Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy  

作　　者：Chuqiao Shi Michael C.Cao Sarah M.Rehn Sang-Hoon Bae Jeehwan Kim Matthew RJones David A.Muller Yimo Han 

机构地区：[1]Department of Materials Science and NanoEngineering,Rice University,Houston,TX,USA [2]School of Applied and Engineering Physics,Cornell University,Ithaca,NY,USA [3]Department of Chemistry,Rice University,Houston,TX,USA [4]Department of Mechanical Engineering and Materials Science,Washington University in Saint Louis,Saint Louis,MO,USA [5]Institute of Materials Science and Engineering,Washington University in Saint Louis,Saint Louis,MO,USA [6]Department of Mechanical Engineering,Massachusetts Institute of Technology,Cambridge,MA,USA [7]Kavli Institute for Nanoscale Science,Cornell University,Ithaca,NY,USA

出　　处：《npj Computational Materials》2022年第1期1072-1080,共9页计算材料学（英文）

基　　金：C.S.and Y.H.are supported by start-up funds provided by Rice University.Y.H.acknowledges the support from the Welch Foundation(C-2065-20210327);M.C.and D.A.M are supported by the NSF MRSEC program(DMR-1719875);S.M.R.would like to acknowledge financial support from a National Science Foundation Graduate Research Fellowship(No.1842494)。

摘　　要：Understanding lattice deformations is crucial in determining the properties of nanomaterials,which can become more prominent in future applications ranging from energy harvesting to electronic devices.However,it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area.Here,we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials.Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy(4D-STEM).Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample.Using this purely data-driven analysis,we have uncovered different types of material deformations,such as strain,lattice distortion,bending contour,etc.,which can significantly impact the band structure and subsequent performance of nanomaterials-based devices.We envision that this data-driven procedure will provide insight into materials’intrinsic structures and accelerate the discovery of materials.

关 键 词：utilize purely OVERCOME 

分 类 号：TB30[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]