有效地确定固体的波恩有效电荷,LO-TO分裂和拉曼张量,使用实时空间以原子为中心的深度学习方法
Olivier Malenfant-Thuot1, Kevin Ryczko2,3,4, Isaac Tamblyn2,3
1Département de physique et Institut Courtois, Université de Montréal, Montréal, Québec, Canada.
概括
我们开发了实时空间原子分解网络 (radnet),这是一个深度神经网络,用于预测材料属性,如极化和介电电容率. 这个框架准确计算周期系的拉曼光谱.
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
- 计算化学的计算化学
背景情况:
- 对材料属性的准确预测对于发现新材料至关重要.
- 现有的机器学习模型对周期系统中的拉曼预测有局限性.
- 深度神经网络为推进材料建模提供了一个有希望的途径.
研究的目的:
- 介绍实时空间原子分解网络 (radnet),一个新的深度神经网络 (DNN) 框架.
- 解决当前机器学习模型在周期系统中的拉曼预测方面的局限性.
- 能够准确地预测固体中的极化和电子介电电容量张量.
主要方法:
- 在以原子为中心的框架内利用深层卷积神经网络.
- 应用自动区分,以有效计算材料属性.
- 集成的周期性边界条件和对称性用于通用预测.
主要成果:
- 在GaAs和BN中获得极化和介电电容的优异预测准确度.
- 成功计算了波恩有效电荷,LO-TO分裂频率和拉曼张量.
- 计算的拉曼光谱与初始结果有很强的一致性.
结论:
- 雷德网框架为预测关键材料特性提供了一个强大的工具.
- 证明了DNN能够准确地模拟周期系中的复杂现象的能力.
- 在考虑系统对称性时,为推广极化预测打开了新的可能性.
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