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Updated: Jul 30, 2025

Negative Additive Manufacturing of Complex Shaped Boron Carbides
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Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning.

Ji-Hoon Park1, Ang-Yu Lu1, Mohammad Mahdi Tavakoli1

  • 1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

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|May 17, 2023
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Summary

Synthesizing large 2D materials like hexagonal boron nitride (hBN) requires understanding growth dynamics. Machine learning analysis reveals optimal growth windows for large hBN flakes, improving synthesis control.

Keywords:
Gaussian processchemical vapor depositiongrowth parameterhexagonal boron nitridemachine learning

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Surface Science

Background:

  • Wafer-scale monolayer two-dimensional (2D) materials are synthesized using epitaxial chemical vapor deposition (CVD).
  • Scaling up 2D material synthesis necessitates a systematic analysis of growth dynamics and mechanisms.
  • Previous studies often used limited control variate methods, hindering comprehensive optimization.

Purpose of the Study:

  • To systematically analyze the influence of growth parameters on two-dimensional (2D) material synthesis.
  • To unravel the growth mechanisms of hexagonal boron nitride (hBN) on copper.
  • To establish growth windows for large hBN flake sizes using advanced analysis.

Main Methods:

  • Epitaxial chemical vapor deposition (CVD) of monolayer hexagonal boron nitride (hBN) on single-crystalline Cu (111).
  • Systematic variation of growth parameters to control hBN domain sizes.
  • Gaussian process modeling to explore parameter correlations and identify optimal growth conditions.

Main Results:

  • Demonstrated control over hBN domain sizes by adjusting CVD growth parameters.
  • Identified correlations between multiple growth parameters influencing hBN synthesis.
  • Provided specific growth windows for achieving large-sized hBN flakes.

Conclusions:

  • A machine learning-based approach offers a more comprehensive understanding of 2D material growth mechanisms.
  • This methodology facilitates optimization of CVD synthesis for large-scale 2D material production.
  • The findings advance the controlled synthesis of high-quality hexagonal boron nitride for various applications.