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Retrospective on a decade of machine learning for chemical discovery

Author

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  • O. Anatole von Lilienfeld

    (University of Vienna
    University of Basel)

  • Kieron Burke

    (University of California, Irvine)

Abstract

Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.

Suggested Citation

  • O. Anatole von Lilienfeld & Kieron Burke, 2020. "Retrospective on a decade of machine learning for chemical discovery," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18556-9
    DOI: 10.1038/s41467-020-18556-9
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    Cited by:

    1. Lei, Yang & Chen, Yuming & Chen, Jinghai & Liu, Xinyan & Wu, Xiaoqin & Chen, Yuqiu, 2023. "A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas," Energy, Elsevier, vol. 273(C).
    2. Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Alessandro Tibo & Jiazhen He & Jon Paul Janet & Eva Nittinger & Ola Engkvist, 2024. "Exhaustive local chemical space exploration using a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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