  In situ reflective high-energy electron diffraction (RHEED) is widely used to
monitor the surface crystalline state during thin-film growth by molecular beam
epitaxy (MBE) and pulsed laser deposition. With the recent development of
machine learning (ML), ML-assisted analysis of RHEED videos aids in
interpreting the complete RHEED data of oxide thin films. The quantitative
analysis of RHEED data allows us to characterize and categorize the growth
modes step by step, and extract hidden knowledge of the epitaxial film growth
process. In this study, we employed the ML-assisted RHEED analysis method to
investigate the growth of 2D thin films of transition metal dichalcogenides
(ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and
K-means clustering were used to separate statistically important patterns and
visualize the trend of pattern evolution without any notable loss of
information. Using the modified PCA, we could monitor the diffraction intensity
of solely the ReSe2 layers by filtering out the substrate contribution. These
findings demonstrate that ML analysis can be successfully employed to examine
and understand the film-growth dynamics of 2D materials. Further, the ML-based
method can pave the way for the development of advanced real-time monitoring
and autonomous material synthesis techniques.
