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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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.