Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides.
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| Title: | Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides. |
|---|---|
| Authors: | Cui, Tingting1 (AUTHOR), Liu, Heng1 (AUTHOR), Liu, Xin1 (AUTHOR), Min, Yonggang1 (AUTHOR) ygmin@gdut.edu.cn |
| Source: | Materials (1996-1944). Dec2025, Vol. 18 Issue 24, p5541. 12p. |
| Subjects: | Glass transition temperature, Polyimides, Prediction models, Computer-assisted molecular design, Genetic algorithms, Multiple regression analysis, Thermodynamic laws |
| Abstract: | Highlights: What are the main findings? A robust seven-descriptor QSPR model predicts polyimide Tg via GA-MLR. Key descriptors (Chi0n, MinPartialCharge) govern chain rigidity and interactions. Tg is controlled by modulation of free volume, unifying the structure-property link. What are the implications of the main findings? The model provides direct, interpretable physicochemical insights into Tg. Free volume theory offers a unified mechanism for diverse molecular features. Actionable design principles are given for tailoring Tg via molecular architecture. The rational design of polyimides (PIs) with targeted glass transition temperature (Tg) is crucial for advanced microelectronics applications. While data-driven approaches offer promise, there is a pressing need for models that are not only predictive but also physically interpretable, especially with limited datasets. Herein, we present a highly interpretable Quantitative Structure-Property Relationship (QSPR) model for accurate Tg prediction of PIs. Employing a Genetic Algorithm combined with Multiple Linear Regression (GA-MLR), we identified an optimal set of seven molecular descriptors from a curated dataset. The model demonstrates robust predictive performance and strong generalization ability, validated through rigorous statistical tests. Crucially, we provide a deep physicochemical interpretation of the descriptors, unifying their influence under the framework of free volume theory. We show that key descriptors govern Tg by modulating the fractional free volume through distinct mechanisms: descriptors like Chi0n increase free volume by introducing molecular branching that disrupts chain packing, while MinPartialCharge influences Tg through its effect on intermolecular interactions. This mechanistic understanding is translated into clear molecular design guidelines, distinguishing strategies for achieving high-Tg versus processable, low-Tg polymers. Our work establishes a reliable and transparent computational tool that bridges data-driven prediction with fundamental chemical insight for accelerating PIs development. [ABSTRACT FROM AUTHOR] |
| Copyright of Materials (1996-1944) is the property of MDPI 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190471474 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cui%2C+Tingting%22">Cui, Tingting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Heng%22">Liu, Heng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xin%22">Liu, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Min%2C+Yonggang%22">Min, Yonggang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ygmin@gdut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Dec2025, Vol. 18 Issue 24, p5541. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Glass+transition+temperature%22">Glass transition temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Polyimides%22">Polyimides</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+molecular+design%22">Computer-assisted molecular design</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Thermodynamic+laws%22">Thermodynamic laws</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A robust seven-descriptor QSPR model predicts polyimide Tg via GA-MLR. Key descriptors (Chi0n, MinPartialCharge) govern chain rigidity and interactions. Tg is controlled by modulation of free volume, unifying the structure-property link. What are the implications of the main findings? The model provides direct, interpretable physicochemical insights into Tg. Free volume theory offers a unified mechanism for diverse molecular features. Actionable design principles are given for tailoring Tg via molecular architecture. The rational design of polyimides (PIs) with targeted glass transition temperature (Tg) is crucial for advanced microelectronics applications. While data-driven approaches offer promise, there is a pressing need for models that are not only predictive but also physically interpretable, especially with limited datasets. Herein, we present a highly interpretable Quantitative Structure-Property Relationship (QSPR) model for accurate Tg prediction of PIs. Employing a Genetic Algorithm combined with Multiple Linear Regression (GA-MLR), we identified an optimal set of seven molecular descriptors from a curated dataset. The model demonstrates robust predictive performance and strong generalization ability, validated through rigorous statistical tests. Crucially, we provide a deep physicochemical interpretation of the descriptors, unifying their influence under the framework of free volume theory. We show that key descriptors govern Tg by modulating the fractional free volume through distinct mechanisms: descriptors like Chi0n increase free volume by introducing molecular branching that disrupts chain packing, while MinPartialCharge influences Tg through its effect on intermolecular interactions. This mechanistic understanding is translated into clear molecular design guidelines, distinguishing strategies for achieving high-Tg versus processable, low-Tg polymers. Our work establishes a reliable and transparent computational tool that bridges data-driven prediction with fundamental chemical insight for accelerating PIs development. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Materials (1996-1944) is the property of MDPI 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: 10.3390/ma18245541 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 5541 Subjects: – SubjectFull: Glass transition temperature Type: general – SubjectFull: Polyimides Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Computer-assisted molecular design Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Thermodynamic laws Type: general Titles: – TitleFull: Interpreting Molecular Descriptors for Glass Transition Temperature Prediction and Design of Polyimides. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cui, Tingting – PersonEntity: Name: NameFull: Liu, Heng – PersonEntity: Name: NameFull: Liu, Xin – PersonEntity: Name: NameFull: Min, Yonggang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961944 Numbering: – Type: volume Value: 18 – Type: issue Value: 24 Titles: – TitleFull: Materials (1996-1944) Type: main |
| ResultId | 1 |
