Misko
@csmisko
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FAIR, Atomwise, PetBot, Petcube, Freenome. On the side, shitty kitty, Vanlife, catforceone and all the DIY
San Francisco, CA
Joined July 2012
Unsinkable Sam 🐈⬛ He survived three WWII ship sinkings: the Bismarck, HMS Cossack, and HMS Ark Royal. Both sides, three ships, nine lives. Basically the toughest cat in history.
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2️⃣ We’re also releasing the Open Molecular Crystals (OMC25) dataset, comprised of 25 million structures, that was created to enable the FastCSP workflow. Read the paper: https://t.co/G8SX27serA Find the dataset: https://t.co/vySGNOhJZv We’re excited to share these advances,
huggingface.co
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A workflow to generate stable crystal structures, fast! ⚡ Excited to see this out! an awesome application of UMA 🤩
The Meta FAIR Chemistry team continues to make meaningful strides. 1️⃣ Today we’re announcing FastCSP, a workflow that generates stable crystal structures for organic molecules. This accelerates material discovery efforts and cuts down the time to design molecular crystals from
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The Meta FAIR Chemistry team continues to make meaningful strides. 1️⃣ Today we’re announcing FastCSP, a workflow that generates stable crystal structures for organic molecules. This accelerates material discovery efforts and cuts down the time to design molecular crystals from
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uma-s-1.1 is now live in the UMA educational demo! https://t.co/FZOQqIM8i0 The demo featured in two tutorials recently - one at NAM by @johnkitchin and one at LBL by Jagriti Sahoo! We also added some new tutorials on fine-tuning and DAC applications to
facebook-fairchem-uma-demo.hf.space
🚀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
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LLMs are crowded. GNNs for atoms? Just getting started ⚛️ UMA 1.1 is out — a universal MLIP for real-world chemistry, biology, and materials. High-accuracy. Open-source. Huge open field. It feels like AlexNet days. One GPU, one idea = real impact that can change the world!🌎
🚀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
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🚀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
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Today we're announcing that Eventual has raised $30M in Seed and Series A funding from @CRV and @felicis as well as @ycombinator, @M12vc and @Citi and others. The AI era needs data infrastructure built for AI, not retrofitted. 🧵
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We're starting to see more and more AI for chemistry and biology which I'm super excited about given the potential for good! @AIatMeta just released OMol25 on @huggingface, a dataset of 𝟭𝟬𝟬𝗠+ 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗰𝗼𝗻𝗳𝗼𝗿𝗺𝗲𝗿𝘀 spanning 83 elements and diverse chemical
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We released the Open Molecules 2025 (OMol25) Dataset last week! 🚀🧪 OMol25 is a large (100M+) and diverse molecular DFT dataset for training machine learning models. It was a massive collaborative and interdisciplinary effort and I’m super proud of the whole team! 🙌 1/7
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6 BILLION CPU core hours to generate open molecules DFT dataset! Along with UMA models that perform across domains! I am very excited and proud to be part of the amazing FAIRChem team @AIatMeta . I can’t wait to see how this open source release will be used by the community!
Excited to share our latest releases to the FAIR Chemistry’s family of open datasets and models: OMol25 and UMA! @AIatMeta @OpenCatalyst OMol25: https://t.co/UhIDiFFzzN UMA: https://t.co/eQ2wPIxbxY Blog: https://t.co/VFaeynyZN7 Demo: https://t.co/Dj29ZfhBRO
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Introducing All-atom Diffusion Transformers — towards Foundation Models for generative chemistry, from my internship with the FAIR Chemistry team @OpenCatalyst @AIatMeta There are a couple ML ideas which I think are new and exciting in here 👇
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For existing MLIPs, lower test errors do not always translate to better performance in downstream tasks. We bridge this gap by proposing eSEN -- SOTA performance on compliant Matbench-Discovery (F1 0.831, κSRME 0.321) and phonon prediction. https://t.co/rzpjGm32QL 1/6
arxiv.org
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors...
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Excited to be part of this release! How we connect computational models to real experiments is essential to making real world impact. Here we introduce OCx24 as a first step to what it would take to bridge this gap and make it a reality. Paper:
arxiv.org
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new...
OCx24 bridges the gap between computational models and experimental results with a dataset of diverse materials, featuring both positive and negative outcomes, tested under industrial conditions. We focus on metal alloy nanoparticles, requiring precise synthesis control.
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Excited to unveil OCx24, a two-year effort with @UofT 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:
arxiv.org
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new...
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