Academic Journal
OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions.
| Τίτλος: | OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions. |
|---|---|
| Συγγραφείς: | Wang, Ji-Feng, Sun, Yu-Bo, Chen, Qiu-Tong, Ji, Fei-Fan, Song, Yuan-Yuan, Ruan, Meng-Yuan, Wang, Ying |
| Πηγή: | Chinese Journal of Polymer Science (Springer Science & Business Media B.V.); Oct2025, Vol. 43 Issue 10, p1749-1760, 12p |
| Θεματικοί όροι: | Artificial intelligence, Databases, Benchmarking (Management), Statistical models |
| Περίληψη: | 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 spanning 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 R2 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10118-025-3402-y Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Ji-Feng%22">Wang, Ji-Feng</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yu-Bo%22">Sun, Yu-Bo</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Qiu-Tong%22">Chen, Qiu-Tong</searchLink><br /><searchLink fieldCode="AR" term="%22Ji%2C+Fei-Fan%22">Ji, Fei-Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Yuan-Yuan%22">Song, Yuan-Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Ruan%2C+Meng-Yuan%22">Ruan, Meng-Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Ying%22">Wang, Ying</searchLink> – Name: TitleSource Label: Source Group: Src Data: Chinese Journal of Polymer Science (Springer Science & Business Media B.V.); Oct2025, Vol. 43 Issue 10, p1749-1760, 12p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmarking+%28Management%29%22">Benchmarking (Management)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 spanning 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 R2 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10118-025-3402-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1749 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Databases Type: general – SubjectFull: Benchmarking (Management) Type: general – SubjectFull: Statistical models Type: general Titles: – TitleFull: OpenPoly: A Polymer Database Empowering Benchmarking and Multi-property Predictions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Ji-Feng – PersonEntity: Name: NameFull: Sun, Yu-Bo – PersonEntity: Name: NameFull: Chen, Qiu-Tong – PersonEntity: Name: NameFull: Ji, Fei-Fan – PersonEntity: Name: NameFull: Song, Yuan-Yuan – PersonEntity: Name: NameFull: Ruan, Meng-Yuan – PersonEntity: Name: NameFull: Wang, Ying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02567679 Numbering: – Type: volume Value: 43 – Type: issue Value: 10 Titles: – TitleFull: Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) Type: main |
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