Zachary Ulissi and his team developed a machine learning system to search through millions of intermetallics to discover new materials for electrocatalysis.
Typically, catalysts are discovered through trial and error coupled with chemical intuition. Now, an automatic machine-learning framework has been developed that can guide itself to fnd intermetallic surfaces with desired catalytic properties.
Through their 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.