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NameChidozie Ezeakunne
Organization or InstitutionFlorida A&M University
TopicComputational Chemistry
Title

Integrating Density Functional Theory with Machine Learning for Enhanced Band Gap Prediction in Metal Oxides

Author(s)

Chidozie Ezeakunne, Bipin Lamichhane, and Shyam Kattel*

Author Institution(s)

Florida A&M University

Abstract

In this comprehensive study, we used a combination of density functional theory with Hubbard U correction (DFT+U) and machine learning (ML) to accurately predict the band gaps and lattice parameters of metal oxides: TiO2 (rutile and anatase), ZnO, ZnO2, CeO2, and ZrO2. The results show that including Up values for oxygen 2p orbitals alongside Ud/f for metal 3d or 4f orbitals significantly improves the band gap and lattice parameters predictions. Our extensive DFT+U calculations identified optimal (Up, Ud/f) pairs, which closely predicted the experimental value of band gap and lattice parameters: (8 eV, 8 eV) for rutile TiO2, (3 eV, 6 eV) for anatase TiO2, (6 eV, 12 eV) for ZnO, (10 eV, 10 eV) for ZnO2, (9 eV, 5 eV) for ZrO2 and (7 eV, 12 eV) for CeO2. The ML analysis shows that of all ML models included in the current study, polynomial regression reliably produces results with accuracy comparable to DFT calculations. Therefore, this study not only identifies the best (Up, Ud/f) pairs to predict experimentally measured band gap and lattice parameters but also highlights the effectiveness of a simple regression ML model in predicting band gaps of metal oxides.