Autonomous platform for solution processing of electronic polymers

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, H. Christopher Fry, C. Suzanne Miller, Henry Chan () and Jie Xu ()
Additional contact information
Chengshi Wang: Argonne National Laboratory
Yeon-Ju Kim: Argonne National Laboratory
Aikaterini Vriza: Argonne National Laboratory
Rohit Batra: Argonne National Laboratory
Arun Baskaran: Argonne National Laboratory
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
H. Christopher Fry: Argonne National Laboratory
C. Suzanne Miller: Argonne National Laboratory
Henry Chan: Argonne National Laboratory
Jie Xu: Argonne National Laboratory

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-55655-3

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