# Structure prediction of boron-doped graphene by machine learning

https://mdr.nims.go.jp/datasets/4f5b2c66-7ded-44c6-bea0-638fd212e6c2

## File

- [1.5018065.pdf](https://mdr.nims.go.jp/filesets/0f9e2b42-825c-4dd0-9cd2-82d0a909a2ce/download) ([Detail](https://mdr.nims.go.jp/filesets/0f9e2b42-825c-4dd0-9cd2-82d0a909a2ce.md))

## Id

4f5b2c66-7ded-44c6-bea0-638fd212e6c2

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-08-05T16:24:08.573408Z

## Updated at

2024-01-05T13:12:04.652704Z

## Published at

2021-08-13T18:55:17.667521Z

## Doi



## First published url

https://doi.org/10.1063/1.5018065

## Date published

2018-06-28

## Recorded date published

2018-6-28

## Resource type

journal_article

## Manuscript type

authors_original

## Collection



## Title

- title: Structure prediction of boron-doped graphene by machine learning
  title_type: original
  lang: en

## Description

- description: Heteroatom doping has endowed graphene with manifold aspects of material
    properties and boosted its applications. The atomic structure determination of
    doped graphene is vital to understand its material properties. Motivated by the
    recently synthesized boron-doped graphene with relatively high concentration,
    here we employ machine learning methods to search the most stable structures of
    doped boron atoms in graphene, in conjunction with the atomistic simulations.
    From the determined stable structures, we find that in the free-standing pristine
    graphene, the doped boron atoms energetically prefer to substitute for the carbon
    atoms at different sublattice sites and that the para configuration of boron-boron
    pair is dominant in the cases of high boron concentrations. The boron doping can
    increase the work function of graphene by 0.7 eV for a boron content higher than
    3.1%
  description_type: abstract
  lang: en

## Creator

- name: Hou, Zhufeng
  role: author
  orcid: https://orcid.org/0000-0002-0069-5573
- name: Dieb, Thaer M.
  role: author
  orcid: https://orcid.org/0000-0002-8111-2009
- name: Tsuda, Koji
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606

## Contact agent



## Publisher

organization: AIP Publishing

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## Keyword

- subject: Materials design
  schema: not_defined
- subject: Monte Carlo tree search
  schema: not_defined

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## Fileset

- id: 0f9e2b42-825c-4dd0-9cd2-82d0a909a2ce
  filename: 1.5018065.pdf
  content_type: application/pdf
  size: 1746136
  md5: a1c7c29cefc4cf56f245e5c098dd884f

## Thumbnail

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filename: 1.5018065.pdf