Researchers design deep learning model ensembles to investigate the magnetic behavior of perovskite oxide multilayers
Yayoi Takamura, professor and chair of materials science and engineering at the University of California, Davis, and researchers at Lawrence Livermore National Laboratory (LLNL) have designed deep-learning model ensembles, a method in machine learning involving multiple neural networks, to investigate the magnetic behavior of perovskite oxide multilayers.
Perovskite oxides are gaining attention for use in next-generation magnetic and ferroelectric devices due to their exceptional charge transport properties and the opportunity to tune the different properties of electrons and atoms, including charge, spin, lattice and orbital degrees of freedom. While the materials may offer a pathway for innovative designs in perovskite oxide-based devices, the atomic-level compositions of the interfaces between perovskite oxides are unknown, therefore hindering the establishment of design principles using these materials. With this new model, the researchers investigated the effects of composition and process parameters on the magnetic behavior of perovskite oxide multilayers.