📢 Paper code release 📃💻 After 2 years of work, I'm excited to announce our newest paper, MatterGen, has been published in Nature! nature.com/articles/s41586-0…

Our team at @MSFTResearch is looking for a 2y ML post-doc to re-think materials design using AI. This is an exceptional opportunity to join our ambitious mission of solving some of the most pressing challenges that exist today. #ai4science #materials ⬇️See link below ⬇️

Postdoc openings on experimental data integration integration into BioEmu-next, focusing on cryo-EM and binding affinities. Join us at @MSFTResearch Cambridge or Berlin. #AI #MachineLearning #Protein #Biology apply.careers.microsoft.com/…

MatterSim now faster than ever, and with LAMMPS support for your multi-GPU simulations

Excited to announce major #MatterSim updates! 👩‍🔬 Experimentally synthesized high thermal conductor identified by MatterSim ⚡️ 3-5x inference speed-up 💪 MatterSim-MT: a new multi-task foundation model for in silico materials characterization ⬇️ Details below (1/6)

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. msft.it/6017vPamT

I'm thrilled that the new #deeplearning #DFT Skala-1.1 functional is out! arxiv.org/abs/2506.14665 Skala-1.1 breaks the longstanding accuracy-to-cost trade-off of Jacob's Ladder. Amazing collaboration with @paolagorigiorgi and the entire @MSFTResearch AI4Science team! #CompChem

The latest release of Skala is out! Accuracy largely improved beyond hybrids at semilocal cost! #compchem

Exciting new update of Skala. This table should speak for itself: Skala is now the leading density functional in main-group chemistry surpassing previous SOTA hybrid functional at a cheaper, semi-local cost. #compchem #ai4science

Today we're sharing a major Skala update: new paper and model release. Skala is a deep-learned XC functional for DFT: 2.8 kcal/mol on GMTKN55, wins 32/55 subsets & surpasses SOTA hybrids in accuracy at semi-local cost. Paper: arxiv.org/abs/2506.14665 Code: github.com/microsoft/skala

If you are interested in AI, materials science, and working on hard problems with an amazing team, this is the place for you.

We are significantly expanding to accelerate our ambitious plans for AI-driven materials discovery at @MSFTResearch AI for Science. Looking for a Data Engineer, ML Engineer and Applied Scientist (UK/NL/DE). ⬇️See job postings below ⬇️

Interested in ML for (natural) science (specifically generative models, RL, geometric DL)? We are hiring researchers, no chemistry background required! Please reach out! apply.careers.microsoft.com/…

Come join us to push the frontier of AI for biomolecular dynamics and function. A few days left to apply. @MSFTResearch AI for Science Cambridge or Berlin. Researchers: aka.ms/bioemu-ml Software ML Engineers: aka.ms/bioemu-eng #MachineLearning #AI #Biology

Skala community edition update: GPU4PySCF support is in ✅ That means Skala can plug into GPU-accelerated PySCF DFT workflows via GPU4PySCF (API-compatible with PySCF). Install docs are in the repo README: github.com/microsoft/skala #DFT #PySCF #GPU #GPU4PySCF #CompChem

#MachineLearning researchers: Join us at @MSFTResearch #ArtificialInteligence for Science to push the frontier of AI for molecular Biology or AI for Chemistry. Work with @marwinsegler or my team in Berlin, Cambridge or Amsterdam. apply.careers.microsoft.com/…

We show that our tuned LM can outperform frontier models (incl. retrieval) on the task, and retains high steerability via prompting. arxiv.org/abs/2512.13668 - work lead by the legendary @fiberleif !

📢 Hiring into three new roles in our team at MSR AI for Science, working on deep learning for DFT! 💼 I'll be at #NeurIPS in San Diego, so please reach out if interested! 👇🏻

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 we consume to high-performance materials in aerospace, electronics, and medicine. Today, we introduce a new way to simulate them. We built a machine learning force field (MLFF) to predict macroscopic properties across a broad range of polymers—trained only on quantum-chemical data, with no experimental fitting. Specifically, we accurately compute polymer densities via large-scale MD simulations, achieving higher accuracy than classical force fields. We also capture second-order phase transitions, enabling prediction of glass transition temperatures. These two properties are fundamental to processing and application design. Finally, we created a benchmark based on experimental data for 130 polymers plus an accompanying quantum-chemical dataset—laying the foundation for a fully in silico design pipeline for next-generation polymeric materials. The incredible team: Jean Helie, @temporaer, Yicheng Chen, Guillem Simeon, @a_kzna, @ErnestoCheco, @erunzzz, Gabriele Tocci, @chc273, @yatao_li, @SherryLixueC, @zunwang_msr, Bichlien H. Nguyen, Jake A. Smith, and Lixin Sun. 📄 Preprint: arxiv.org/abs/2510.13696 ⚙️ Data and code release: in progress⏳ #MLFFs #Polymers #AIforScience #DeepLearning #SimPoly #ScientificML #Microsoft #MicrosoftResearch #MicrosoftQuantum