通过机器学习增强原子探头断层扫描对化学短距离秩序进行定量三维成像
Yue Li1, Ye Wei2, Zhangwei Wang3
1Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237, Düsseldorf, Germany. yue.li@mpie.de.
Nature communications
|November 17, 2023
概括
这项研究揭示了Fe-Al合金中的化学短程顺序 (CSRO),使用先进的原子探头断层扫描. 机器学习克服了分辨率限制,对3D原子结构进行成像,并将CSRO与材料属性联系起来.
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
- 计算材料科学科学 计算材料科学
背景情况:
- 化学短距离顺序 (CSRO) 描述了晶体材料中的原子自我组织,形成特定的原子社区.
- 对于CSRO来说,传统的表征方法是间接的,通过分辨率受到限制,并且无法提供3D原子架构.
- 了解CSRO对于解释Fe-Al等合金在热处理后异常性质变化至关重要.
研究的目的:
- 开发一种机器学习增强的原子探头断层扫描方法,用于多个CSRO的高分辨率3D成像.
- 解决关于体中心立方Fe-Al合金中存在的CSRO类型的长期问题.
- 建立火温度,CSRO和材料特性,如纳米硬度和电电阻力之间的定量相关性.
主要方法:
- 开发一种机器学习算法,以提高原子探头断层扫描 (APT) 数据的分辨率.
- 应用增强的APT技术来研究Fe-Al合金的原子结构.
- 对回火温度的相关性分析,确定了CSRO,纳米硬度和电阻测量.
主要成果:
- 该研究提供了非统计学B2-CSRO在Fe-Al合金中的直接3D证据,挑战了以前假定的D03-CSRO.
- 在回火温度,特定类型的CSRO和纳米硬度和电电阻的变化之间建立了定量相关性.
- 该方法在表现出修改D03-CSRO.的Fe-Ga合金上成功验证了该方法.
结论:
- 机器学习增强的APT方法克服了分辨率限制,使CSRO的直接3D成像成为可能.
- 这些发现澄清了Fe-Al合金中异常性质的原子尺度起源,确定B2-CSRO是关键因素.
- 这种多功能策略可以应用于研究各种材料中的排序现象,帮助设计先进材料.
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