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Updated: Dec 12, 2025

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A database of battery materials auto-generated using ChemDataExtractor.

Shu Huang1, Jacqueline M Cole2,3,4

  • 1Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK.

Scientific Data
|August 9, 2020
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Summary
This summary is machine-generated.

A new battery materials database was created by automatically analyzing scientific papers. This resource aids battery material design and prediction using data science methods.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Data Science

Background:

  • Battery material research relies on extensive literature data.
  • Manual data extraction is time-consuming and prone to errors.
  • A comprehensive, accessible database is needed for advanced battery research.

Purpose of the Study:

  • To create the first auto-generated database of battery materials.
  • To compile chemical-property relations from academic papers.
  • To facilitate battery material design and prediction through data science.

Main Methods:

  • Utilized ChemDataExtractor (version 1.5), a chemistry-aware natural language processing toolkit.
  • Modified the toolkit for the specific domain of batteries.
  • Processed 229,061 academic papers to extract data.

Main Results:

  • Generated a database with 292,313 records and 214,617 chemical-property relations.
  • Included data for 17,354 unique chemicals and properties like capacity, voltage, and conductivity.
  • 117,403 data points were multivariate, with a property as the dependent variable.

Conclusions:

  • The auto-generated database provides a representative overview of battery material information from scientific literature.
  • Public availability of this database enables data-science-driven battery material design and prediction.
  • A Graphical User Interface (GUI) is provided to enhance database usability.