Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation
A new machine learning approach that transforms time-series analysis into temperature-series analysis is introduced to analyze stress-induced ferroelectricity in SrTiO3 at 231 MPa using birefringence images observed at successive temperatures. The spatial distribution of the temperature-series data...
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Taylor & Francis Group
2024-12-01
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| Series: | Science and Technology of Advanced Materials: Methods |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27660400.2024.2342234 |
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| author | Hirotaka Manaka Kensei Toyoda Yoko Miura |
| author_facet | Hirotaka Manaka Kensei Toyoda Yoko Miura |
| author_sort | Hirotaka Manaka |
| collection | DOAJ |
| description | A new machine learning approach that transforms time-series analysis into temperature-series analysis is introduced to analyze stress-induced ferroelectricity in SrTiO3 at 231 MPa using birefringence images observed at successive temperatures. The spatial distribution of the temperature-series data for each of the 42,280 pixels was clustered using the multivariate [Formula: see text]-shape clustering method based on the shape similarity of the temperature dependence. In addition, to obtain the structural and ferroelectric phase transition temperatures, [Formula: see text] and [Formula: see text], hierarchical Bayesian temperature-series estimation was performed at each pixel (as a lower level) constrained over the entire cluster (as a higher level) considering the measurement error. Consequently, the K-shape clustering method revealed four clusters corresponding to elongated ferroelectric domains, explained by slight differences in retardance and fast-axis direction. Statistical analysis of the Bayesian posterior probability distribution showed a uniform distribution of [Formula: see text] over the sample, but an inhomogeneous distribution of [Formula: see text]. The higher [Formula: see text] regions exhibited a concentration of stress and/or strain. The Pearson correlation coefficient calculations suggested a strong to moderate relationship between the distribution of TF and the ferroelectric state, while the correlation between Tc and the ferroelectric state was weak or nonexistent. The combination of machine learning and statistics provides a more reliable and less arbitrary approach to analyzing temperature-series data. These multilevel analyses are particularly useful in studying critical phenomena near phase transition temperatures in condensed matter physics. |
| format | Article |
| id | doaj-art-f56492a1e60a4f0ab49a0eb85c23fd1c |
| institution | Kabale University |
| issn | 2766-0400 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | Science and Technology of Advanced Materials: Methods |
| spelling | doaj-art-f56492a1e60a4f0ab49a0eb85c23fd1c2024-12-10T09:58:05ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002024-12-014110.1080/27660400.2024.2342234Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimationHirotaka Manaka0Kensei Toyoda1Yoko Miura2Graduate School of Science and Engineering, Kagoshima University, Kagoshima, JapanGraduate School of Science and Engineering, Kagoshima University, Kagoshima, JapanNational Institute of Technology, Suzuka College, Suzuka, JapanA new machine learning approach that transforms time-series analysis into temperature-series analysis is introduced to analyze stress-induced ferroelectricity in SrTiO3 at 231 MPa using birefringence images observed at successive temperatures. The spatial distribution of the temperature-series data for each of the 42,280 pixels was clustered using the multivariate [Formula: see text]-shape clustering method based on the shape similarity of the temperature dependence. In addition, to obtain the structural and ferroelectric phase transition temperatures, [Formula: see text] and [Formula: see text], hierarchical Bayesian temperature-series estimation was performed at each pixel (as a lower level) constrained over the entire cluster (as a higher level) considering the measurement error. Consequently, the K-shape clustering method revealed four clusters corresponding to elongated ferroelectric domains, explained by slight differences in retardance and fast-axis direction. Statistical analysis of the Bayesian posterior probability distribution showed a uniform distribution of [Formula: see text] over the sample, but an inhomogeneous distribution of [Formula: see text]. The higher [Formula: see text] regions exhibited a concentration of stress and/or strain. The Pearson correlation coefficient calculations suggested a strong to moderate relationship between the distribution of TF and the ferroelectric state, while the correlation between Tc and the ferroelectric state was weak or nonexistent. The combination of machine learning and statistics provides a more reliable and less arbitrary approach to analyzing temperature-series data. These multilevel analyses are particularly useful in studying critical phenomena near phase transition temperatures in condensed matter physics.https://www.tandfonline.com/doi/10.1080/27660400.2024.2342234Temperature-series analysis-shape clusteringhierarchical Bayesian estimationbirefringence imagestress-induced ferroelectricitySrTiO3 |
| spellingShingle | Hirotaka Manaka Kensei Toyoda Yoko Miura Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation Science and Technology of Advanced Materials: Methods Temperature-series analysis -shape clustering hierarchical Bayesian estimation birefringence image stress-induced ferroelectricity SrTiO3 |
| title | Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation |
| title_full | Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation |
| title_fullStr | Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation |
| title_full_unstemmed | Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation |
| title_short | Multivariate temperature-series analysis of stress-induced ferroelectricity in SrTiO3: a machine learning approach with K-shape clustering and hierarchical Bayesian estimation |
| title_sort | multivariate temperature series analysis of stress induced ferroelectricity in srtio3 a machine learning approach with k shape clustering and hierarchical bayesian estimation |
| topic | Temperature-series analysis -shape clustering hierarchical Bayesian estimation birefringence image stress-induced ferroelectricity SrTiO3 |
| url | https://www.tandfonline.com/doi/10.1080/27660400.2024.2342234 |
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