Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy
Name
s41524-022-00793-9.pdf
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Shi, Chuqiao
Cao, Michael C.
Rehn, Sarah M.
Bae, Sang-Hoon
Kim, Jeehwan
Jones, Matthew R.
Muller, David A.
Han, Yimo
Date Issued
May 18, 2022
Journal
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Citation
Shi, C., Cao, M.C., Rehn, S.M. et al. Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy. npj Comput Mater 8, 114 (2022).
Version
Final published version
Abstract
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.
Subjects
Computer Science Applications
Mechanics of Materials
General Materials Science
Modeling and Simulation
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41524-022-00793-9
