Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings. OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it's important to us to support open source as part of that.

Watch Cursor build a 3M line browser in a week

claude.ai/gift It's that time of the year! Keep your loved ones thinking!

Jeff Bezos will be co-CEO of AI startup Project Prometheus theverge.com/news/821943/jef…

🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here. 🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%) 🔹 Executes up to 200 – 300 sequential tool calls without human interference 🔹 Excels in reasoning, agentic search, and coding 🔹 256K context window Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns. K2 Thinking is now live on kimi.com in chat mode, with full agentic mode coming soon. It is also accessible via API. 🔌 API is live: platform.moonshot.ai 🔗 Tech blog: moonshotai.github.io/Kimi-K2… 🔗 Weights & code: huggingface.co/moonshotai

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

🔥 Today we announce the Meta OMol25 Electronic Structures Dataset - 500 TB of molecular data in collaboration with @mshuaibii and team at @AIatMeta. We envision a future where researchers can rapidly design molecules and peptides to treat diseases, discover catalysts to revolutionize synthesis and manufacturing, identify the next electrolyte to store and transport energy to protect the grid, and more. But these breakthrough discoveries require data. Data to train next-generation AI models and interatomic potentials. Data to push the boundaries of what's computationally possible in molecular chemistry and lead the world in AI for science. Data that captures the full complexity of chemical systems, from small organic molecules to massive biomolecular complexes. The OMol25 Electronic Structures dataset includes the raw DFT outputs, electronic densities, wavefunctions, and molecular orbital information for over 4M million high-accuracy quantum chemical calculations. We see this as a transformative opportunity to develop higher quality partial charges, partial spins, and advanced electronic features to unlock the next generation of physics-informed ML models. The Materials Data Facility is proud to make these data available via the Eagle cluster at ALCF through a high-performance Globus endpoint. Given the dataset's unprecedented scale, we're first releasing all output data for a 4M random OMol25 split, with the full multi-petabyte dataset following based on community engagement. For this first release, the data are quite raw, and as-created by the Meta team. There's a significant opportunity for the community to build tools that simplify access to these data, allow data query and browsing, create databases of calculated properties and descriptors, and much more. We intend to work on these topics with all of you. We can't wait to see what you can do with these data! Access Details: github.com/facebookresearch/… Eagle was pioneered as the Petrel project, a new way to provide researchers access to high-quality, high-volume data by Ian Foster, Rachana Ananthakrishnan, Kyle Chard, Michael Papka, Rick Stevens, and others. Globus.org provides core platform capabilities (auth, data transfer, workflow automation, and compute) to over 600k researchers. Thanks to support from NIST and James Warren for making the MDF vision of vast troves of open data to fuel discovery possible. @mshuaibii, @zackulissi , @argonne, @argonne_lcf

We’re thrilled to share the release of our 2025 XPRIZE Impact Report. 🚀From carbon removal to space exploration, our prizes prove philanthropy can deliver 60x ROI in global impact. Learn how we're building the #BusinessOfBreakthroughs: xprize.org/2025-impact-repor…

“Can researchers stop AI making up citations?” nature.com/articles/d41586-0…

Mind-blowing that anyone can now run o3-level models locally within minutes. Testing gpt-oss:120b on 2x A100 SXM4: ~370 tok/s prompt processing, ~49 tok/s generation. Still more expensive than API calls, but the accessibility is game-changing

Glass materials continue to fascinate me. My last visit to @LTalirz at @SCHOTT_AG was genuinely eye-opening—learned so much about what’s possible with glass design. Highly recommend checking out the @MaterialismPod episode with Schott on this topic. materialismpodcast.com/episo…

Update on this paper from last year on AI in scientific discovery. MIT put out a press release: "no confidence in the provenance, reliability or validity of the data...[or] veracity of the research." "The author is no longer at MIT."

There are few scientists that I've admired more from afar than @xie_tian. I've followed his work since MIT CGCNN days and we have worked in parallel on generative AI for new crystal structures over the last five years. So it was a real pleasure to chat with him on the @MaterialismPod about his latest and greatest work at @Microsoft, ✨Mattergen✨!! It's pretty impressive: unconditioned as well as single or even multi-property conditioned generation for inverse design, screening for stability, some experimental validation, open source (w00t!). Give it a listen (links below) #podcast #microsoft #crystalStructure #materialsScience #materials #diffusion

The Materials Project is excited to announce MatPES, a quality-over-quantity r2SCAN and PBE dataset for training ML interatomic potentials! Huge thanks to our collabs at @materialsvrlab, the @cedergroup, and within MP! arxiv.org/abs/2503.04070