Machine learning potentials have shown considerable success in accelerating quantum mechanical calculations for computational catalysis applications. These applications are often focused on a particular chemistry or narrow combination of elements, limiting their ability to screen new catalysts. The Open Catalyst Project aims to develop new ML methods and models to accelerate the catalyst simulation process for renewable energy technologies and improve our ability to predict properties across catalyst composition. The initial release of the Open Catalyst 2020 (OC20) dataset presented the largest open dataset of thermochemical intermediates across multi-metallic surfaces, spanning 55 unique elements and 80 reaction intermediates. Since then, the development of state-of-the-art models and methods has been well underway by ourselves, collaborators, and members of both the catalysis and ML communities. I will introduce the Open Catalyst Project, the associated OC20 dataset, and recent updates since our initial release. Specifically I will discuss some of the progress in building state-of-the-art models and common challenges faced along the way.
