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A self-driving laboratory advances the Pareto front for material properties

Author

Listed:
  • Benjamin P. MacLeod

    (The University of British Columbia
    The University of British Columbia)

  • Fraser G. L. Parlane

    (The University of British Columbia
    The University of British Columbia)

  • Connor C. Rupnow

    (The University of British Columbia
    The University of British Columbia
    The University of British Columbia)

  • Kevan E. Dettelbach

    (The University of British Columbia)

  • Michael S. Elliott

    (The University of British Columbia)

  • Thomas D. Morrissey

    (The University of British Columbia
    The University of British Columbia)

  • Ted H. Haley

    (The University of British Columbia)

  • Oleksii Proskurin

    (The University of British Columbia)

  • Michael B. Rooney

    (The University of British Columbia)

  • Nina Taherimakhsousi

    (The University of British Columbia)

  • David J. Dvorak

    (The University of British Columbia)

  • Hsi N. Chiu

    (The University of British Columbia)

  • Christopher E. B. Waizenegger

    (The University of British Columbia)

  • Karry Ocean

    (The University of British Columbia)

  • Mehrdad Mokhtari

    (The University of British Columbia)

  • Curtis P. Berlinguette

    (The University of British Columbia
    The University of British Columbia
    The University of British Columbia
    MaRS Centre)

Abstract

Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m−1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m−1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m−1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

Suggested Citation

  • Benjamin P. MacLeod & Fraser G. L. Parlane & Connor C. Rupnow & Kevan E. Dettelbach & Michael S. Elliott & Thomas D. Morrissey & Ted H. Haley & Oleksii Proskurin & Michael B. Rooney & Nina Taherimakhs, 2022. "A self-driving laboratory advances the Pareto front for material properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28580-6
    DOI: 10.1038/s41467-022-28580-6
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    References listed on IDEAS

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    1. Daniel Salley & Graham Keenan & Jonathan Grizou & Abhishek Sharma & Sergio Martín & Leroy Cronin, 2020. "A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    2. Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
    3. Zhiming Li & Konda Gokuldoss Pradeep & Yun Deng & Dierk Raabe & Cemal Cem Tasan, 2016. "Metastable high-entropy dual-phase alloys overcome the strength–ductility trade-off," Nature, Nature, vol. 534(7606), pages 227-230, June.
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    Cited by:

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    2. Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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