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This content will become publicly available on March 6, 2026

Title: Integrating density functional theory with machine learning for enhanced band gap prediction in metal oxides
In this 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), cubic ZnO, cubic ZnO2, cubic CeO2, and cubic ZrO2. Our results show that including Up values for oxygen 2p orbitals alongside Ud/f for metal 3d or 4f orbitals significantly enhances the accuracy of these predictions. Through extensive DFT+U calculations, we identify optimal (Up, Ud/f) integer pairs that closely reproduce experimentally measured band gaps and lattice parameters for each oxide: (8 eV, 8 eV) for rutile TiO2; (3 eV, 6 eV) for anatase TiO2; (6 eV, 12 eV) for c-ZnO; (10 eV, 10 eV) for c-ZnO2; (9 eV, 5 eV) for c-ZrO2; and (7 eV, 12 eV) for c-CeO2. Our ML analysis showed that simple supervised ML models can closely reproduce these DFT+U results at a fraction of the computational cost and generalize well to related polymorphs. Our approach builds on existing high-throughput DFT+U frameworks by providing fast pre-DFT estimates of structural properties and band gaps. Since this work does not aim to improve the underlying DFT+U method, the ML model shares its limitations. We also note that the reported values of Up strongly depend on the choice of correlated orbitals, and caution is recommended with a different choice of correlated orbitals.  more » « less
Award ID(s):
2503612 2055012
PAR ID:
10584845
Author(s) / Creator(s):
; ;
Publisher / Repository:
Royal Society of Chemistry
Date Published:
Journal Name:
Physical Chemistry Chemical Physics
Volume:
27
Issue:
10
ISSN:
1463-9076
Page Range / eLocation ID:
5338 to 5358
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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