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Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties. (arXiv:2309.04168v1 [cond-mat.mtrl-sci])

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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.

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