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机器学习可以发现二维Invar和反Invar单层

Shun Tian^1,2, Ke Zhou³, Wanjian Yin¹

¹College of Energy, SIEMIS, Soochow University, Suzhou, China.

Nature communications

|August 14, 2024

概括

研究人员确定了机械特性,以对具有正或负热膨胀的二维晶体进行分类. 这一发现有助于发现零热膨胀 (ZTE) 2D Invar单层和大热膨胀 2D 反Invar单层.

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:

有正热膨胀 (PTE) 和负热膨胀 (NTE) 的材料很常见,但零热膨胀 (ZTE) 的材料很少见.
了解二维材料的热膨胀对于先进的电子技术至关重要.

研究的目的:

确定关键的机械描述符来分类二维晶体的热膨胀特性.
发现具有零,大正或大负热膨胀的新二维材料.

主要方法:

利用高通量计算来分析二维晶体的特性.
使用符号回归来识别预测性机械描述符.
计算各种二维单层的线性热膨胀系数 (LTEC).

主要成果:

确定了平面内拉伸刚度和平面外曲刚度作为PTE和NTE 2D晶体的关键描述符.
发现了ZTE 2D Invar单层,其 LTEC 在±2×10−6 K−1.1 范围内.
发现了具有很大的PTE和NTE (LTEC>±15×10−6 K−1) 的2D抗Invar单层.

结论:

机械描述符可以有效地分类和预测二维材料的热膨胀.
这些发现有助于为纳米尺度电子产品量身定制的热膨胀的二维材料的发现.
这项研究促进了对具有极端热膨胀特性的材料的理解.

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