论文标题
机器学习分析磁性隧道连接的隧道磁力磁力，MGAL2O4
Machine learning analysis of tunnel magnetoresistance of magnetic tunnel junctions with disordered MgAl2O4
论文作者
论文摘要
通过贝叶斯的优化以及最低的绝对收缩和选择算子（LASSO）技术与第一原理计算相结合，我们研究了Fe/无序MGAL2O4（MAO）/Fe（MAO）/Fe（001）磁力（001）磁性隧道连接（MTJS）的隧道磁磁性（TMR）效应，以确定较大的MAO TMR仪料的结构。通过贝叶斯优化具有1728个结构候选者，在300个结构计算中达到收敛性，获得了最大的TMR比率的最佳结构。获得的结构的表征表明，两个Al原子之间的平面距离在确定TMR比率方面起着重要作用。由于无序MAO的AL-AL距离显着影响复杂带结构的虚拟部分，因此Fe/无序mao/Fe MTJS中Δ1状态的大多数旋转电导随着平面al-Al距离的增加而增加，导致TMR比率较大。此外，我们发现，当[001]平面中Al，Mg和空位的数量的比率为2：1：1时，TMR比往往很大，这表明AL原子位置的控制对于增强MTJ中MTJ的TMR比率的控制至关重要。目前的工作揭示了材料信息学的有效性和优势，并在设计基于MTJ的高性能自旋设备中结合了第一原理传输计算。
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/disordered-MgAl2O4(MAO)/Fe(001) magnetic tunnel junctions (MTJs) to determine 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 material informatics combined with first-principles transport calculations in designing high-performance spintronic devices based on MTJs.
