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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
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    Cited by:

    1. 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.
    2. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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).
    9. Agrawal, Ajay & McHale, John & Oettl, Alexander, 2024. "Artificial intelligence and scientific discovery: a model of prioritized search," Research Policy, Elsevier, vol. 53(5).
    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. 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.

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