Measuring the Spin Polarization with a Superconducting Point Contact and Machine Learning

Measuring spin polarization (P) of materials is essential for understanding their fundamental properties and for their application in spintronics. Point contact Andreev reflection (PCAR) spectroscopy is a straightforward yet powerful technique for measuring P. However, conventional analysis methods depend on iterative fitting procedures that are time-consuming, subjective, and often lead to non-unique solutions. This complexity arises from the interplay of multiple physical parameters with pressure, including temperature, superconducting gap, and interfacial barrier strength. Here, we present a machine learning (ML) approach that utilizes convolutional neural networks (CNNs) to facilitate the rapid and automated extraction of P from PCAR spectra. We validate the ML model by analyzing experimental PCAR spectra from various materials reported in the literature. The predicted parameters by the CNN model show excellent agreement with the literature values, demonstrating its robust performance across a wide range of materials and parameter sets. This approach significantly reduces analysis time while maintaining accuracy, providing a practical tool for material characterization, thus accelerating materials discovery for spintronics.