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We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS2 using photoluminescence (PL) intensity as the objective function. Through iterative experiments guided by BO, an improvement of 86.6 % in PL intensity is achieved within 13 optimization rounds. Statistical analysis revealed the relationships between growth conditions and PL intensity, highlighting the importance of critical conditions, including the tungsten source concentration and Ar flow rate. Furthermore, the effectiveness of BO is demonstrated by comparison with random search, showing its ability to converge to optimal conditions with fewer iterations. This research highlights the potential of ML-driven approaches in accelerating material synthesis and optimization processes, paving the way for advances in 2D material-based technologies.

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  This document is the Accepted Manuscript version of a Published Work that appeared in final form in Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2, copyright © 2024 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsami.4c15275

Keyword: Bayesian optimization, 2D materials, Crystal growth

Date published: 2024-10-30

Publisher: American Chemical Society

Journal:

• ACS Applied Materials & Interfaces (ISSN: 19448244) vol. 16 issue. 43

Funding:

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5019

First published URL: https://doi.org/10.1021/acsami.4c15275

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Updated at: 2025-10-21 15:50:25 +0900

Published on MDR: 2025-10-21 15:43:29 +0900