FAIR Chemistry

Introducing fairchem - our revamped codebase consolidating our AI modeling efforts in chemistry and materials science. fairchem makes it easy to interface with our data, models, demos, and applications - including an easy to use ASE calculator: github.com/FAIR-Chem/fair…

@steenblikrs @KevinKaichuang @JeffGuo__ @ML_Chem @CHEMICALMlRACLE @ml_chemistarip Definitely check out our demo - huggingface.co/spaces/faceboo…! Definitely not a production tool and more so educational. But it provides code snippets for someone to reproduce at scale.

I am looking for an interactive chemistry modeling tool (maybe with drag and drop capabilities) for a non-chemist that I can use with the latest Open Molecules 2025 dataset. What do you recommend @OpenCatalyst @KevinKaichuang @JeffGuo__ @ML_Chem @CHEMICALMlRACLE @ml_chemistarip

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

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

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

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…

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 @bkmi13 @RickyTQChen @bwood_m

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…

and of course huge thanks to @jrib_ for their help and effort with the …tbench-discovery.materialsproject.org

Work by @lbluque, @mshuaibii, @xiangfu_ml, @bwood_m, @csmisko, Meng Gao, @ammarhrizvi, C. Lawrence Zitnick, @zackulissi Get in touch to share your experience and feedback!

Introducing Meta’s Open Materials 2024 (OMat24) Dataset and Models! All under permissive open licenses for commercial and non-commercial use! Paper: arxiv.org/abs/2410.12771 Dataset: huggingface.co/datasets/fairc… Models: huggingface.co/fairchem/OMAT24 🧵1/x

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!

Work by @KareemAbdelmaq3 @mshuaibii @AdeeshKolluru Raffaele Cheula and @johnkitchin

Before using an adsorption energy model, one should be aware of surface reconstructions that can impact results. Alternatively, total energy models are more robust models to surface reconstructions that still work on par with existing adsorption energy models

Interesting new work led by the @johnkitchin group exploring the sources of error in OC20. #fn1" target="_blank" rel="nofollow noopener">pubs.rsc.org/en/content/art… Highlights in this thread.