IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-022-35766-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35766-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35766-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2020. "Measuring partisan media bias in US Newscasts from 2001-2012," Working Paper 183/2020, Helmut Schmidt University, Hamburg, revised 15 Nov 2022.
    2. Ananthan Nambiar & Tobias Rubel & James McCaull & Jon deVries & Mark Bedau, 2021. "Dropping diversity of products of large US firms: Models and measures," Papers 2110.08367, arXiv.org.
    3. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    4. Julia Seiermann, 2018. "Only Words? How Power in Trade Agreement Texts Affects International Trade Flows," UNCTAD Blue Series Papers 80, United Nations Conference on Trade and Development.
    5. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Arthur Dyevre & Nicolas Lampach, 2021. "Issue attention on international courts: Evidence from the European Court of Justice," The Review of International Organizations, Springer, vol. 16(4), pages 793-815, October.
    7. Dewenter, Ralf & Dulleck, Uwe & Thomas, Tobias, 2018. "The political coverage index and its application to government capture," Research Papers 6, EcoAustria – Institute for Economic Research.
    8. Pastwa, Anna M. & Shrestha, Prabal & Thewissen, James & Torsin, Wouter, 2021. "Unpacking the black box of ICO white papers: a topic modeling approach," LIDAM Discussion Papers LFIN 2021018, Université catholique de Louvain, Louvain Finance (LFIN).
    9. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    10. Milena Djourelova & Ruben Durante, 2019. "Media attention and strategic timing in politics: Evidence from U.S. presidential executive orders," Economics Working Papers 1675, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    12. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    13. Erkan Işığıçok & Sadullah Çelik & Dilek Özdemir Yılmaz, 2023. "Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 10-25, December.
    14. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    15. Purwoko Haryadi Santoso & Edi Istiyono & Haryanto & Wahyu Hidayatulloh, 2022. "Thematic Analysis of Indonesian Physics Education Research Literature Using Machine Learning," Data, MDPI, vol. 7(11), pages 1-41, October.
    16. Markus Eberhardt & Giovanni Facchini & Valeria Rueda, 2023. "Gender Differences in Reference Letters: Evidence from the Economics Job Market," The Economic Journal, Royal Economic Society, vol. 133(655), pages 2676-2708.
    17. Rauh, Christian, 2018. "Validating a sentiment dictionary for German political language—a workbench note," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 319-343.
    18. Ferrara, Federico M. & Masciandaro, Donato & Moschella, Manuela & Romelli, Davide, 2022. "Political voice on monetary policy: Evidence from the parliamentary hearings of the European Central Bank," European Journal of Political Economy, Elsevier, vol. 74(C).
    19. James Evans, 2022. "From Text Signals to Simulations: A Review and Complement to Text as Data by Grimmer, Roberts & Stewart (PUP 2022)," Sociological Methods & Research, , vol. 51(4), pages 1868-1885, November.
    20. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2022. "Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1217-1248, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35766-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.