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

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

机构地区：[1]Department of Aerospace and Mechanical Engineering,University of Arizona,Tucson,AZ,85721,USA [2]Department of Mechanical and Industrial Engineering,University of Illinois Chicago,Chicago,IL,60607,USA [3]Department of Biomedical Engineering,University of Illinois Chicago,Chicago,IL,60607,USA [4]Electronic Visualization Laboratory,University of Illinois Chicago,Chicago,IL,60607,USA

出　　处：《npj Computational Materials》2024年第1期289-302,共14页计算材料学(英文)

基　　金：the National Science Foundation,award DMR-2311104,as well as CNS-1828265.

摘　　要：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 ofHEO 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)_(3)O_(4) 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 FCNDL method can be applied to assist experimental HEO and beyond NP analysis in various operating conditions.

关 键 词：CONDITIONS ENTROPY OXIDES 

分 类 号：O64[理学—物理化学]