The Open Catalyst Project is a collaborative research effort between Fundamental AI Research (FAIR) at Meta and Carnegie Mellon University's (CMU) Department of Chemical Engineering. The aim is to use AI to model and discover new catalysts for use in renewable energy storage to help in addressing climate change.

Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen. To be widely adopted, this process requires cost-effective solutions to running chemical reactions.

An open challenge is finding low-cost catalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (density functional theory), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of AI or machine learning may provide a method to efficiently approximate these calculations, leading to new approaches in finding effective catalysts.

To enable the broader research community to participate in this important project, we have released the Open Catalyst 2020 (OC20) and 2022 (OC22) datasets for training ML models. These datasets altogether contain 1.3 million molecular relaxations with results from over 260 million DFT calculations. In addition to the data, baseline models and code are open-sourced on our Github page. View the leaderboard to see the latest results and to submit your own to the evaluation server!