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Congrats Frank! Well deserved!

Really amazing datasets out there! Great work FAIR Chemistry !

New paper out! We introduce “Generator Matching” (GM), a method to build GenAI models for any data type (incl. multimodal) with any Markov process. GM unifies a range of state-of-the-art models and enables new designs of generative models. arxiv.org/abs/2410.20587 (1/5)

Does equivariance matter when you have lots of data and compute? In a new paper with Sönke Behrends, Pim de Haan, and Taco Cohen, we collect some evidence. arxiv.org/abs/2410.23179 1/7

Feel free to reach out here as well. I will be looking for an intern in 2025!

Generating stable materials as text, then refining with flow matching?? I was astonished how well this works. Nice paper with clearly impressive results. Really great work Anuroop Sriram, et. al.!!

Exciting to see SBI used for such an important and big fundamental physics problem!

New paper again: in this study, we explore how ML can benefit numerical relativity simulations, when generating a training dataset is prohibitively expensive. The answer we came out with is super-resolution + constrained optimization! arxiv.org/abs/2411.02453

bkmi

Excited to unveil OCx24, a two-year effort with University of Toronto and @VSParticle! We've synthesized and tested in the lab hundreds of metal alloys for catalysis. With 685 million AI-accelerated simulations, we analyzed 20,000 materials to try and bridge simulation and reality. Paper:

Spent the weekend hacking together Exa embeddings over 4500 NeurIPS 2024 papers - neurips.exa.ai Let's you: - do otherwise impossible searches ("transformer architectures inspired by neuroscience") - explore a 2D t-SNE plot - chat with Claude about multiple papers

I learned a lot working on this project! Please take a look, I think its quite interesting stuff. Sampling conformers using carteisan coordinates, a potential, and NO data! Frank Noe Jose Miguel Hernández-Lobato

Really happy to have been on the team while this was going on. Also excited about what it means for sampling! Check out the blog post about our work sampling from these models: linkedin.com/posts/aiatmeta…

We've open sourced Adjoint Sampling! It's part of a bundled release showcasing FAIR's research and open source commitment to AI for science. github.com/facebookresear… x.com/AIatMeta/statu…

Great pic and even greater release! :)

François is really good!

🚀Exciting news! We are releasing new UMA-1.1 models (Small and Medium) today and the UMA paper is now on arxiv! UMA represents a step-change in what’s possible with a single machine learning interatomic potential (short overview in the post below). The goal was to make a model

Cool work led by Guan-Horng Liu! Removing the restriction on memoryless SDEs enables a lot of relevant cases in chemistry and more... also better results! Take advantage of the freedom of flow & bridge matching to choose a base dist & learn from energy alone! No more data!