Making the Bridge Between Experiment and Theory in Metal Oxides for Renewable Energy: Based on TiO2, ZnO, and BiVO4

Metal oxides such as TiO<sub>2</sub>, ZnO, and BiVO<sub>4</sub> have emerged as pivotal materials for renewable energy technologies owing to their versatile electronic, optical, and catalytic properties. This review highlights the importance of bridging experimental investigations with Density Functional Theory (DFT) to deepen the understanding of structure-property relationships in these systems. Experimental approaches provide critical insights into synthesis strategies, performance evaluation, and sustainability, whereas DFT offers predictive power at the atomic scale by elucidating electronic structures, reaction mechanisms, and defect dynamics. The synergy of these methods enables the rational design of advanced materials for photocatalysis, solar cells, and energy storage applications. Looking ahead, research opportunities lie in the development of doped and heterostructured metal oxides, the integration of machine learning for accelerated material discovery, and the implementation of in situ/operando studies that capture time-resolved phenomena. By making the bridge between experiment and theory, significant progress can be achieved toward sustainable and efficient energy solutions.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2026-02-24
DOI
https://doi.org/10.3390/ijms27052087
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Making the Bridge Between Experiment and Theory in Metal Oxides for Renewable Energy: Based on TiO2, ZnO, and BiVO4

Habtamu Fekadu Etefa, Francis B. Dejene
International Journal of Molecular Sciences
Machine Learning in Materials Science
article

Making the Bridge Between Experiment and Theory in Metal Oxides for Renewable Energy: Based on TiO2, ZnO, and BiVO4

Habtamu Fekadu Etefa, Francis B. Dejene
article en

Abstract

Metal oxides such as TiO<sub>2</sub>, ZnO, and BiVO<sub>4</sub> have emerged as pivotal materials for renewable energy technologies owing to their versatile electronic, optical, and catalytic properties. This review highlights the importance of bridging experimental investigations with Density Functional Theory (DFT) to deepen the understanding of structure-property relationships in these systems. Experimental approaches provide critical insights into synthesis strategies, performance evaluation, and sustainability, whereas DFT offers predictive power at the atomic scale by elucidating electronic structures, reaction mechanisms, and defect dynamics. The synergy of these methods enables the rational design of advanced materials for photocatalysis, solar cells, and energy storage applications. Looking ahead, research opportunities lie in the development of doped and heterostructured metal oxides, the integration of machine learning for accelerated material discovery, and the implementation of in situ/operando studies that capture time-resolved phenomena. By making the bridge between experiment and theory, significant progress can be achieved toward sustainable and efficient energy solutions.

International Journal of Molecular SciencesVol. 27(5)
Walter Sisulu University (ZA)
Openalex Percentile: Top 47%
Machine Learning in Materials Science
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