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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.04536 |
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| _version_ | 1866915231441092608 |
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| author | Du, Guanshihan Yao, Yuanyuan Zhou, Linming Huang, Yuhui Tanwani, Mohit Tian, He Chen, Yu Song, Kaishi Li, Juan Gao, Yunjun Das, Sujit Wu, Yongjun Chen, Lu Hong, Zijian |
| author_facet | Du, Guanshihan Yao, Yuanyuan Zhou, Linming Huang, Yuhui Tanwani, Mohit Tian, He Chen, Yu Song, Kaishi Li, Juan Gao, Yunjun Das, Sujit Wu, Yongjun Chen, Lu Hong, Zijian |
| contents | Ferroelectric oxide superlattices with complex topological structures such as vortices, skyrmions, and flux closure domains have garnered significant attention due to their fascinating properties and potential applications. However, progress in this field is often impeded by challenges such as limited data-sharing mechanisms, redundant data generation efforts, high barriers between simulations and experiments, and the underutilization of existing datasets. To address these challenges, we have created the Polar Topological Structure Toolbox and Database(PTST). This community driven repository compiles both standard datasets from high throughput phase field simulations and user submitted nonstandard datasets. The PTST utilizes a Global Local Transformer (GL Transformer) to classify polarization states by dividing each sample into spatial sub blocks and extracting hierarchical features, resulting in ten distinct topological categories. Through the PTST web interface, users can easily retrieve polarization data based on specific parameters or by matching experimental images. Additionally, a Binary Phase Diagram Generator allows users to create strain and electric field phase diagrams within seconds. By providing ready-to-use configurations and integrated machine-learning workflows, PTST significantly reduces computational load, streamlines reproducible research, and promotes deeper insights into ferroelectric topological transitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PTST: A polar topological structure toolkit and database Du, Guanshihan Yao, Yuanyuan Zhou, Linming Huang, Yuhui Tanwani, Mohit Tian, He Chen, Yu Song, Kaishi Li, Juan Gao, Yunjun Das, Sujit Wu, Yongjun Chen, Lu Hong, Zijian Materials Science Mesoscale and Nanoscale Physics Ferroelectric oxide superlattices with complex topological structures such as vortices, skyrmions, and flux closure domains have garnered significant attention due to their fascinating properties and potential applications. However, progress in this field is often impeded by challenges such as limited data-sharing mechanisms, redundant data generation efforts, high barriers between simulations and experiments, and the underutilization of existing datasets. To address these challenges, we have created the Polar Topological Structure Toolbox and Database(PTST). This community driven repository compiles both standard datasets from high throughput phase field simulations and user submitted nonstandard datasets. The PTST utilizes a Global Local Transformer (GL Transformer) to classify polarization states by dividing each sample into spatial sub blocks and extracting hierarchical features, resulting in ten distinct topological categories. Through the PTST web interface, users can easily retrieve polarization data based on specific parameters or by matching experimental images. Additionally, a Binary Phase Diagram Generator allows users to create strain and electric field phase diagrams within seconds. By providing ready-to-use configurations and integrated machine-learning workflows, PTST significantly reduces computational load, streamlines reproducible research, and promotes deeper insights into ferroelectric topological transitions. |
| title | PTST: A polar topological structure toolkit and database |
| topic | Materials Science Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2504.04536 |