Integrating density functional theory with machine learning for enhanced band gap prediction in metal oxides
Chidozie Ezeakunne, Bipin Lamichhane, Shyam Kattel
Abstract
In this study, we used a combination of density functional theory and machine learning (ML) to accurately predict the band gaps and lattice parameters of metal oxides: TiO 2 (rutile and anatase), cubic ZnO, cubic ZnO 2 , cubic CeO 2 , and cubic ZrO 2 .
Topics & Concepts
Density functional theoryMetalBand gapMaterials scienceNanotechnologyComputational chemistryChemistryOptoelectronicsMetallurgyMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesCatalytic Processes in Materials Science