在金属合金中量化化学短程顺序
Killian Sheriff1, Yifan Cao1, Tess Smidt2
1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
这项研究引入了一种使用机器学习的新型多尺度方法,用于量化金属合金中的化学短程顺序 (SRO). 该方法准确地预测SRO长度尺度,并将其与格子扭曲相关联.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 合金物理 合金物理
背景情况:
- 金属合金形成固体溶液,其中的元素占据了水晶格子的位置.
- 化学短程顺序 (SRO) 描述了合金中元素的非随机排列.
- 在像高合金这样的复杂合金中量化SRO是具有挑战性的,因为其配置复杂.
研究的目的:
- 在原子分辨率下开发和介绍一种用于预测和量化合金中SRO的多尺度方法.
- 为了弥合电子结构计算和SRO特征的长度尺度之间的差距.
- 为了将SRO与基本材料特性 (如局部格子扭曲) 相关联.
主要方法:
- 一种集成机器学习技术的多规模计算方法.
- 使用电子结构计算来告知SRO预测.
- 开发一种方法来量化SRO长度尺度和空间分布.
主要成果:
- 开发的方法准确地预测SRO长度尺度,与实验测量保持一致.
- 建立了SRO和基本量之间的全面相关性,包括局部格子扭曲.
- 证明了在原子分辨率下量化SRO的能力.
结论:
- 这种新方法提高了在固体溶液阶段对SRO的定量理解.
- 这项工作使SRO长度尺度能够严格纳入预测性机械和热力学模型.
- 方便设计和优化具有量身定制性能的先进金属合金.
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