title: Machine learning bandgaps of double perovskites.
abstract: The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.
authors.affiliation: Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos 87545, NM, USA.
authors.forename: G
authors.initials: G
authors.lastname: Pilania
authors.affiliation: Department of Materials Science &Engineering and Institute of Materials Science, University of Connecticut, Storrs, 06269 CT USA.
authors.forename: A
authors.initials: A
authors.lastname: Mannodi-Kanakkithodi
authors.affiliation: Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos 87545, NM, USA.
authors.forename: B P
authors.initials: BP
authors.lastname: Uberuaga
authors.affiliation: Department of Materials Science &Engineering and Institute of Materials Science, University of Connecticut, Storrs, 06269 CT USA.
authors.forename: R
authors.initials: R
authors.lastname: Ramprasad
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos 87545, NM, USA.
authors.forename: J E
authors.initials: JE
authors.lastname: Gubernatis
authors.affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos 87545, NM, USA.
authors.forename: T
authors.initials: T
authors.lastname: Lookman