Broadening access to Skala creates a faster path to predictive DFT
Microsoft Research ships Skala 1.1, a deep-learning exchange-correlation functional that accelerates computational chemistry predictions.
Why it matters
A specialized ML model for quantum chemistry (DFT) reaches broader accessibility and improved accuracy. Relevant to practitioners building or deploying AI in scientific computing; enthusiasts tracking frontier labs' research output beyond LLMs.
The key facts
8 to knowSkala 1.1 released by Microsoft Research
Deep-learning exchange-correlation functional for computational chemistry
Improved accuracy over prior version
Expanded accessibility across computational chemistry ecosystem
Living benchmark for computational performance tracking
Improved accuracy in exchange-correlation functional (deep-learning for density functional theory)
Living benchmark for tracking computational performance
Application domain: materials science, drug discovery, molecular simulation
Go to the source
Microsoft Researchmicrosoft.com
Publisher excerpt: Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance.