Ben Miller @bkmi.bsky.social · 14/05/2025UMA, without fine-tuning to specific tasks, performs similarly or better in both accuracy and inference-speed/memory-efficiency than specialized models on a wide-range of material, molecular and catalysis benchmarks. 110 0
Ben Miller @bkmi.bsky.social · 14/05/2025With all these large diverse datasets, we ask whether it’s possible to train a single model that performs well across several domains without the need for additional finetuning? Introducing UMA - our latest model trained on over 30 billion atoms across our open-science datasets. 100 0
Ben Miller @bkmi.bsky.social · 14/05/2025Baseline models are released and evaluated on OOD evaluation tasks. Metrics that go beyond just a structure’s energy and forces are important for understanding how well models perform on downstream applications. We hope to release a public leaderboard in the near future! 100 0
Ben Miller @bkmi.bsky.social · 14/05/2025With over 100M structures calculated at ωB97M-V/def2-TZVPD, with variable charge+spin, and spanning biomolecules, electrolytes, metal complexes, and community datasets, OMol25 represents the largest most diverse dataset of its kind. 100 0
Ben Miller @bkmi.bsky.social · 14/05/2025While our earlier datasets (OC20, OC22, OMat24) focused on catalysts and materials, it was evident the community lacked a large, high quality, diverse molecular DFT dataset for training ML models. The Open Molecules 2025 (OMol25) Dataset hopes to accomplish just that. 100 0
Ben Miller @bkmi.bsky.social · 14/05/2025Excited to share our latest releases to the FAIR Chemistry’s family of open datasets and models: OMol25 and UMA! @AIatMeta @OpenCatalyst OMol25: huggingface.co/facebook/OMo... UMA: huggingface.co/facebook/UMA Blog: ai.meta.com/blog/meta-fa... Demo: huggingface.co/spaces/faceb... 162 0