机器学习辅助筛选具有 Dzyaloshinskii Moriya 相互作用的二维铬化铁磁材料

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-10-03 DOI:10.1038/s41524-024-01419-y
Peng Han, Jingtong Zhang, Shengbin Shi, Yunhong Zhao, Yajun Zhang, Jie Wang
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引用次数: 0

摘要

磁性天线因其固有的拓扑稳定性和纳米级尺寸而成为高密度存储和逻辑器件的潜在候选材料。二维(2D)Janus 过渡金属掺杂物(TMDs)由于打破了反转对称性而被广泛用于诱导天线。然而,Janus TMDs 的实验合成非常罕见,这表明 Janus 构型可能不是最稳定的 MXY 结构。在此,我们通过机器学习辅助的高通量第一性原理计算证明,并非所有的 MXY 化合物都能稳定地形成 Janus 层状结构,很大一部分化合物更倾向于形成比 Janus 构型能量更低的其他构型。有趣的是,这些新构型表现出很强的 Dzyaloshinskii-Moriya 相互作用(DMI),即使在强磁场下也能产生并稳定天膜。这项工作不仅为获得具有强 DMI 的铁磁材料提供了有效方法,还为通过实验合成 TMDs 提供了理论指导。
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Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction

Magnetic skyrmions are potential candidates for high-density storage and logic devices because of their inherent topological stability and nanoscale size. Two-dimensional (2D) Janus transition metal chalcogenides (TMDs) are widely used to induce skyrmions due to the breaking of inversion symmetry. However, the experimental synthesis of Janus TMDs is rare, which indicates that the Janus configuration might not be the most stable MXY structure. Here, through machine-learning-assisted high-throughput first-principles calculations, we demonstrate that not all MXY compounds can be stabilized in Janus layered structure and a large proportion prefer to form other configurations with lower energy than the Janus configuration. Interestingly, these new configurations exhibit a strong Dzyaloshinskii–Moriya interaction (DMI), which can generate and stabilize skyrmions even under a strong magnetic field. This work provides not only an efficient method for obtaining ferromagnetic materials with strong DMI but also a theoretical guidance for the synthesis of TMDs via experiments.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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