A fast transferable method for predicting the glass transition temperature of polymers from chemical structure
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Brierley-Croft, Sebastian Olmsted, Peter D. Hine, Peter J. Mandle, Richard J. Chaplin, Adam Grasmeder, John Mattsson, Johan |
| author_facet | Brierley-Croft, Sebastian Olmsted, Peter D. Hine, Peter J. Mandle, Richard J. Chaplin, Adam Grasmeder, John Mattsson, Johan |
| contents | We present a new method that successfully predicts the glass transition temperature $T_{\! \textrm{g}}$ of polymers based on their monomer structure. The model combines ideas from Group Additive Properties (GAP) and Quantitative Structure Property Relationship (QSPR) methods, where GAP (or Group Contributions) assumes that sub-monomer motifs contribute additively to $T_{\! \textrm{g}}$, and QSPR links $T_{\! \textrm{g}}$ to the physico-chemical properties of the structure through a set of molecular descriptors. This method yields fast and accurate predictions of $T_{\! \textrm{g}}$ for polymers based on chemical motifs outside the data sample, which resolves the main limitation of the GAP approach. Using a genetic algorithm, we show that only two molecular descriptors are necessary to predict $T_{\! \textrm{g}}$ for PAEK polymers. Our QSPR-GAP method is readily transferred to other physical properties, to measures of activity (QSAR), or to different classes of polymers such as conjugated or bio-polymers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A fast transferable method for predicting the glass transition temperature of polymers from chemical structure Brierley-Croft, Sebastian Olmsted, Peter D. Hine, Peter J. Mandle, Richard J. Chaplin, Adam Grasmeder, John Mattsson, Johan Soft Condensed Matter Disordered Systems and Neural Networks Materials Science Data Analysis, Statistics and Probability We present a new method that successfully predicts the glass transition temperature $T_{\! \textrm{g}}$ of polymers based on their monomer structure. The model combines ideas from Group Additive Properties (GAP) and Quantitative Structure Property Relationship (QSPR) methods, where GAP (or Group Contributions) assumes that sub-monomer motifs contribute additively to $T_{\! \textrm{g}}$, and QSPR links $T_{\! \textrm{g}}$ to the physico-chemical properties of the structure through a set of molecular descriptors. This method yields fast and accurate predictions of $T_{\! \textrm{g}}$ for polymers based on chemical motifs outside the data sample, which resolves the main limitation of the GAP approach. Using a genetic algorithm, we show that only two molecular descriptors are necessary to predict $T_{\! \textrm{g}}$ for PAEK polymers. Our QSPR-GAP method is readily transferred to other physical properties, to measures of activity (QSAR), or to different classes of polymers such as conjugated or bio-polymers. |
| title | A fast transferable method for predicting the glass transition temperature of polymers from chemical structure |
| topic | Soft Condensed Matter Disordered Systems and Neural Networks Materials Science Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2411.06461 |