Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
Abstract
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 for 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 Bi2Se3/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)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2022
- Source ID
- 10.1063/5.0078814
Entities
People
- Alexander J. Grutter
- Cui-Zu Chang
- Henry Heiberger
- Leon Fan
- Lingjie Zhou
- Mingda Li
- Nina Andrejevic
- Thanh Nguyen
- Yi-Fan Zhao
- Zhantao Chen
Organizations
- Massachusetts Institute of Technology
- National Institute of Standards and Technology
- National Science Foundation
- Pennsylvania State University
- United States Department of Energy