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Dielectric Ceramics Database Automatically Constructed by Data Mining in the Literature.

Xiaochao Wang1, Wanli Zhang1, Wenxu Zhang1

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Journal of Chemical Information and Modeling
|July 23, 2024
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Summary

Researchers developed a data-mining pipeline using natural language processing (NLP) to extract dielectric ceramics properties from over 12,900 articles. This approach aids in predicting material properties and discovering structure-property relationships.

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Area of Science:

  • Materials Science
  • Data Science
  • Computational Chemistry

Background:

  • Dielectric ceramics literature is extensive, offering potential for big-data analysis.
  • Identifying structure-property relationships and predicting material properties are crucial for materials development.

Purpose of the Study:

  • To construct a data-mining pipeline for extracting dielectric ceramic properties from published literature.
  • To normalize over 20 material properties and analyze their trends over time.
  • To predict dielectric properties and identify key influencing factors.

Main Methods:

  • Natural language processing (NLP) pipeline for information extraction from ~12,900 articles.
  • Normalization of over 20 dielectric ceramic properties.
  • Training an XGBoost model for dielectric constant prediction.
  • Utilizing SHAP for feature importance analysis.

Main Results:

  • High micro-F1 scores achieved in NLP tasks (91.6% for sentence classification, 82.4% for named entity recognition).
  • Demonstrated property distribution trends over publication years.
  • XGBoost model successfully predicted dielectric constants, with Q × f identified as a key factor for accuracy.

Conclusions:

  • The NLP-based data-mining pipeline effectively extracts and normalizes dielectric ceramic properties.
  • The approach facilitates the discovery of structure-property relationships and aids in property prediction.
  • Findings provide insights for experimentalists aiming to enhance dielectric material performance.