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Register a new userElucidating proximity magnetism through polarized neutron reflectometry and machine learning
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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 Bi$_2$Se$_3$/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)$_2$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.
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Introducing magnetic order into a topological insulator (TI) system has been attracting much attention with an expectation of realizing exotic phenomena such as quantum anomalous Hall effect (QAHE) or axion insulator states. The magnetic proximity effect (MPE) is one of the promising schemes to induce the magnetic order on the surface of TI without introducing disorder accompanied by doping magnetic impurities in TI. In this study, we investigate the MPE at the interface of a heterostructure consisting of a topological crystalline insulator (TCI) SnTe and Fe by employing polarized neutron reflectometry. The ferromagnetic order penetrates $sim$ 3 nm deep into the SnTe layer from the interface with Fe, which persists up to room temperature. Our findings demonstrate that the interfacial magnetism is induced by the MPE on the surface of TCI preserving the coherent topological states, which is essential for the bulk-edge correspondence, without introducing disorder arising from a random distribution magnetic impurities. This opens up a way for realizing next generation electronics, spintronics, and quantum computational devices by making use of the characteristics of TCI.
In this letter we report a direct observation of a magnetic proximity effect in an amorphous thin film exchange-spring magnet by the use of neutron reflectometry. The exchange-spring magnet is a trilayer consisting of two ferromagnetic layers with high $T_c$s separated by a ferromagnetic layer, which is engineered to have a significantly lower $T_c$ than the embedding layers. This enables us to measure magnetization depth profiles at which the low $T_c$ material is in a ferromagnetic or paramagnetic state, while the embedding layers are ferromagnetic. A clear proximity effect is observed 7 K above the $T_c$ of the embedded layer, with a range extending 50 $unicode{xC5}$.
We have investigated the variation in the magnetization of highly ordered pyrolytic graphite (HOPG) after neutron irradiation, which introduces defects in the bulk sample and consequently gives rise to a large magnetic signal. We observe strong paramagnetism in HOPG, increasing with the neutron fluence. We correlate the induced paramagnetism with structural defects by comparison with density-functional theory calculations. In addition to the in-plane vacancies, the trans-planar defects also contribute to the magnetization. The lack of any magnetic order between the local moments is possibly due to the absence of hydrogen/nitrogen chemisorption, or the magnetic order cannot be established at all in the bulk form.
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.
We experimentally demonstrate the existence of magnetic coupling between two ferromagnets separated by a thin Pt layer. The coupling remains ferromagnetic regardless of the Pt thickness, and exhibits a significant dependence on temperature. Therefore, it cannot be explained by the established mechanisms of magnetic coupling across nonmagnetic spacers. We show that the experimental results are consistent with the presence of magnetism induced in Pt in proximity to ferromagnets, in direct analogy to the well-known proximity effects in superconductivity.
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