OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions

Ji-Feng Wang; Yu-Bo Sun; Qiu-Tong Chen; Fei-Fan Ji; Yuan-Yuan Song; Meng-Yuan Ruan; Ying Wang

doi:10.1007/s10118-025-3402-y

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OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions

RESEARCH ARTICLE | Updated：2025-09-26

- OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions
- Chinese Journal of Polymer Science Vol. 43, Issue 10, Pages: 1749-1760(2025)
- Affiliations：
  
  State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, AI for Polymer Science Research Center, Fudan University, Shanghai 200438, China
- Author bio：
  
  wying@fudan.edu.cn
- Funds：
- DOI：10.1007/s10118-025-3402-y
  CLC：
- Received：30 May 2025，
  
  Revised：2025-06-19，
  
  Accepted：25 June 2025，
  
  Online First：10 September 2025，
  
  Published：05 October 2025
- Accepted：
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Wang, J. F.; Sun, Y. B.; Chen, Q. T.; Ji, F. F.; Song, Y. Y.; Ruan, M. Y.; Wang, Y. OpenPoly: a polymer database empowering benchmarking and multi-property predictions. Chinese J. Polym. Sci. 2025, 43, 1749–1760

Ji-Feng Wang, Yu-Bo Sun, Qiu-Tong Chen, et al. OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1749-1760.
Wang, J. F.; Sun, Y. B.; Chen, Q. T.; Ji, F. F.; Song, Y. Y.; Ruan, M. Y.; Wang, Y. OpenPoly: a polymer database empowering benchmarking and multi-property predictions. Chinese J. Polym. Sci. 2025, 43, 1749–1760 DOI： 10.1007/s10118-025-3402-y.

Ji-Feng Wang, Yu-Bo Sun, Qiu-Tong Chen, et al. OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1749-1760. DOI： 10.1007/s10118-025-3402-y.

摘要

OpenPoly offers 3

985 curated polymer–property pairs across 26 properties. A unified benchmark shows XGBoost surpasses deep models in sparse data

and the database enables rapid identification of candidates for high-temperature dielectrics and fuel-cell membranes

advancing data-driven polymer discovery.

Abstract

Advancing the integration of artificial intelligence and polymer science requires high-quality

open-source

and large-scale datasets. However

existing polymer databases often suffer from data sparsity

lack of polymer-property labels

and limited accessibility

hindering systematic modeling across property prediction tasks. Here

we present OpenPoly

a curated experimental polymer database derived from extensive literature mining and manual validation

comprising 3985 unique polymer-property data points span

ning 26 key properties. We further develop a multi-task benchmarking framework that evaluates property prediction using four encoding methods and eight representative models. Our results highlight that the optimized degree-of-polymerization encoding coupled with Morgan fingerprints achieves an optimal trade-off between computational cost and accuracy. In data-scarce condition

XGBoost outperforms deep learning models on key properties such as dielectric constant

glass transition temperature

melting point

and mechanical strength

achieving

scores of 0.65–0.87. To further showcase the practical utility of the database

we propose potential polymers for two energy-relevant applications: high temperature polymer dielectrics and fuel cell membranes. By offering a consistent and accessible benchmark and database

OpenPoly paves the way for more accurate polymer-property modeling and fosters data-driven advances in polymer genome engineering.

关键词

Keywords

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