Investigating magnetic van der Waals materials using data-driven approaches
Investigating magnetic van der Waals materials using data-driven approaches
复制标题
使用数据驱动的方法研究磁性范德华材料
DOI：
10.1039/d3tc00001j
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发表时间：
2023
影响因子：
6.4
通讯作者：
Rhone, Trevor David
中科院分区：
文献类型：
--
作者：
Bhattarai, Romakanta;Minch, Peter;Rhone, Trevor David
在这项工作中，我们调查的形式AiAliB4X8的磁性单层的基础上，著名的内禀拓扑磁性货车德瓦尔斯（VDW）材料MnBi2Te4（MBT）使用第一性原理计算和机器学习技术。我们选择一个初始的结构子集来计算热力学性质，电子性质，如带隙，和磁性，如磁矩和磁序使用密度泛函理论（DFT）。数据分析方法用于深入了解材料特性的微观起源。还探讨了材料的性质对化学成分的依赖性。例如，我们发现，形成能和磁矩在很大程度上取决于A和B网站，而带隙取决于所有三个网站。最后，我们采用机器学习工具来加速在MBT系列中寻找具有优化性能的新型vdW磁体。这项研究为快速预测具有理想特性的新材料创造了途径，这些材料可以在自旋电子学，光电子学和量子计算中应用。
In this work, we investigate magnetic monolayers of the form AiAiiB4X8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi2Te4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. This study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.