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MatGD: Materials Graph Digitizer.

Jaewoong Lee1, Wonseok Lee1, Jihan Kim1

  • 1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.

ACS Applied Materials & Interfaces
|December 26, 2023
PubMed
Summary
This summary is machine-generated.

We created Material Graph Digitizer (MatGD) to extract data from scientific graphs. This tool accurately digitizes data lines, aiding material science research and discovery.

Keywords:
batterycatalystdata miningfigure miningmachine learningmetal−organic frameworks (MOFs)

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Scientific publications contain vast amounts of valuable data within figures.
  • Extracting this data manually is time-consuming and prone to errors.
  • Automated tools are needed to efficiently mine data from published scientific graphs.

Purpose of the Study:

  • To develop an automated tool, Material Graph Digitizer (MatGD), for digitizing data from scientific graphs.
  • To improve the accuracy and efficiency of data extraction from research publications.
  • To facilitate the collection of data for machine learning model training in materials science.

Main Methods:

  • MatGD algorithm involves four steps: graph identification, axes/data separation, data line discernment (including legend matching), and data extraction.
  • The tool was applied to 501,045 figures from 62,534 papers in batteries, catalysis, and metal-organic frameworks (MOFs).
  • Performance was evaluated based on accuracy in legend detection and data line separation.

Main Results:

  • MatGD achieved over 99% accuracy in legend marker and text detection.
  • The tool demonstrated a 66% accuracy in data line separation, surpassing existing tools.
  • Successfully processed a large dataset of figures from key materials science domains.

Conclusions:

  • MatGD is a highly accurate and efficient tool for digitizing data from scientific graphs.
  • This tool can significantly accelerate the collection of experimental data from literature.
  • The extracted data can power machine learning models for enhanced material predictions and discovery.