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Machine learning analysis of tunnel magnetoresistance of magnetic tunnel junctions with disordered MgAl2O4

Published in Phys. Rev. Researchhttps://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023187

Through Bayesian optimization and the least absolute shrinkage and selection operator (LASSO) technique combined with first-principles calculations, we investigated the tunnel magnetoresistance (TMR) effect of Fe/disorderedMgAl2O4(MAO)/Fe(001) magnetic tunnel junctions (MTJs) to determine the structures of disordered-MAO that give large TMR ratios. The optimal structure with the largest TMR ratio was obtained by Bayesian optimization with 1728 structural candidates, where the convergence was reached within 300 structure calculations. Characterization of the obtained structures suggested that the in-plane distance between two Al atoms plays an important role in determining the TMR ratio. Since the Al-Al distance of disordered MAO significantly affects the imaginary part of complex band structures, the majority-spin conductance of the Δ1 state in Fe/disordered-MAO/Fe MTJs increases with increasing in-plane Al-Al distance, leading to larger TMR ratios. Furthermore, we found that the TMR ratio tended to be large when the ratio of the number of Al, Mg, and vacancies in the [001] plane was 2:1:1, indicating that the control of Al atomic positions is essential to enhancing the TMR ratio in MTJs with disordered MAO. The present work reveals the effectiveness and advantage of materials informatics combined with first-principles transport calculations in designing high-performance spintronic devices based on MTJs.

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