作者：Yue Li Timoteo Colnaghi Yilun Gong Huaide Zhang Yuan Yu Ye Wei Bin Gan Min Song Andreas Marek Markus Rampp Siyuan Zhang Zongrui Pei Matthias Wuttig Jörg Neugebauer Zhangwei Wang Baptiste Gault
在固体中，化学短程有序（CSRO）是指某些物种的原子占据特定晶体位置的自组织。CSRO越来越多地被认为是一种调节材料机械性能和功能性能的杠杆。然而，CSRO畴的财产与形貌、数密度和原子构型之间的定量关系仍然难以捉摸。在此，我们展示了机器学习增强原子探针成像（APT）如何挖掘近原子分辨率的APT数据，并联合利用该技术的高元素灵敏度，对CoCrNi中熵合金中的CSRO进行3D定量分析。我们揭示了多种CSRO配置，它们的形成得到了最先进的蒙特卡罗模拟的支持。通过对这些CSRO的定量分析，我们可以建立加工参数和物理财产之间的关系。CSRO的明确特性将有助于通过以下方式完善先进材料的设计策略
In solids, chemical short-range order (CSRO) refers to the self-organisationof atoms of certain species occupying specific crystal sites. CSRO isincreasingly being envisaged as a lever to tailor the mechanical and functionalproperties of materials. Yet quantitative relationships between properties andthe morphology, number density, and atomic configurations of CSRO domainsremain elusive. Herein, we showcase how machine learning-enhanced atom probetomography (APT) can mine the near-atomically resolved APT data and jointlyexploit the technique’s high elemental sensitivity to provide a 3D quantitativeanalysis of CSRO in a CoCrNi medium-entropy alloy. We reveal multiple CSROconfigurations, with their formation supported by state-of-the-art Monte-Carlosimulations. Quantitative analysis of these CSROs allows us to establishrelationships between processing parameters and physical properties. Theunambiguous characterization of CSRO will help refine strategies for designingadvanced materials by manipulating atomic-scale architectures.
论文链接：http://arxiv.org/pdf/2303.13433v1
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