Predicting column heights and elemental composition in experimental transmission electron microscopy images of high-entropy oxides using deep learning

IF 9.4 1区材料科学 Q1 CHEMISTRY, PHYSICAL

npj Computational Materials Pub Date : 2024-11-30 DOI:10.1038/s41524-024-01461-w

Ishraque Zaman Borshon, Marco Ragone, Abhijit H. Phakatkar, Lance Long, Reza Shahbazian-Yassar, Farzad Mashayek, Vitaliy Yurkiv

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Abstract

A novel approach is presented by integrating images-driven deep learning (DL) with high entropy oxides (HEOs) analysis. A fully convolutional neural network (FCN) is used to interpret experimental scanning transmission electron microscopy (STEM) images of HEO of various sizes. The FCN model is designed to predict column heights (CHs) and elemental distributions from single, experimentally acquired STEM images of complex (Mn, Fe, Ni, Cu, Zn)₃O₄ HEO nanoparticles (NPs) at atomic resolution. The model’s ability to predict elemental distributions was tested across various crystallographic zones. It was found that the model could effectively adapt to different atomic configurations and operational conditions. One of the significant outcomes was the identification of substantial elemental inhomogeneities in all experimental NPs, which highlighted the random and complex nature of element distribution within HEOs. The developed FCN DL method can be applied to assist experimental HEO and beyond NP analysis in various operating conditions.

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利用深度学习预测高熵氧化物实验透射电子显微镜图像的柱高和元素组成

提出了一种将图像驱动深度学习（DL）与高熵氧化物（HEOs）分析相结合的新方法。采用全卷积神经网络（FCN）对不同尺寸HEO的实验扫描透射电子显微镜（STEM）图像进行了解译。FCN模型旨在预测柱高（CHs）和元素分布从单一的，实验获得的复杂（Mn, Fe, Ni, Cu, Zn）3O4 HEO纳米颗粒（NPs）在原子分辨率的STEM图像。该模型预测元素分布的能力在不同的晶体区域进行了测试。结果表明，该模型能有效地适应不同的原子构型和操作条件。其中一个重要的结果是在所有实验NPs中发现了大量的元素不均匀性，这突出了heo中元素分布的随机性和复杂性。所开发的FCN DL方法可以在各种操作条件下辅助实验HEO和超NP分析。

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来源期刊

npj Computational Materials Mathematics-Modeling and Simulation

CiteScore

15.30

自引率

5.20%

发文量

229

审稿时长

6 weeks

期刊介绍： npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.