Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry (PNR) facilitates structural characterization of multilayered materials with depth sensitivity, enabling the study of interfacial phenomena such as the magnetic proximity effect, a promising pathway for magnetizing topological insulators (TIs) and advancing TI-based device applications. However, PNR profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. Here, we develop an alternate, data-driven framework to retrieve the parameters of candidate proximity-coupled systems from their PNR profiles with minimal user intervention. Using a variational autoencoder, we map PNR profiles to a low-dimensional latent space from which the true sample parameters can be readily obtained. The decoded profiles directly inform the suitability of the parameter space through the reconstruction quality and are robust to moderate perturbation of the inputs. Importantly, we find that the latent mapping naturally bypasses the issue of multiple local minima, and is both well-organized and visually interpretable in terms of physical parameters. We evaluate our model by recovering the sample parameters from experimental PNR profiles of two candidate proximity-coupled systems.