低成本的振动自由能量在固体溶液中,使用机器学习力场
Kasper Tolborg1,2,3, Aron Walsh2,4
1Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg Ø, Denmark.
The journal of physical chemistry letters
|December 15, 2023
概括
本研究介绍了一种低成本的方法,将振动纳入合金的集群扩张计算中. 这提高了预测阶段图的准确性,有助于材料设计.
科学领域:
- 计算材料科学 计算材料科学
- 热力学是一种热力学.
- 固态物理 固态物理
背景情况:
- 准确的相位图预测对于合金设计至关重要.
- 集群膨胀是失序晶体的一个关键方法.
- 由于计算成本高,振动效应往往被遗漏.
研究的目的:
- 开发一种计算成本低廉的方法,将振动自由能量纳入集群扩张中.
- 为了提高合金相位图的计算预测的准确性.
主要方法:
- 将机器学习力场 (MLFF) 安装到从集群扩张构建的放松轨迹上.
- 使用MLFF计算声子分散和振动自由能量.
- 将该方法应用于像Na1-xKxCl和Ag1-xPdx这样的 (伪) 二进制系统.
主要成果:
- 在MLFF的方法准确地捕捉振动属性.
- 包括振动效应显著改善了与实验混合性差距的协议.
- 在两个不同的合金系统中取得了成功.
结论:
- 开发的方法使得在相位图计算中常规包含振动效应.
- 这导致了对材料特性和稳定性的更准确的预测.
- 促进合金和固体溶液的合理设计.
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