机器学习用于在压力工程四级III-V半导体中加速带隙预测
Badal Mondal1,2, Julia Westermayr1,3, Ralf Tonner-Zech1
1Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig, 04103 Leipzig, Germany.
The Journal of chemical physics
|September 8, 2023
概括
我们开发了一种机器学习模型来预测四级III-V半导体的带隙特性. 这种方法可以有效地虚拟选用于光电子应用的新材料.
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
- 计算化学计算化学
背景情况:
- 四级III-V半导体对于光电子非常重要.
- 带隙属性 (大小和字符) 决定了设备的性能.
- 这些材料的实验选往往是不切实际的.
研究的目的:
- 开发一种计算效率高的方法,用于预测四级III-V半导体的带隙特性.
- 为了能够快速虚拟选各种各样的材料组成和菌株.
- 加速用于光电子应用的新材料的发现.
主要方法:
- 使用第一原则计算与机器学习相结合.
- 在带隙大小和字符数据上训练机器学习模型.
- 该模型的输入特征包括材料组成和应变.
- 实现了密度函数理论 (DFT) 的准确性.
主要成果:
- 开发了一个高度准确的机器学习模型用于带隙预测.
- 该模型有效地根据构成和应变预测带隙特性.
- 证明了在众多材料组成和应变值中进行计算高效预测的能力.
结论:
- 开发的机器学习方法为四级III-V半导体的虚拟选提供了一个强大的工具.
- 这种方法显著加速了对光电子设备有前途的材料的识别.
- 为材料设计提供了广泛的构成和应变空间的探索.
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