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Leveraging Data-Driven strategy for Accelerating the Discovery of Polyesters with Targeted Glass Transition Temperatures
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  • Xiaoying He,
  • Mengxian Yu,
  • Jian-Peng Han,
  • Jie Jiang,
  • Qingzhu Jia,
  • Qiang Wang,
  • Zheng-Hong Luo,
  • Fangyou Yan,
  • Yin-Ning Zhou
Xiaoying He
Tianjin University of Science and Technology
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Mengxian Yu
Tianjin University of Science and Technology
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Jian-Peng Han
Shanghai Jiao Tong University
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Jie Jiang
East China University of Science and Technology
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Qingzhu Jia
Tianjin University of Science and Technology
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Qiang Wang
Tianjin University of Science and Technology
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Zheng-Hong Luo
Shanghai Jiao Tong University
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Fangyou Yan
Tianjin University of Science and Technology
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Yin-Ning Zhou
Shanghai Jiao Tong University

Corresponding Author:[email protected]

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Abstract

To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data-driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures (Tgs) to develop a quantitative structure-property relationship (QSPR) model. The model underwent rigorous validation (external validation, internal validation, Y-random and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in-silico retrosynthesis strategy, over 95000 virtual polyesters were designed, largely expanding the available space for polyester materials. External assessments highlight the good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized diverse virtual polyesters with Tgs covering a sufficient large temperature range. It is believed that this data-driven approach can drive future product development of polymer industry.
24 Oct 2023Submitted to AIChE Journal
29 Oct 2023Assigned to Editor
29 Oct 2023Submission Checks Completed
29 Oct 2023Review(s) Completed, Editorial Evaluation Pending
06 Nov 2023Reviewer(s) Assigned