Fig. 1. Process of machine learning

Fig. 2. （a） RHEED pattern； （b） AFM image； （c） θ-2θ scanned XRD pattern； （d） cross-sectional HAADF-STEM image of the SrRuO₃ thin film with the RRR of 50~52； （e） magnified image near the interface in Fig.（d）； （f） magnified image near the interface in Fig.（e）^［34］

Fig. 3. Change relationship between of epitaxial TiN films with temperature under different conditions^［35］

Fig. 4. Overarching framework for the application of machine learning in RHEED^［37］

Fig. 5. Probability of achieving 7×7 RHEED pattern in two different deoxidation runs^［38］

Fig. 6. RHEED model multiclass classification model includes convolutional neural network and fully connected layer^［42］

Fig. 7. Controlled growth process of high-density quantum dots^［43］

Fig. 8. （a） K-means clustering up to K = 7 for SrTiO₃ on TbScO₃； （b） mean representative images in each cluster； （c） K-means minimization function plotted for each value of K^［45］

Fig. 9. NMF with rank 4 for LAO. （a）~（d） corresponding coefficient plots for the four clusters； （e）~（h） corresponding basis plots for the four clusters^［47］

Fig. 10. PCA results. （a） Six PCs of the RHEED video for the 3UC-thick-ReSe₂ thin film， PC1 shows the diffraction signal of graphene， PC2 contains the signals of both the graphene and ­ReSe₂ layers， PC3-6 show the signal of only the 2D growth of ReSe₂ layer； （b） corresponding score plots； （c） original RHEED video； （d）~（e） modified RHEED video. Blue and orange lines denote the （0，0） and （2，0） diffraction streaks of the ReSe₂ thin film （shown in the inset）， respectively^［50］