Carnegie Mellon University researchers team with Facebook AI Research to discover novel catalysts

October 20, 2020 |

In Pennsylvania, researchers from Carnegie Mellon University have teamed up with Facebook AI Research (FAIR) to break down one of these barriers. Combining the expertise in machine learning, dataset development, and computing power of FAIR with years of prior research using machine learning techniques for novel catalyst discovery, the team is empowering researchers around the world to join in the effort through the Open Catalyst Project, an open effort to produce datasets and models designed to help researchers discover new catalysts for renewable energy storage.

Current methods for converting renewable energy into other fuels requires catalysts that are often incredibly expensive and fairly inefficient, such as platinum. In order to reduce the cost and increase the efficiency of these processes, new catalysts will have to be discovered and implemented. But discovering new catalysts is an arduous and costly undertaking. Catalytic surfaces are made using a combination of several elements known to be effective for these purposes. There are 55 of these elements in the current dataset alone, with nearly 10,000 possible combinations. Add to that the fact that different ratios and configurations of these elements also have an effect, and the possibilities expand into the billions.

The Open Catalyst Project was created to facilitate the rapid exploration of all these billions of possibilities. As the first step in the project, they have created the Open Catalyst 2020 (OC20) dataset, an open source database containing molecular data on more than 1.3 million electrocatalyst relaxations across chemistry and catalysts-the largest dataset of its kind in the world. It’s essentially a catalogue of data on molecules known to be important for renewable energy applications, which will allow machine learning algorithms to quickly test millions of possible combinations, and eventually discover more efficient and inexpensive electrocatalysts.

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