Finding the right combination of catalysts is a time-consuming process. There are billions of possible combinations of elements to try. Experimentalists using standard synthesis methods can try 10 materials per day, while a modern computational laboratory using quantum mechanical simulation tools such as density functional theory (DFT) can run 40,000 simulations per year. The goal of Open Catalyst is to enable researchers to screen millions and maybe even billions of possible catalysts per year.

The Open Catalyst team uses a comparatively small number of DFT calculations to train more efficient ML models on the fundamental physics governing quantum mechanics to learn how to predict the forces exerted on atoms by each other and the overall system energy. Focusing on materials and molecules that are important in renewable energy applications, the OC20 dataset is the largest dataset of electrocatalyst structures to date and as such should lead to significant improvements in ML models, specifically in their ability to generalize and learn the underlying physics governing molecules at inorganic interfaces. It also opens the door to predicting reaction selectivity across catalyst composition.

C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Johannes Klicpera, Adeesh Kolluru, Janice Lan, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Ammar Rizvi, Alex Schneidman, Nima Shoghi, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Richard Tran, Brook Wander, Brandon Wood, Junwoong Yoon, Jure Zbontar, Devi Parikh, Zachary Ulissi