Machine-learning Surrogate Model Leads to Discovery of new Iridium-Oxide Catalysts | Center for Interface Science and Catalysis

June 18, 2020

A newly developed Active Learning (AL) algorithm (AL_IrO3_Anim (1).html) was used to search the bulk-polymorphic space of the Ir-Oxides. Using this approach we are able to find 196 IrO2 polymorphs and 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable,
termed α-IrO3. This project was funded under TRI and SUNCAT-FWP. Please follow this link to see the algorithm in motion:

AL-ML Movie of IrO3 Algorithm

10.1021/acs.chemmater.0c01894

Raul Abram Flores Jr., Christopher Paolucci, Kirsten Winther, Ankit Jain, Jose Antonio Garrido Torres, Joseph H. Montoya, Jens K. Nørskov, Michal Bajdich, Thomas Bligaard

Publications:

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction

Research Areas:

Theoretical method development, Catalysis informatics, Electrocatalysis

Scientific Highlights