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Elucidating Proximity Magnetism through Polarized Neutron Reflectometry and Machine Learning

Published

Author(s)

Nina Andrejevic, Zhantao Chen, Thanh Nguyen, Leon Fan, Henry Heiberger, Valeria Lauter, Ling-Jie Zhou, Yi-Fan Zhao, Cui-Zu Chang, Alexander Grutter, Mingda Li

Abstract

Polarized neutron reflectometry (PNR) 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 PNR 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 (TI)-ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging PNR profile of the TI-antiferromagnet heterostructure (Bi,Sb)2Te^3/Cr2O3 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.
Citation
Nature Communications

Keywords

Machine Learning, Neutron, Reflectometry

Citation

Andrejevic, N. , Chen, Z. , Nguyen, T. , Fan, L. , Heiberger, H. , Lauter, V. , Zhou, L. , Zhao, Y. , Chang, C. , Grutter, A. and Li, M. (2022), Elucidating Proximity Magnetism through Polarized Neutron Reflectometry and Machine Learning, Nature Communications (Accessed July 6, 2022)
Created April 13, 2022