Phase division and recognition of crystal HRTEM images based on machine learning and deep learning

Abstract

The High Resolution Transmission Electron Microscope (HRTEM) images provide valuable insights into the atomic microstructure, dislocation patterns, defects, and phase characteristics of materials. However, the current analysis and research of HRTEM images of crystal materials heavily rely on manual expertise, which is labor-intensive and susceptible to subjective errors. This study proposes a combined machine learning and deep learning approach to automatically partition the same phase regions in crystal HRTEM images. The entire image is traversed by a sliding window to compute the amplitude spectrum of the Fast Fourier Transform (FFT) in each window. The generated data is transformed into a 4-dimensional (4D) format. Principal component analysis (PCA) on this 4D data estimates the number of feature regions. Non-negative matrix factorization (NMF) then decomposes the data into a coefficient matrix representing feature region distribution, and a feature matrix corresponding to the FFT magnitude spectra. Phase recognition based on deep learning enables identifying the phase of each feature region, thereby achieving automatic segmentation and recognition of phase regions in HRTEM images of crystals. Experiments on zirconium and oxide nanoparticle HRTEM images demonstrate the proposed method achieve the consistency of manual analysis. Code and supplementary material are available at https://github.com/rememberBr/HRTEM2.