Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning

Ji Hoon Park, Ang Yu Lu, Mohammad Mahdi Tavakoli, Na Yeon Kim, Ming Hui Chiu, Hongwei Liu, Tianyi Zhang, Zhien Wang, Jiangtao Wang, Luiz Gustavo Pimenta Martins, Zhengtang Luo, Miaofang Chi, Jianwei Miao, Jing Kong

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. This new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials.

Original languageEnglish
Pages (from-to)4741-4748
Number of pages8
JournalNano Letters
Volume23
Issue number11
DOIs
StatePublished - Jun 14 2023

Funding

J.-H.P., A.-Y.L., J.W., and J.K. acknowledge the support from the U.S. Army Research Office (ARO) MURI project under grant W911NF-18-1-04320431 and the US Army Research Office through the Institute for Soldier Nanotechnologies at MIT, under cooperative agreement W911NF-18-2-0048. T.Z., Z.W., and J.K. acknowledge the support from the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) under award DE-SC0020042. J.M. acknowledges partial support by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Division of Materials Sciences and Engineering, under award DE-SC0010378. The STEM experiment was performed at the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy Office of Science User Facility. M.C. acknowledges Early Career project FWP# ERKCZ55 supported by the DOE Office of Science.

Keywords

  • Gaussian process
  • chemical vapor deposition
  • growth parameter
  • hexagonal boron nitride
  • machine learning

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