"Through this collaboration, we're breaking new ground in material discovery. It marks a significant leap in our ability to predict and validate materials that are critical for clean energy solutions. The results we're seeing with electrocatalysts demonstrate the real-world potential of AI in addressing urgent climate challenges," says Larry Zitnick, Research Director at Meta AI.
Electrocatalysts play a vital role in decarbonising industries and achieving global climate targets, particularly in clean energy processes such as carbon dioxide reduction reactions (CO2RR), hydrogen production, and the development of next-generation batteries.
To speed up the discovery of these catalysts, Meta's FAIR team has been developing AI models that can identify suitable candidates for energy conversion processes in a matter of hours rather than months.
To address this gap and fast-track the transition from discovery to manufacturing, VSP, Meta, and the University of Toronto (UoT) collaborated to test datasets of hundreds of unique and diverse materials in the lab, creating an open-source experimental catalyst database.
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