دورية أكاديمية
Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy
| العنوان: | Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy |
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| المؤلفون: | Chuqiao Shi, Michael C. Cao, Sarah M. Rehn, Sang-Hoon Bae, Jeehwan Kim, Matthew R. Jones, David A. Muller, Yimo Han |
| المصدر: | npj Computational Materials, Vol 8, Iss 1, Pp 1-9 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Materials of engineering and construction. Mechanics of materials LCC:Computer software |
| مصطلحات موضوعية: | Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2057-3960 |
| Relation: | https://doaj.org/toc/2057-3960 |
| DOI: | 10.1038/s41524-022-00793-9 |
| URL الوصول: | https://doaj.org/article/8ccb7f1ed7bd4d9cb84d5f03915ca201 |
| رقم الأكسشن: | edsdoj.8ccb7f1ed7bd4d9cb84d5f03915ca201 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20573960 |
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| DOI: | 10.1038/s41524-022-00793-9 |