Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties

TL;DR

This paper develops a Bayesian optimization method with active learning to efficiently identify alloy compositions in the Ta-Nb-Hf-Zr-Ti system that exhibit high spin Hall conductivity and spin Hall angle, accelerating materials discovery.

Contribution

It introduces a multi-objective Bayesian optimization approach with active learning tailored for complex alloy systems to optimize spin transport properties.

Findings

Achieved target compositions with high SHC (~-2.0×10$^{-3}$ Ω$^{-1}$cm$^{-1}$) and SHA (~0.03) within less than 5 iterations.

Confirmed that spin Hall conductivity is mainly due to extrinsic skew scattering.

Demonstrated the method's potential for rapid materials discovery in vast chemical spaces.

Abstract

Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10$^{-3}$ $\Omega$ cm)$^{-1}$) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space.