Deep learning for mapping element distribution of high-entropy alloys in scanning transmission electron microscopy images
Deep learning for mapping element distribution of high-entropy alloys in scanning transmission electron microscopy images
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DOI：
10.1016/j.commatsci.2021.110905
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发表时间：
2022-01
影响因子：
3.3
通讯作者：
Marco Ragone;Mahmoud Tamadoni Saray;Lance Long;R. Shahbazian‐Yassar;F. Mashayek;Vitaliy Yurkiv
中科院分区：
文献类型：
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作者：
Marco Ragone;Mahmoud Tamadoni Saray;Lance Long;R. Shahbazian‐Yassar;F. Mashayek;Vitaliy Yurkiv
机器学习（ML）和深度学习（DL）算法的最新发展为有效分析化学复杂材料的原子结构铺平了道路。在这项工作中，我们提出了一个DL模型建立在一个完全卷积神经网络（FCN），以解决复杂的PtNiPdCoFe高熵合金（HEAs）中表示的扫描透射电子显微镜（STEM）图像在原子分辨率的随机元素分布。所提出的神经网络的目标是通过语义分割来学习STEM图像的像素强度与原子列中不同组成元素的原子数量（即，（3）在HEA的结构中。我们证明，我们的DL模型能够正确估计柱高，或者对于用于训练和测试网络的模拟STEM图像中所表示的HEA结构中的大多数柱，误差高达1个原子。这在实验图像中的元素分布的估计中建立了足够高水平的置信度。在不同的STEM图像的纳米粒子的预测分布揭示了不均匀的波动，在列内的元素原子分数的局部聚集。最明显的聚集由Pt显示，其是合成的HEA材料中最大和最负电性的元素。建议的DL方法是有益的HEAs和多元3D材料的结构特性的深入表征一般。
The latest developments of machine learning (ML) and deep learning (DL) algorithms have paved the way to effectively analyze the atomic structure of chemically-complex materials. In this work, we present a DL model built upon a fully convolutional neural network (FCN) to resolve the random elements distribution of complex PtNiPdCoFe high-entropy alloys (HEAs) represented in the scanning transmission electron microscopy (STEM) images at atomic resolution. The objective of the proposed neural network is to learn through semantic segmentation the non-linear correlations between the pixels’ intensities of STEM images and the number of atoms of different constituent elements in the atomic columns (i.e., column heights) in the HEA’s structure. We demonstrate that our DL model is capable of correctly estimating the column heights or with an error up to 1 atom for the majority of the columns in the HEA structures represented in the simulated STEM images used to train and test the network. This establishes a sufficiently high level of confidence in the estimation of the element distribution in experimental images. The predicted distributions in different STEM images of nanoparticles reveal inhomogeneous fluctuations with local aggregations in the elemental atomic fractions within the columns. The most pronounced aggregation is displayed by Pt, which is the largest and most electronegative element in the synthesized HEA material. The proposed DL method is beneficial for an in-depth characterization of the structural properties of HEAs and multielement 3D materials in general.