在使用机器学习的X射线衍射数据中解热力学效应
Rachel E Lim1, Shun Li Shang1, Chihpin Chuang2
1Pennsylvania State University, University Park, PA 16802, USA.
Acta crystallographica. Section A, Foundations and advances
|January 31, 2025
概括
这项研究引入了一种结合基于物理的建模和机器学习的新方法,用于在X射线衍射数据中分离热和机械应变. 这种方法增强了对在热力学负荷下材料行为的理解,特别是在激光融过程中.
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
- 晶体学 晶体学是指结晶学.
背景情况:
- 在热力学负荷下分析晶体材料时,X射线衍射 (XRD) 是至关重要的.
- 空间扩大和重叠的衍射峰使复杂应变状态的分析变得复杂.
- 现有的方法难以解开不同的格子变形机制.
研究的目的:
- 从XRD数据中开发一种新的方法来解热和机械弹性菌株.
- 分析Inconel 625在激光化过程中的热力学状态演变.
- 为了提高晶体材料的应变分析的准确性.
主要方法:
- 利用基于物理的建模 (热传输,流体流动,弹性塑性,XRD模拟) 和机器学习 (高斯过程回归) 的组合.
- 使用模拟生成训练数据,将衍射强度映射到应变场.
- 采用第一原理密度函数理论来准确计算材料属性.
主要成果:
- 从复杂的XRD图案中成功解了热和机械弹性菌株.
- 提取了Inconel 625在激光化过程中热力学状态的演变.
- 演示了训练有素的模型分析不规则形状的衍射峰值的能力.
结论:
- 开发的方法有效地分离了热和机械应变贡献.
- 这种方法为快速加工过程中材料的热力学行为提供了详细的见解.
- 机器学习增强的XRD分析为材料表征提供了一个强大的工具.
关键词:
高斯过程回归的高斯过程回归.第一个原则是计算计算.机器学习是机器学习.基于物理学的建模.压力 压力 压力 压力压力就是压力,压力就是压力.超级合金是一种超级合金.同步X射线衍射同步X射线衍射.更多相关视频
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