Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

The design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure-property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-11-21
DOI
https://doi.org/10.1021/jacs.5c12241
Primary Topic
Conducting polymers and applications
Type
article
Field-Weighted Citation Impact
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article

Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

Doga Ozgulbas, Shiyu Hu, Jianing Zhou, Aikaterini Vriza et al.
Journal of the American Chemical Society
Conducting polymers and applications
article

Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

Doga Ozgulbas, Shiyu Hu, Jianing Zhou, Aikaterini Vriza, Henry Chan, Anna M. Österholm, Jianguo Mei, Yukun Wu, Ian Foster, Jie Xu, Maria K. Y. Chan, John R. Reynolds, Qiaomu Yang, Rafael Vescovi, Subramanian K. R. S. Sankaranarayanan, Yuepeng Zhang, Zhiyang Wang
article en

Abstract

The design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure-property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.

Journal of the American Chemical SocietyVol. 147(48)
Argonne National Laboratory (US), Georgia Institute of Technology (US), Purdue University West Lafayette (US), Center for NanoScience (DE), University of Chicago (US), Department of Physics, Mathematics and Informatics (BY)
Openalex Percentile: Top 68%
Conducting polymers and applications
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