Integrating density functional theory with machine learning for enhanced band gap prediction in metal oxides

Ezeakunne, Chidozie [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0009000493189195); Lamichhane, Bipin [Department of Physics, Florida A&M University, Tallahassee, FL 32307, USA] (ORCID:000000029503083X); Kattel, Shyam [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0000000258435889)

doi:10.1039/d4cp03397c

DOE OSTI · 2528123

Integrating density functional theory with machine learning for enhanced band gap prediction in metal oxides

Ezeakunne, Chidozie [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0009000493189195)·Lamichhane, Bipin [Department of Physics, Florida A&M University, Tallahassee, FL 32307, USA] (ORCID:000000029503083X)·Kattel, Shyam [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0000000258435889)

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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 .

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Ezeakunne, Chidozie [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0009000493189195), Lamichhane, Bipin [Department of Physics, Florida A&M University, Tallahassee, FL 32307, USA] (ORCID:000000029503083X), Kattel, Shyam [Department of Physics, University of Central Florida, Orlando, FL 32816, USA] (ORCID:0000000258435889). 2025-03-06. Integrating density functional theory with machine learning for enhanced band gap prediction in metal oxides. https://doi.org/10.1039/d4cp03397c

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