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Come work with us on the FAIR Chemistry team! Roles: - Postdoc: metacareers.com/jobs/119414675… - Research interns: metacareers.com/jobs/124365935… Reach out if you have any questions and help spread the word!

Our team at FAIR is looking for research interns in 2025. We work on a range of AI for chemistry topics from applied projects to machine learning potentials and generative models. If you are interested please apply and don’t hesitate to reach out! metacareers.com/jobs/124365935…

I’m excited to share our latest work on generative models for materials called FlowLLM. FlowLLM combines Large Language Models and Riemannian Flow Matching in a simple, yet surprisingly effective way for generating materials. arxiv.org/abs/2410.23405 Benjamin Kurt Miller Ricky T. Q. Chen Brandon Wood

Today we're excited to introduce OCx24 - an experimental catalyst dataset aimed to help bridge the gap between computational and experimental results. Read more below! Paper: arxiv.org/abs/2411.11783 Dataset: github.com/FAIR-Chem/fair… Blogpost: ai.meta.com/blog/open-cata…

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. arxiv.org/abs/2502.12147 1/6

Introducing the newest members to the family - OMol25 and UMA. Check them out below!

The Open Molecules 2025 dataset is out! With >100M gold-standard ωB97M-V/def2-TZVPD calcs of biomolecules, electrolytes, metal complexes, and small molecules, OMol is by far the largest, most diverse, and highest quality molecular DFT dataset for training MLIPs ever made 1/N