Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data
Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data
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DOI：
10.1016/j.actamat.2022.117633
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发表时间：
2021-06
期刊：
影响因子：
9.4
通讯作者：
Xuyang Zhou;Ye Wei;Markus Kuhbach;Huan Zhao;F. Vogel;R. D. Kamachali;G. Thompson;D. Raabe;B. Gault
中科院分区：
文献类型：
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作者：
Xuyang Zhou;Ye Wei;Markus Kuhbach;Huan Zhao;F. Vogel;R. D. Kamachali;G. Thompson;D. Raabe;B. Gault
晶界（GB）是控制许多类型的多晶材料的性质的平面晶格缺陷。因此，它们的结构已被详细研究。然而，对它们的化学特征知之甚少，这是由于在散装材料样品内的原子长度尺度上探测这些特征的实验困难。原子探针层析成像（APT）是一种能够完成这一任务的工具，能够在近原子尺度上量化化学特性。使用APT数据集，我们在这里提出了一种基于机器学习的方法，用于自动量化GB的化学特征。我们使用2万张谷物内部、GB或三重连接的合成图像训练了一个卷积神经网络（CNN）。这样一个经过训练的CNN会自动从APT数据中检测GB的位置。然后对这些GB进行成分映射和分析，包括揭示它们的平面化学装饰图案。我们应用这种方法来实验获得的APT数据集有关的三个案例研究，即镍-磷，铂-金，铝-锌-镁-铜合金。在第一种情况下，我们提取了GB特定的偏析功能的函数的取向差和重合位置的晶格字符。其次，我们揭示了界面过剩和平面内的化学特征，不能被发现的标准成分分析。最后，我们跟踪的时间演变的化学装饰从早期阶段的溶质GB偏析在稀限界面相分离，其特征在于复杂的组成模式的演变。这种基于机器学习的方法提供了对GB化学分析的定量，无偏见和自动化访问，作为与界面热力学，动力学和相关化学-结构-性质关系相关的新发现的支持工具。
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.