ORNL and University of Tennessee researchers design an automation process for choosing optimal perovskites to improve solar technologies
Researchers at the Department of Energy's Oak Ridge National Laboratory (ORNL) and the University of Tennessee have proposed a way to automate the search for new materials, with a focus on metal halide perovskites (MHPs), to advance solar energy technologies. The study, part of an ORNL-UT Science Alliance collaboration, aims to identify the most stable MHP materials for device integration.
The team has developed a novel workflow that combines robotics and machine learning to study metal halide perovskites. 'Our approach speeds exploration of perovskite materials, making it exponentially faster to synthesize and characterize many material compositions at once and identify areas of interest,' said ORNL's Sergei Kalinin.