Uncovering Material Deformations via Machine Learning Combined with Four-Dimensional Scanning Transmission Electron Microscopy

TL;DR

This paper presents an unsupervised machine learning method using hierarchical clustering to automatically detect and analyze lattice deformations in nanomaterials from 4D-STEM data, enabling large-scale, unbiased deformation analysis.

Contribution

The study introduces a rapid, semi-automated, data-driven approach combining 4D-STEM with hierarchical clustering to uncover multi-scale lattice deformations without prior knowledge.

Findings

Successfully identified various deformation types such as strain and lattice distortion

Enabled large-area analysis of lattice deformations in nanomaterials

Facilitated insights into how deformations affect material properties

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 and enables extraction of essential features even 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 the intrinsic structures and accelerate the discovery of novel materials.