FAIR Chemistry

AI for chemistry and material science @AIatMeta. Previously known as Open Catalyst Project.

[FAIR Chemistry @OpenCatalyst]

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:

https://t.co/VGKb5OcTvV

[FAIR Chemistry @OpenCatalyst]

@steenblikrs @KevinKaichuang @JeffGuo__ @ML_Chem @CHEMICALMlRACLE @ml_chemistarip Definitely check out our demo - https://t.co/cPmeQIkx6d! Definitely not a production tool and more so educational. But it provides code snippets for someone to reproduce at scale.

[Sam Blau @SamMBlau]

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

[FAIR Chemistry @OpenCatalyst]

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

[Muhammed Shuaibi @mshuaibii]

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

[Xiang Fu @xiangfu_ml]

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

[FAIR Chemistry @OpenCatalyst]

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: https://t.co/UsVWpS01lr
Dataset: https://t.co/NyXVBMgznn
Blogpost: https://t.co/extRxI6zC5

[Jehad Abed @jehad__abed]

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: https://t.co/t3OcNvx1OH
Dataset: https://t.co/GMnJLzcsSh
Blogpost: https://t.co/vSHDQEJvNK

[Anuroop Sriram @anuroopsriram]

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.
https://t.co/xQ2TJnpusA
@bkmi13 @RickyTQChen @bwood_m

[Brandon Wood @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!
https://t.co/dSSM8wHPK5

[FAIR Chemistry @OpenCatalyst]

and of course huge thanks to @jrib_ for their help and effort with the https://t.co/lYzgPO0aMJ

[FAIR Chemistry @OpenCatalyst]

Introducing Meta’s Open Materials 2024 (OMat24) Dataset and Models! All under permissive open licenses for commercial and non-commercial use!

Paper: https://t.co/vYSutPJT7L
Dataset: https://t.co/nDZUnSiwL6
Models: https://t.co/MMPq0zKeGi

🧵1/x

[FAIR Chemistry @OpenCatalyst]

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!

[FAIR Chemistry @OpenCatalyst]

Come work with us on the FAIR Chemistry team!

Roles:
- Postdoc: https://t.co/eU3YpvUCB1
- Research interns: https://t.co/N0YLMXE5Br

Reach out if you have any questions and help spread the word!

[FAIR Chemistry @OpenCatalyst]

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

[FAIR Chemistry @OpenCatalyst]

Interesting new work led by the @johnkitchin group exploring the sources of error in OC20.

https://t.co/mmZmoEwoXo

Highlights in this thread.

[FAIR Chemistry @OpenCatalyst]

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