# Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2 

https://mdr.nims.go.jp/datasets/4041fda1-acba-4626-a7f9-a2949d032480

## File

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## Id

4041fda1-acba-4626-a7f9-a2949d032480

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-22T02:24:23.426559Z

## Updated at

2025-10-21T06:50:25.398616Z

## Published at

2025-10-21T06:43:29.653184Z

## Doi

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

## First published url

https://doi.org/10.1021/acsami.4c15275

## Date published

2024-10-30

## Recorded date published

2024-10-30

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: 'Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of
    WS2 '
  title_type: original
  lang: en

## Description

- description: '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. '
  description_type: abstract
  lang: eng

## Creator

- name: Feng Zhang
  role: author
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Quantum Materials
    Field/2D Quantum Materials Group
  ror: https://ror.org/026v1ze26
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Algorithm Team
  ror: https://ror.org/026v1ze26
- name: Fanyu Zeng
  role: author
  orcid: https://orcid.org/0009-0005-1145-2939
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Quantum Materials
    Field/2D Quantum Materials Group
  ror: https://ror.org/026v1ze26
- name: Daichi Kozawa
  role: author
  orcid: https://orcid.org/0000-0002-0629-5589
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Quantum Materials
    Field/2D Quantum Materials Group
  ror: https://ror.org/026v1ze26
- name: Ryo Kitaura
  role: author
  orcid: https://orcid.org/0000-0001-8108-109X
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Quantum Materials
    Field/2D Quantum Materials Group
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: American Chemical Society

## Managing organization



## Keyword

- subject: Bayesian optimization
  schema: not_defined
- subject: 2D materials
  schema: not_defined
- subject: Crystal growth
  schema: not_defined

## Rights

- description: 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
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2024-10-15
end_date: 2025-10-15

## Journal

- title: ACS Applied Materials & Interfaces
  issn: '19448244'
  volume: '16'
  issue: '43'

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## Instrument



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## Specimen



## Chemical composition



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