论文标题
部分可观测时空混沌系统的无模型预测
Electric field tunable multi-state tunnel magnetoresistances in 2D van der Waals magnetic heterojunctions
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Magnetic tunnel junction (MTJ) based on van der Waals (vdW) magnetic layers has been found to present excellent tunneling magnetoresistance (TMR) property, which has great potential applications in field sensing, non-volatile magnetic random access memories, and spin logics. Although MTJs composed of multilayer vdW magnetic homojunction have been extensively investigated, the ones composed of vdW magnetic heterojunction is still to be explored. Here we use first-principles approaches to reveal that the magnetic heterojunction MTJs have much more distinguishable TMR values than the homojunction ones. In the MTJ composed of bilayer CrI3/bilayer Cr2Ge2Te6 heterojunction, we find there are eight stable magnetic states, leading to six distinguishable electronic resistances. As a result, five sizable TMRs larger than 300% can be obtained (the maximum TMR is up to 620,000%). Six distinguishable memories are obtained which is two times larger than that of a four-layered homojunction MTJ. The underlying relationships among magnetic state, spin-polarized band structures, and transmission spectrums are further revealed to explain the multiple TMR values. We also find that the magnetic states and thus TMRs can be efficiently modulated by an external electric field. This study opens an avenue to the design of high-performance MTJ devices based on vdW heterojunctions.
