on GMTKN55. Skala is now available in CP2K and being integrated into Psi4, FHI-aims, ORCA, and VASP. A living benchmark for tracking computational performance of Skala releases was also introduced.

From the source

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 ... Microsoft Research is also introducing a living benchmark that will track the computational performance of successive, increasingly optimized Skala releases ... It achieves a weighted average error of 2.8 kcal/mol on GMTKN55

microsoft.com