Unveiling Rare Earth dopant configurations and crystal field analysis in tetragonal BaTiO3 via Machine Learning and Superposition Modeling

Osman Yigit; Muhammed Acikgoz

doi:10.1039/d5tc03420e

January 1, 2026Journal of Materials Chemistry C

Unveiling Rare Earth dopant configurations and crystal field analysis in tetragonal BaTiO3 via Machine Learning and Superposition Modeling

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Key Points

The research aims to explore the effects of rare earth doping and crystal field interactions in barium titanate using computational techniques.
Employs machine learning algorithms to analyze dopant configurations.
Utilizes superposition modeling to assess crystal field effects.
Focuses on tetragonal barium titanate structure.
Identified specific configurations of rare earth dopants that enhance material properties.
Demonstrated significant changes in electronic structures due to crystal field effects.
Provided insights into the potential applications in advanced electronic devices.

Implication

Analysis unveils dopant configurations and crystal field dynamics in barium titanate, suggesting advancements in electronic applications.

Abstract

Barium titanate (BaTiO3) is a model ferroelectric perovskite with enduring technological relevance in electronics, catalysis, and energy storage. Rare-earth (RE) doping provides a powerful strategy to engineer its structural, electronic,...

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Cite This Study

Yigit et al. (2026) studied this question.

synapsesocial.com/papers/698d6dd15be6419ac0d530ec https://doi.org/10.1039/d5tc03420e

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Unveiling Rare Earth dopant configurations and crystal field analysis in tetragonal BaTiO3 via Machine Learning and Superposition Modeling

Key Points

Implication

Abstract

Discussion

Cite This Study

Unveiling Rare Earth dopant configurations and crystal field analysis in tetragonal BaTiO3 via Machine Learning and Superposition Modeling

Key Points

Implication

Abstract

Discussion

Cite This Study

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