Atomic arrangement of van der Waals heterostructures using X-ray scattering and crystal truncation rod analysis

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

“…This ML analysis is beneficial for analyzing the growth dynamics and layer thicknesses for ultrathin van der Waals thin films, and the corresponding results are consistent with those of the K-means clustering method. Our results suggest that the ML-assisted RHEED analysis could be developed into an automatic validation method for investigating ultrathin 2D materials films, and it is complementary to other surface analysis tools [ 7 , 38 , 39 ]. Furthermore, this method can be applied to analyze the thin-film growth of other 2D materials, such as 2D chalcogenides, 2D MXenes, 2D oxides, and hexagonal boron nitrides [ 40 – 43 ].…”

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

“…RHEED is a powerful technique that monitors the surface state during lm growth because it provides extensive physical information, including surface morphology, growth rate, lattice spacing, crystalline disorder, and surface reconstruction. [74][75][76][77][78][79][80] Using the RHEED dataset, we will conduct a more quantitative analysis of the growth modes of 2D materials over time and identify crystal structures during the lm growth. [81][82][83][84] Along with the in situ RHEED data, we collected diverse surface analysis results for targeted samples, including AFM images, XPS, and PL/Raman spectra.…”