论文标题
使用压缩传感和密度功能理论预测基于钴的啤酒啤酒化合物的磁矩的一般规则
A general rule for predicting the magnetic moment of Cobalt-based Heusler compounds using compressed sensing and density functional theory
论文作者
论文摘要
我们提出了一个一般规则，用于估计基于二$ 2 $（钴）的Heusler合金的磁矩，尤其是当掺入后期过渡金属时。我们想出了一个可以描述纯co $ _2 $ yz化合物和具有化学配方co $ _2 $ y $ y $ _ $ _ $ _ {1-x} $ m $ _x $ z $ _x $ z（m是掺杂剂）的掺杂的pure co $ _2 $ yz化合物（m是掺杂剂），使用在线数据使用Co $ _2 $ YZ结构和压缩的传感方法。新提出的描述符不仅取决于化合物的价电子数量，还取决于掺杂位点中未占用的D-电子的数量。对拟议的描述符的性能和Slater-Pauling规则进行了比较。与Slater-Pauling规则不同，该规则仅对半金属的Heusler化合物有效，我们的机器学习方法更为通用，因为它适用于任何CO $ _2 $ YZ YZ Heusler化合物，无论它们是否是半米。我们使用该新规则来估计一些尚未发现的赫斯勒化合物的磁矩，并将结果与基于密度功能理论（DFT）计算进行比较。最后，我们使用DFT和机器学习调查来证明它们的稳定性。
We propose a general rule for estimating the magnetic moments of Co$ 2$(cobalt)-based Heusler alloys, especially when doped with late transition metals. We come up with a descriptor that can characterise both pure Co$_2$YZ compounds and the doped ones with the chemical formula Co$_2$Y$_{1-x}$M$_x$Z (M is the dopant) using online data for magnetic moments of Heusler alloys with Co$_2$YZ structure and compressive sensing approach. The newly proposed descriptor not only depends on the number of valence electrons of the compound also it depends on the number of unoccupied d-electrons in the doping site. A comparison of the performance of the proposed descriptor and the Slater-Pauling rule is made. Unlike the Slater-Pauling rule, which is only effective for half-metallic Heusler compounds, our machine-learning approach is more generic since it applies to any Co$_2$YZ Heusler compounds, regardless of whether they are half-metals or not. We use this new rule to estimate the magnetic moments of a few yet-to-be-discovered Heusler compounds and compare the results to density functional theory (DFT) based calculations. Finally, we use DFT and machine learning investigations to prove their stability.
