IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v533y2016i7601d10.1038_nature17439.html
   My bibliography  Save this article

Machine-learning-assisted materials discovery using failed experiments

Author

Listed:
  • Paul Raccuglia

    (Haverford College)

  • Katherine C. Elbert

    (Haverford College)

  • Philip D. F. Adler

    (Haverford College)

  • Casey Falk

    (Haverford College)

  • Malia B. Wenny

    (Haverford College)

  • Aurelio Mollo

    (Haverford College)

  • Matthias Zeller

    (Purdue University)

  • Sorelle A. Friedler

    (Haverford College)

  • Joshua Schrier

    (Haverford College)

  • Alexander J. Norquist

    (Haverford College)

Abstract

Failed chemical reactions are rarely reported, even though they could still provide information about the bounds on the reaction conditions needed for product formation; here data from such reactions are used to train a machine-learning algorithm, which is subsequently able to predict reaction outcomes with greater accuracy than human intuition.

Suggested Citation

  • Paul Raccuglia & Katherine C. Elbert & Philip D. F. Adler & Casey Falk & Malia B. Wenny & Aurelio Mollo & Matthias Zeller & Sorelle A. Friedler & Joshua Schrier & Alexander J. Norquist, 2016. "Machine-learning-assisted materials discovery using failed experiments," Nature, Nature, vol. 533(7601), pages 73-76, May.
    • Handle: RePEc:nat:nature:v:533:y:2016:i:7601:d:10.1038_nature17439
    DOI: 10.1038/nature17439
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature17439
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature17439?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jia-Min Lu & Hui-Feng Wang & Qi-Hang Guo & Jian-Wei Wang & Tong-Tong Li & Ke-Xin Chen & Meng-Ting Zhang & Jian-Bo Chen & Qian-Nuan Shi & Yi Huang & Shao-Wen Shi & Guang-Yong Chen & Jian-Zhang Pan & Zh, 2024. "Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Yuanyuan Jiang & Zongwei Yang & Jiali Guo & Hongzhen Li & Yijing Liu & Yanzhi Guo & Menglong Li & Xuemei Pu, 2021. "Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
    4. Zhang, Jianyu & Lu, Wei, 2022. "Sparse data machine learning for battery health estimation and optimal design incorporating material characteristics," Applied Energy, Elsevier, vol. 307(C).
    5. Li, Jing & Yu, Qian, 2024. "Scientists’ disciplinary characteristics and collaboration behaviour under the convergence paradigm: A multilevel network perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    6. Nathan J. Szymanski & Pragnay Nevatia & Christopher J. Bartel & Yan Zeng & Gerbrand Ceder, 2023. "Autonomous and dynamic precursor selection for solid-state materials synthesis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    8. 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.
    9. Seeram Ramakrishna & Tong-Yi Zhang & Wen-Cong Lu & Quan Qian & Jonathan Sze Choong Low & Jeremy Heiarii Ronald Yune & Daren Zong Loong Tan & Stéphane Bressan & Stefano Sanvito & Surya R. Kalidindi, 2019. "Materials informatics," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2307-2326, August.
    10. Maria Vogiatzaki & Stelios Zerefos & Marzia Hoque Tania, 2020. "Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    11. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    12. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    13. Lixin He & Siqi Sun & Pengfei Lan & Yanqing He & Bincheng Wang & Pu Wang & Xiaosong Zhu & Liang Li & Wei Cao & Peixiang Lu & C. D. Lin, 2022. "Filming movies of attosecond charge migration in single molecules with high harmonic spectroscopy," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Zhenxing Wang & Yunjun Yu & Kallol Roy & Cheng Gao & Lei Huang, 2023. "The Application of Machine Learning: Controlling the Preparation of Environmental Materials and Carbon Neutrality," IJERPH, MDPI, vol. 20(3), pages 1-4, January.

    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:nature:v:533:y:2016:i:7601:d:10.1038_nature17439. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.