Computational screening of magnetocaloric alloys
Computational screening of magnetocaloric alloys
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DOI：
10.1103/physrevmaterials.4.024402
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发表时间：
2020-02-04
影响因子：
3.4
通讯作者：
Seshadri, Ram
中科院分区：
文献类型：
--
作者：
Garcia, Christina A. C.;Bocarsly, Joshua D.;Seshadri, Ram
在过去的几十年里，一个令人兴奋的发展一直是使用高通量计算筛选作为一种手段，确定有前途的候选材料的各种结构或功能特性。实验上，经常发现性能最高的材料含有大量的原子位置无序。然而，这些在高通量计算搜索中经常被忽视，这是由于难以处理不具有简单、定义明确的晶体学晶胞的材料。在这里，我们证明了磁热材料的筛选与密度泛函理论为基础的磁变形代理的帮助下，可以扩展到原子位置无序的系统。这是通过对固溶体中有序超晶胞的磁变形进行热力学平均来实现的。我们表明，高度非单调磁热性能的无序固溶体Mn（Co 1-xFex）Ge和（Mn 1-xNix）CoGe的成功捕获使用这种方法。
An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it is often found that the highest-performing materials contain substantial atomic site disorder. These are frequently overlooked in high-throughput computational searches, however, due to difficulties in dealing with materials that do not possess simple, well-defined crystallographic unit cells. Here we demonstrate that the screening of magnetocaloric materials with the help of the density-functional-theory-based magnetic deformation proxy can be extended to systems with atomic site disorder. This is accomplished by thermodynamic averaging of the magnetic deformation for ordered supercells across a solid solution. We show that the highly nonmonotonic magnetocaloric properties of the disordered solid solutions Mn(Co1-xFex)Ge and (Mn1-xNix)CoGe are successfully captured using this method.