🚀 After two+ years of intense research, we’re thrilled to introduce Skala — a scalable deep learning density functional that hits chemical accuracy on atomization energies and matches hybrid-level accuracy on main group chemistry — all at the cost of semi-local DFT. ⚛️🔥🧪🧬

Very exciting work by my colleagues @MSFTResearch AI for Science!

MLFFs 🤝 Polymers — SimPoly works! Our team at @MSFTResearch AI for Science is proud to present SimPoly (SIM-puh-lee) — a deep learning solution for polymer simulation. Polymeric materials are foundational to modern life—found in everything from the clothes we wear and the food

Our deep learned exchange-correlation functional Skala is finally available to try out 🎉 Tell us what works and where skala can be improved!

Skala is now available to everyone! Why are we releasing it? Because we’re not just aiming to publish a cool paper — we’re on a mission to bring DFT to chemical accuracy using deep learning. And to make real progress, we need the community’s feedback. #compchem

Deadline for this opportunity is extended until sept 25.

Researchers have developed a #DeepLearning system called BioEmu that rapidly generates diverse protein conformations, enabling fast, accurate insights into protein flexibility and function. Learn more this week in Science: https://t.co/Pe15hm9F52

BioEmu now published in @ScienceMagazine !! What is BioEmu? Check out this video: https://t.co/PAj96iKvR7

Today in the journal Science: BioEmu from Microsoft Research AI for Science. This generative deep learning method emulates protein equilibrium ensembles – key for understanding protein function at scale. https://t.co/WwKjj5B0eb

Both openings are available in Berlin (🇩🇪Germany), Amsterdam (🇳🇱The Netherlands) and Cambridge (🇬🇧UK).

Interested in our mission @MSFTResearch to make DFT more accurate and push what’s possible in quantum chemistry? Do you want to directly contribute? We have 2 job openings! 🧪Senior researcher: https://t.co/P9nZkwweM5 ⚛️Senior software engineer:

Microsoft Research invites organizations of all sizes to join the DFT Research Early Access Program to explore the potential of our new Skala functional and accelerate innovation across industries through faster and more accurate density functional theory. https://t.co/KKL9gHIUez

Extremely excited to be sharing the output of my internship in @MSFTResearch's #AIForScience team: "Understanding multi-fidelity training of machine-learned force-fields" 🤖🧪

We're excited to reveal this major advance in computational chemistry, an AI model called Skala @MSFTResearch for the exchange-correlation functional, accurate enough, and super fast, for precise in silico predictions of chemical experiments. An early access program is now open.

🚀 After two years of intense research, we’re thrilled to introduce Skala — a scalable DL density functional that hits chemical accuracy on atomization energies and matches hybrid-level performance on main group chemistry — all at the cost of a semi-local functional. ⚛️🔥🧪⚗️🧬

Microsoft researchers achieved a breakthrough in the accuracy of DFT, a method for predicting the properties of molecules and materials, by using deep learning. This work can lead to better batteries, green fertilizers, precision drug discovery, and more. https://t.co/LuH7ZFgyVv

@chinwei_h @GiuliaLuise1 @DerkKooi @Lab_initio Deniz Gunceler @megjanestanley @ikwess Lin Huang, Xinran Wei @jagarridotorres Abylay Katbashev, @balintmt @sekoumarkaba @RobertoSordillo Yingrong Chen, David Williams-Young, Christopher Bishop, @ktakeda1 @marwinsegler @vgsatorras Jan Hermann @paolagorigiorgi

This is a highly collaborative team effort across deep learning, quantum chemistry & physics ⚡🧪 #DFT #ChemTwitter #CompChem #AI4Science 👥 The dream team: @chinwei_h, @GiuliaLuise1, @DerkKooi, Thijs Vogels, Sebastian Ehlert, Stephanie Lanius, Klaas Giesbertz, ...

To test Skala’s practical utility, we show it reliably predicts equilibrium geometries and dipole moments. Though only minimal constraints are built into its neural network design, more exact physical constraints emerge naturally as training data grows!

Which data? Trained on ~150k high-accuracy reaction energies, incl. 80k atomization energies, Skala hits an unprecedented 1.06 kcal/mol on atomization energies on W4-17. On GMTKN55 it reaches 3.89 WTMAD-2, matching SOTA hybrid functionals at the cost of semi-local DFT.

What makes Skala different? Skala is a deep-learning based XC functional that bypasses expensive hand-designed nonlocal features typically used to achieve higher accuracy, by learning nonlocal representations directly from an unprecedented amount of high-accuracy data.