# Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

https://mdr.nims.go.jp/datasets/d18e1667-ca64-48e3-b496-33d57e8876f1

## File

- [nanomaterials-3261714-Revision_T.pdf](https://mdr.nims.go.jp/filesets/0500b5ae-96fc-4838-9523-4effc300749e/download) ([Detail](https://mdr.nims.go.jp/filesets/0500b5ae-96fc-4838-9523-4effc300749e.md))

## Id

d18e1667-ca64-48e3-b496-33d57e8876f1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-23T01:06:15.210498Z

## Updated at

2024-12-24T04:58:23.036419Z

## Published at

2024-12-24T04:58:23.129377Z

## Doi

https://doi.org/10.48505/nims.5200

## First published url

https://doi.org/10.3390/nano14211688

## Date published

2024-10-22

## Recorded date published



## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research
  title_type: original
  lang: en

## Description

- description: 'The synthesis, characterization, and application of carbon nanotubes
    (CNTs) have long posed significant challenges due to the inherent multiple complexity
    nature involved in their production, processing, and analysis. Recent advancements
    in machine learning (ML) have pro-vided researchers with novel and powerful tools
    to address these challenges. This review explores the role of ML in the field
    of CNT research, focusing on how ML has enhanced CNT research by: (1) revolutionizing
    CNT synthesis through the optimization of complex multivariable systems, enabling
    autonomous systems, and reducing reliance on conventional trial-and-error approaches;
    (2) im-proving the accuracy and efficiency of CNT characterizations; and (3) accelerating
    the develop-ment of CNT applications across several fields such as electronics,
    composites, and biomedical fields. The review concludes by offering perspectives
    on the future potential of integrating ML further into CNT research, highlighting
    its role in driving the field forward.'
  description_type: abstract
  lang: und

## Creator

- name: Guohai Chen
  role: author
  orcid: https://orcid.org/0000-0001-8481-0972
- name: Dai-Ming Tang
  role: author
  orcid: https://orcid.org/0000-0001-7136-7481

## Contact agent



## Publisher

organization: MDPI AG

## Managing organization



## Keyword

- subject: machine learning
  schema: not_defined
- subject: carbon nanotube
  schema: not_defined
- subject: in situ TEM
  schema: not_defined
- subject: growth mechanism
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Nanomaterials
  issn: '20794991'
  volume: '14'
  issue: '21'
  article_number: '1688'

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## Funding

- identifier: JP23K04552
  funder_name: JSPS KAKENHI
- identifier: JP25820336
  funder_name: JSPS KAKENHI
- identifier: JP20K05281
  funder_name: JSPS KAKENHI
- identifier: JP23H01796
  funder_name: JSPS KAKENHI
- identifier: JPMJFR223T
  funder_name: JST-FOREST
- funder_name: WPI-MANA ‘Challenging Research Program (CRP)’
- funder_name: NIMS ‘Support system for curiosity-driven research’
- identifier: JPMXP1224NM5238
  funder_name: Ministry of Education, Culture, Sports, Science and Technology

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## Fileset

- id: 0500b5ae-96fc-4838-9523-4effc300749e
  filename: nanomaterials-3261714-Revision_T.pdf
  content_type: application/pdf
  size: 2489452
  md5: c743c652c1e738eec6ea661b563d26a9

## Thumbnail

fileset_id: 0500b5ae-96fc-4838-9523-4effc300749e
filename: nanomaterials-3261714-Revision_T.pdf