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Toward the design of ultrahigh-entropy alloys via mining six million texts

Author

Listed:
  • Zongrui Pei

    (New York University
    Oak Ridge National Laboratory)

  • Junqi Yin

    (Oak Ridge National Laboratory)

  • Peter K. Liaw

    (University of Tennessee)

  • Dierk Raabe

    (Max-Planck-Institut für Eisenforschung)

Abstract

It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

Suggested Citation

  • Zongrui Pei & Junqi Yin & Peter K. Liaw & Dierk Raabe, 2023. "Toward the design of ultrahigh-entropy alloys via mining six million texts," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35766-5
    DOI: 10.1038/s41467-022-35766-5
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
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