Quantifying the CVD-grown two-dimensional materials via image clustering. Issue 36 (8th September 2021)

• Record Type: Journal Article
• Authors: Li, Zebin; Lee, Jihea; Yao, Fei; Sun, Hongyue
• Is Part Of: Nanoscale. Volume 13:Issue 36(2021)
• Page Start: 15324
• Page End: 15333
• Publication Date: 2021-09-08
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d1nr03802h
• Languages: English
• ISSNs: 2040-3364

Abstract

Abstract : We propose an unsupervised machine learning method to facilitate the quality evaluation of CVD-grown 2D materials. Abstract : Machine learning (ML) techniques have been recently employed to facilitate the development of novel two-dimensional (2D) materials. Among various synthesis approaches, chemical vapor deposition (CVD) has demonstrated tremendous potential in producing high-quality 2D flakes with good controllability, enabling large-scale production at a relatively low cost. Traditionally, the quality of CVD-grown samples can be manually evaluated based on optical images which is labor-intensive and time-consuming. In this paper, we explored a data-driven unsupervised quality assessment strategy based on image clustering via integrating self-organizing map (SOM) and k -means methods for optical image analysis of CVD-grown 2D materials. The high matching rate between the clustering results and material experts' labels indicated a good accuracy of the proposed clustering algorithm. The proposed unsupervised ML methodology will provide materials scientists with an effective tool kit for efficient evaluation of CVD-grown materials' quality and has a broad applicability for various material systems.