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

Hyuk Jin Kim, Minsu Chong, Tae Gyu Rhee, Yeong Gwang Khim, Min Hyoung Jung, Young Min Kim, Hu Young Jeong, Byoung Ki Choi, Young Jun Chang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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. Graphical Abstract: [Figure not available: see fulltext.].

Original languageEnglish
Article number10
JournalNano Convergence
Issue number1
StatePublished - Dec 2023


  • K-means clustering
  • Machine learning
  • Principal component analysis
  • ReSe
  • TMDC


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