Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric conversion by Bayesian optimization

Abstract

We demonstrated the effectiveness of the machine learning method combined with first-principles calculations for the enhancement of the anomalous Nernst effect (ANE) of multilayers. The composition ratio of CoNi homogeneous alloy superlattices was optimized by Bayesian optimization so as to maximize the transverse thermoelectric conductivity (αxy). The nonintuitive optimal composition with a large αxy of ∼10 A K−1 m−1 was identified through the two-step Bayesian optimization using rough and fine candidate pools. The Berry curvature and band dispersion analyses revealed that αxy is enhanced by the appearance of the flat band near the Fermi level due to the multilayer formation. The magnitude of the energy derivative of the anomalous Hall conductivity increases owing to the large Berry curvature near the flat band along the R-M high symmetry line, which emerges only in the optimized superlattice, leading to the αxy enhancement. The effective method verified here will broaden the choices of ANE materials to more complex systems and, therefore, lead to the development of transverse thermoelectric conversion technologies.