Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

TL;DR

This paper introduces a machine learning framework using a variational autoencoder to interpret polarized neutron reflectometry data, enabling high-resolution parameter extraction and revealing interfacial magnetism in layered materials.

Contribution

The study develops a novel data-driven approach with a variational autoencoder to analyze reflectometry profiles, improving parameter retrieval and uncovering hidden interfacial magnetic phenomena.

Findings

Accurately recovered scattering length density profiles of heterostructures.

Identified possible interfacial proximity magnetism in topological insulator-antiferromagnet heterostructure.

Demonstrated good agreement with conventional fitting methods.

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 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.