范德瓦尔斯磁性异构结构的数据驱动研究
Romakanta Bhattarai1, Peter Minch1, Trevor David Rhone1
1Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.
ACS applied materials & interfaces
|August 18, 2025
概括
我们使用数据驱动方法探索磁性范德瓦尔斯 (vdW) 异构结构. 我们的发现揭示了如何将磁性和非磁性VDW材料结合起来,可以调整先进应用的电子和磁性特性.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算材料科学科学 计算材料科学
背景情况:
- 磁性范德瓦尔斯 (vdW) 材料具有独特的物理特性,有可能改变半导体行业.
- 磁性vdW异构结构的现有研究,如MnBi2Te4/Sb2Te3,突出了它们的前景,但需要有效的设计策略.
研究的目的:
- 通过使用数据驱动的框架来研究AiAiiBi4Xi8/Bii4Xii6形式的磁性vdW异构结构.
- 探索如何结合磁性和非磁性VDW单层影响磁性特性和带间隙.
- 为加速新型VDW异构结构的发现,用于自旋电子学,光电子学和拓量子计算.
主要方法:
- 采用数据驱动框架,利用密度函数理论 (DFT) 生成的数据.
- 训练各种机器学习 (ML) 模型来预测大量异构结构的特性.
- 选了 16,431,660 个 AiAiiBi4Xi8/Bii4Xii6 的异构结构,以寻找有前途的候选人.
主要成果:
- 证明结合磁性AiAiiBi4Xi8和非磁性Bi4Xii6单层可以有效调整磁性特性和带间隙.
- 通过使用ML模型,成功预测了大量异构结构数据集的属性.
- 根据ML预测,确定了有前途的候选异构结构.
结论:
- 数据驱动的ML方法显著加快了磁性VDW异构结构的设计和发现.
- 研究的异构结构设计为先进的电子和自旋电子应用提供了量身定制材料的途径.
- 这项工作为开发新一代材料的基础奠定了基础,这些材料用于自旋电子学,光电子学和拓量子计算.
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