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Autonomous platform for solution processing of electronic polymers

Author

Listed:
  • Chengshi Wang

    (Argonne National Laboratory)

  • Yeon-Ju Kim

    (Argonne National Laboratory)

  • Aikaterini Vriza

    (Argonne National Laboratory)

  • Rohit Batra

    (Argonne National Laboratory
    Indian Institute of Technology Madras)

  • Arun Baskaran

    (Argonne National Laboratory
    Corning Incorporated)

  • Naisong Shan

    (The University of Chicago)

  • Nan Li

    (The University of Chicago)

  • Pierre Darancet

    (Argonne National Laboratory)

  • Logan Ward

    (Argonne National Laboratory)

  • Yuzi Liu

    (Argonne National Laboratory)

  • Maria K. Y. Chan

    (Argonne National Laboratory)

  • Subramanian K.R.S. Sankaranarayanan

    (Argonne National Laboratory
    University of Illinois)

  • H. Christopher Fry

    (Argonne National Laboratory)

  • C. Suzanne Miller

    (Argonne National Laboratory)

  • Henry Chan

    (Argonne National Laboratory)

  • Jie Xu

    (Argonne National Laboratory
    The University of Chicago)

Abstract

The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently processing electronic polymer solutions into thin films with specific properties remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore processing pathways for achieving high-conductivity, low-defect electronic polymers films. Leveraging importance-guided Bayesian optimization, Polybot efficiently navigates a complex 7-dimensional processing space. In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. The experimental campaign yields scale-up fabrication recipes, producing transparent conductive thin films with averaged conductivity exceeding 4500 S/cm. Feature importance analysis and morphological characterizations reveal key design factors. This work signifies a significant step towards transforming the manufacturing of electronic polymers, highlighting the potential of AI-driven automation in material science.

Suggested Citation

  • Chengshi Wang & Yeon-Ju Kim & Aikaterini Vriza & Rohit Batra & Arun Baskaran & Naisong Shan & Nan Li & Pierre Darancet & Logan Ward & Yuzi Liu & Maria K. Y. Chan & Subramanian K.R.S. Sankaranarayanan , 2025. "Autonomous platform for solution processing of electronic polymers," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55655-3
    DOI: 10.1038/s41467-024-55655-3
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    References listed on IDEAS

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    1. Kelsey L. Snapp & Benjamin Verdier & Aldair E. Gongora & Samuel Silverman & Adedire D. Adesiji & Elise F. Morgan & Timothy J. Lawton & Emily Whiting & Keith A. Brown, 2024. "Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
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