# High-Throughput Computational Screening of Two-Dimensional Semiconductors

https://mdr.nims.go.jp/datasets/13df47f9-c709-4436-9a60-b6db7f82af7a

## File

- [2D_semiconductors_2022_07_23_SizeReduced.pdf](https://mdr.nims.go.jp/filesets/176a7cbd-ba04-47ee-9871-711e3c764adb/download) ([Detail](https://mdr.nims.go.jp/filesets/176a7cbd-ba04-47ee-9871-711e3c764adb.md))

## Id

13df47f9-c709-4436-9a60-b6db7f82af7a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-12-22T08:06:36.070497Z

## Updated at

2023-12-25T06:17:40.348240Z

## Published at

2023-12-25T07:30:31.723642Z

## Doi

https://doi.org/10.48505/nims.4313

## First published url

https://doi.org/10.1021/acs.jpclett.2c02972

## Date published

2022-12-22

## Recorded date published

2022-12-22

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: High-Throughput Computational Screening of Two-Dimensional Semiconductors
  title_type: original
  lang: en

## Description

- description: By performing high-throughput first-principles calculations combined
    with a semiempirical van der Waals dispersion correction, we have screened 73
    direct- and 183 indirect-gap 2D nonmagnetic semiconductors from nearly 1000 monolayers
    according to the criteria for thermodynamic, mechanical, dynamic, and thermal
    stabilities and conductivity type. We present the calculated lattice constants,
    formation energy, Young’s modulus, Poisson’s ratio, shear modulus, anisotropic
    effective mass, band structure, band gap, ionization energy, electron affinity,
    and simulated scanning tunnel microscopy for each candidate meeting our criteria.
    The resulting 2D semiconductor database provides an ideal platform for computational
    modeling and design of new 2D semiconductors and heterostructures in photocatalysis,
    nanoscale devices, and other applications. Further, a linear fitting model was
    proposed to evaluate band gap, ionization energy, and electron affinity of 2D
    semiconductors from the density functional theory (DFT) calculated data as initial
    input.
  description_type: abstract
  lang: eng

## Creator

- name: Vei Wang
  role: author
  organization: Xi’an University of Technology
- name: Gang Tang
  role: author
  organization: Beijing Institute of Technology
- name: Ya-Chao Liu
  role: author
  organization: Xi’an University of Technology
- name: Ren-Tao Wang
  role: author
  organization: Xi’an University of Technology
- name: Hiroshi Mizuseki
  role: author
  organization: Korea Institute of Science and Technology
- name: Yoshiyuki Kawazoe
  role: author
  organization: New Industry Creation Hatchery Center, Tohoku University, Sendai 980–8579,
    Japan
- name: Jun Nara
  role: author
  organization: National Institute for Materials Science
  department: International Center for Materials Nanoarchitectonics/Nano-Theory Field/First-Principles
    Simulation Group
  ror: https://ror.org/026v1ze26
- name: Wen Tong Geng
  role: author
  organization: Hainan University

## Contact agent



## Publisher

organization: American Chemical Society

## Managing organization



## Keyword

- subject: DFT
  schema: not_defined
- subject: 2D Materials
  schema: not_defined

## Rights

- description: This document is the unedited Author’s version of a Submitted Work
    that was subsequently accepted for publication inJournal of Physical Chemistry
    Letters, copyright © 2022 American Chemical Societyafter peer review. To access
    the final edited and published work see https://doi.org/10.1021/acs.jpclett.2c02972
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Journal of Physical Chemistry Letters
  issn: '19487185'
  volume: '13'
  start_page: 11581
  end_page: 11594

## Conference



## Related item



## Funding

- identifier: JPJ004596
  funder_name: ATLA
  description: 二次元機能性原子薄膜を用いた革新的赤外線センサの研究

## Instrument



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## Specimen



## Chemical composition



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## Fileset

- id: 176a7cbd-ba04-47ee-9871-711e3c764adb
  filename: 2D_semiconductors_2022_07_23_SizeReduced.pdf
  content_type: application/pdf
  size: 3918663
  md5: af2f85e5b5d5ddab28664bfcf4c23bb1

## Thumbnail

fileset_id: 176a7cbd-ba04-47ee-9871-711e3c764adb
filename: 2D_semiconductors_2022_07_23_SizeReduced.pdf