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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2109.08005 |
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| _version_ | 1866913385307701248 |
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| author | Andrejevic, Nina Chen, Zhantao Nguyen, Thanh Fan, Leon Heiberger, Henry Zhou, Ling-Jie Zhao, Yi-Fan Chang, Cui-Zu Grutter, Alexander Li, Mingda |
| author_facet | Andrejevic, Nina Chen, Zhantao Nguyen, Thanh Fan, Leon Heiberger, Henry Zhou, Ling-Jie Zhao, Yi-Fan Chang, Cui-Zu Grutter, Alexander Li, Mingda |
| contents | Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$ and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_08005 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Elucidating proximity magnetism through polarized neutron reflectometry and machine learning Andrejevic, Nina Chen, Zhantao Nguyen, Thanh Fan, Leon Heiberger, Henry Zhou, Ling-Jie Zhao, Yi-Fan Chang, Cui-Zu Grutter, Alexander Li, Mingda Materials Science Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$ and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems. |
| title | Elucidating proximity magnetism through polarized neutron reflectometry and machine learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2109.08005 |