Novel materials for spintronic devices require not only bulk electronic properties but also electronic structures compatible with magnetic/nonmagnetic interfaces. First-principles calculations have revealed the importance of electron band matching (EBM) at the Fermi energy for transport properties such as giant magnetoresistance (GMR). However, direct evaluation of ballistic conductance in GMR devices using first-principles calculations is computationally expensive and unsuitable for high-throughput screening. Here, we propose a machine learning framework to predict ballistic conductance using image representations of the Fermi surface similarity between magnetic and nonmagnetic materials. The images are decomposed into dictionary atoms, and the resulting feature vectors are subjected to regression analysis, with Gaussian process regression achieving the highest accuracy among the various models. Our results demonstrate that the proposed method effectively captures interfacial features essential for electron transport and provides a rapid means to screen material combinations with large EBM. This framework offers a practical step toward realizing material informatics for spintronics, enabling efficient exploration of heterogeneous material combinations for next-generation GMR devices.
Mizutori et al. (Wed,) studied this question.