From density functional theory to machine learning predictive models for electrical properties of spinel oxides.

ABSTRACT: This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of

SUBMITTER: Elbaz Y 

PROVIDER: S-EPMC11637019 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature