Advancements to renewable energy processes are urgently needed to address climate change and energy scarcity around the world. Many of these processes, including the generation of electricity through fuel cells or fuel generation from renewable resources are driven through chemical reactions. The design of new catalysts for enabling new and more efficient reactions is a critical bottleneck in developing cost-effective solutions. Unfortunately, the discovery of new catalyst materials is limited due to the high cost of computational atomic simulations and experimental studies. Machine learning has the potential to significantly reduce the cost of computational simulations by orders of magnitude. By filtering potential catalyst materials based on these simulations, candidates of higher promise may be selected for experimental testing and the rate at which new catalysts are discovered could be greatly increased. The Open Catalyst Challenge invites participants to submit results of machine learning models that simulate the interaction of a molecule on a catalyst's surface. ML models may either directly predict the relaxed state atomic configuration of the entire molecule + catalyst system, or iteratively predict and integrate per-atom forces to simulate how atoms will move around starting from an arbitrary initial state. By predicting this interaction accurately, the catalyst's impact on the overall rate of a chemical reaction may be estimated; a key factor in filtering potential catalysis materials and addressing the world’s energy needs. The Open Catalyst Project is a collaborative research effort between Facebook AI Research (FAIR) 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.