Zachary Ulissi developed a machine learning system to discover new materials for electrocatalysis

Zachary Ulissi and his team developed a machine learning system to search through millions of intermetallics to discover new materials for electrocatalysis.

While a human could study roughly 10 to 20 new energies a week, the machine can study hundreds per day. Prior to the automated system, researchers would have to narrow the space down to one class of materials and work in that space. Now, they can take a more holistic approach. Ulissi’s team has created a system that automates these routine calculations, explores a large search space, and suggests new alloys that have promising properties for electrocatalysis.

Through this study, published in Nature Catalysis, they have a list of materials and intermetallic combinations that experimentalists should try, both for hydrogen evolution and carbon dioxide reduction. The experiments will then determine what will make good electrocatalysts for the large scale.

 Written by Alexandra George

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The paper can be found here