The Open Catalyst demo supports adsorption energy calculations for 11,427 catalyst materials and 86 adsorbates, which amounts to ~100M catalyst surface-adsorbate combinations — a scale impossible to explore without machine learning.
@aiatmeta
-
AI at Atomic Level Accelerates Scientific Discovery
By
–
Our ability to utilize AI to understand the world at the atomic level opens up a range of new possibilities, and opportunities to address some of the most pressing challenges in science. We're excited to help accelerate this field of work with the @OpenCatalyst Project.
-
Open Catalyst Demo Released for Accelerated Material Science Research
By
–
Today we're releasing the Open Catalyst Demo to the public — this new service will allow researchers to accelerate work in material sciences by enabling them to simulate the reactivity of catalyst materials ~1000x faster than existing computational methods using AI.
— AI at Meta (@AIatMeta) 27 juillet 2023
Demo ⬇️Today we're releasing the Open Catalyst Demo to the public — this new service will allow researchers to accelerate work in material sciences by enabling them to simulate the reactivity of catalyst materials ~1000x faster than existing computational methods using AI. Demo
-
Meta AI Research Paper Wins ICML 2023 Outstanding Paper Award
By
–
We’re honored to share that Learning-Rate-Free Learning by D-Adaptation, a paper by Meta AI research scientist @aaron_defazio & @konstmish was selected as an #ICML2023 Outstanding Paper! More details and a link to the paper
-
Hiera: Hierarchical Vision Transformer without Bells-and-Whistles
By
–
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles https://
bit.ly/3OrVBW3 6/7 -
ICML 2023 Papers and Team Workshop Connections
By
–
Bonus: Want to see more of our ICML papers? Or where you can connect with our team at workshops if you're at the conference? More info on our #ICML2023 page https://
bit.ly/452XCgZ 7/7 -
Hyperbolic Image-Text Representations Research
By
–
Hyperbolic Image-Text Representations https://
bit.ly/3YlLC8h 5/7 -
Learnable Reward-Mixing MDPs with Few Latent Contexts
By
–
Reward-Mixing MDPs with Few Latent Contexts are Learnable https://
bit.ly/3KzS4Tt 3/7 -
ELSA: Efficient Label Shift Adaptation Semiparametric Models
By
–
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models https://
bit.ly/43EbRHU 2/7 -
Masked Trajectory Models for Prediction, Representation, and Control
By
–
Masked Trajectory Models for Prediction, Representation, and Control https://
bit.ly/43EMc1N 4/7