# Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

https://mdr.nims.go.jp/datasets/0ff68775-016a-443d-82f0-7b40637d8657

## File

- [STAM.pdf](https://mdr.nims.go.jp/filesets/85fe9179-e77b-4aa0-8b9d-1a8cca4cdcdc/download) ([Detail](https://mdr.nims.go.jp/filesets/85fe9179-e77b-4aa0-8b9d-1a8cca4cdcdc.md))

## Id

0ff68775-016a-443d-82f0-7b40637d8657

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-13T07:50:41.417984Z

## Updated at

2024-01-05T13:11:22.523386Z

## Published at

2023-03-20T07:04:03.378708Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2020.1824985

## Date published

2020-01-31

## Recorded date published

2020-1-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Essential structural and experimental descriptors for bulk and grain boundary
    conductivities of Li solid electrolytes
  title_type: original
  lang: en

## Description

- description: 'We present a computational approach for identifying the important
    descriptors of the ionic conductivities of lithium solid electrolytes. Our approach
    discriminates the factors of both bulk and grain boundary conductivities, which
    have been rarely reported. The effects of the interrelated structural (e.g. grain
    size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability)
    and experimental (e.g. sintering temperature, synthesis method) properties on
    the bulk and grain boundary conductivities are investigated via machine learning.
    The data are trained using the bulk and grain boundary conductivities of Li solid
    conductors at room temperature. The important descriptors are elucidated by their
    feature importance and predictive performances, as determined by a nonlinear XGBoost
    algorithm: (i) the experimental descriptors of sintering conditions are significant
    for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy
    and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell
    volume are the prior structural descriptors while the polarizability and electronegativity
    are the prior chemical descriptors for grain boundary, (iv) the grain size provides
    physical insights such as the thermodynamic condition and should be considered
    for determining grain boundary conductance in solid polycrystalline ionic conductors. '
  description_type: abstract
  lang: eng

## Creator

- name: Yen-Ju Wu
  role: author
  orcid: https://orcid.org/0000-0003-2647-3407
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Takehiro Tanaka
  role: author
- name: Tomoyuki Komori
  role: author
- name: Mikiya Fujii
  role: author
- name: Hiroshi Mizuno
  role: author
- name: Satoshi Itoh
  role: author
  orcid: https://orcid.org/0000-0002-8139-582X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Tadanobu Takada
  role: author
- name: Erina Fujita
  role: author
  orcid: https://orcid.org/0000-0002-0987-5597
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Ionic conductivity
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: grain boundary
  schema: not_defined
- subject: ionic conductor
  schema: not_defined
- subject: Li battery
  schema: not_defined
- subject: grain size
  schema: not_defined
- subject: descriptor
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
  issn: '14686996'
  volume: '21'
  issue: '1'
  start_page: 712
  end_page: 725

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## Related item



## Funding

- identifier: "‘Materials research by Information Integration’ Initiative (MI2I) project"
  funder_name: Japan Science and Technology Agency
  description: "Panasonic-NIMS Center of\r\nExcellence for Advanced Functional Materials
    and ‘Materials\r\nresearch by Information Integration’ Initiative (MI2I) project\r\nof
    the Support Program for Starting Up Innovation Hub from\r\nJapan Science and Technology
    Agency (JST)"

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## Fileset

- id: 85fe9179-e77b-4aa0-8b9d-1a8cca4cdcdc
  filename: STAM.pdf
  content_type: application/pdf
  size: 5922312
  md5: 75d94b6111dd357ee9d25b0c45543855

## Thumbnail

fileset_id: 85fe9179-e77b-4aa0-8b9d-1a8cca4cdcdc
filename: STAM.pdf