Challenges in Information-Mining the Materials Literature: A Case Study and Perspective

Andrew Smith, Vinayak Bhat, Qianxiang Ai, Chad Risko

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions.

Original languageEnglish
Pages (from-to)4821-4827
Number of pages7
JournalChemistry of Materials
Issue number11
StatePublished - Jun 14 2022

Bibliographical note

Publisher Copyright:
© 2022 American Chemical Society.

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Materials Chemistry


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