# A comprehensive data network for data-driven study of battery materials

https://mdr.nims.go.jp/datasets/a8293f23-e053-4c1d-86ec-1656e2efb03c

## File

- [2024_STAM.pdf](https://mdr.nims.go.jp/filesets/d4575380-9883-4d63-aea5-d71dcb753c79/download) ([Detail](https://mdr.nims.go.jp/filesets/d4575380-9883-4d63-aea5-d71dcb753c79.md))

## Id

a8293f23-e053-4c1d-86ec-1656e2efb03c

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-02T08:31:47.613551Z

## Updated at

2024-10-03T23:30:19.395959Z

## Published at

2024-10-03T23:30:19.655098Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2024.2403328

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: A comprehensive data network for data-driven study of battery materials
  title_type: original
  lang: en

## Description

- description: Data-driven material research for property prediction and material
    design using machine learning methods requires a large quantity, wide variety,
    and high-quality materials data. For battery materials, which are commonly polycrystalline,
    ceramics, and composites, multiscale data on substances, materials, and batteries
    are required. In this work, we develop a data network composed of three interlinked
    databases, from which we can obtain comprehensive data on substances such as crystal
    structures and electronic structures, data on materials such as chemical composition,
    structure, and properties, and data on batteries such as battery composition,
    operation conditions, and capacity. The data are extracted from research papers
    on solid electrolytes and cathode materials, selected by screening more than 330
    thousand papers using natural language processing tools. Data extraction and curation
    are carried out by editors specialized in material science and trained in data
    standardization.
  description_type: abstract
  lang: und

## Creator

- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- 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: Huiping Li
  role: author
- name: Lei Fang
  role: author
  orcid: https://orcid.org/0000-0003-4706-0521
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Shigenobu Hayashi
  role: author
- name: Ayako Oishi
  role: author
- name: Natsuko Shimizu
  role: author
- name: Riccarda Caputo
  role: author
- name: Pierre Villars
  role: author

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Material databases
  schema: not_defined
- subject: battery material
  schema: not_defined
- subject: crystal structure
  schema: not_defined
- subject: ionic conductivity
  schema: not_defined
- subject: nature language processing
  schema: not_defined
- subject: cathode
  schema: not_defined
- subject: solid electrolyte
  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: '25'
  issue: '1'
  article_number: '2403328'

## Conference



## Related item



## Funding

- identifier: "“Advanced Battery Collaboration” project of CO"
  funder_name: Japan Science and Technology Agency
- identifier: NIMS Materials Open Platform for All Solid-State B
  funder_name: National Institute for Materials Science
- identifier: Research Network and Facility Services Division
  funder_name: National Institute for Materials Science

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## Fileset

- id: d4575380-9883-4d63-aea5-d71dcb753c79
  filename: 2024_STAM.pdf
  content_type: application/pdf
  size: 7581324
  md5: ba8af22aa83bb1e0937e2c5b48985a01

## Thumbnail

fileset_id: d4575380-9883-4d63-aea5-d71dcb753c79
filename: 2024_STAM.pdf