{"title":"预测不同结构聚合物的玻璃化转变温度","authors":"Xinliang Yu","doi":"10.1007/s00396-025-05421-8","DOIUrl":null,"url":null,"abstract":"<div><p>This work proposes an accurate quantitative structure–property relationship (QSPR) model for glass transition temperatures (<i>T</i><sub>g</sub>s) of 315 polymers with diverse structures. Support vector regression (SVR), combined with a genetic algorithm, was used to develop the SVR model that achieved a determination coefficient (<i>R</i><sup>2</sup>) of 0.937 and a root-mean-square (<i>rms</i>) error of 25.047 K for 79 <i>T</i><sub>g</sub>s of polymers in the test set. The investigation shows that increasing the percentage of C atoms, molecular polarity, complementary information content index, spectral mean absolute deviation and eigenvalue in augmented adjacency matrix by introducing benzene, naphthalene, anthracene, pyridine, quinoline, imide, and/or C‒N atom pairs with a topological distance of 9, can result in high chain rigidity and high <i>T</i><sub>g</sub>s. Conversely, increasing the free volume and flexible segments by introducing groups such as ‒Si‒O‒, ‒Si‒C‒, and ‒Si‒N‒ in the backbone chain, N and O atom pairs, S and X (heteroatoms) atom pairs with a topological distance of 1, F‒X with a topological distance of 2 and CH<sub>2</sub>RX groups can bring down <i>T</i><sub>g</sub>s.</p><h3>Graphical Abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":520,"journal":{"name":"Colloid and Polymer Science","volume":"303 7","pages":"1287 - 1297"},"PeriodicalIF":2.3000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting glass transition temperatures for structurally diverse polymers\",\"authors\":\"Xinliang Yu\",\"doi\":\"10.1007/s00396-025-05421-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This work proposes an accurate quantitative structure–property relationship (QSPR) model for glass transition temperatures (<i>T</i><sub>g</sub>s) of 315 polymers with diverse structures. Support vector regression (SVR), combined with a genetic algorithm, was used to develop the SVR model that achieved a determination coefficient (<i>R</i><sup>2</sup>) of 0.937 and a root-mean-square (<i>rms</i>) error of 25.047 K for 79 <i>T</i><sub>g</sub>s of polymers in the test set. The investigation shows that increasing the percentage of C atoms, molecular polarity, complementary information content index, spectral mean absolute deviation and eigenvalue in augmented adjacency matrix by introducing benzene, naphthalene, anthracene, pyridine, quinoline, imide, and/or C‒N atom pairs with a topological distance of 9, can result in high chain rigidity and high <i>T</i><sub>g</sub>s. Conversely, increasing the free volume and flexible segments by introducing groups such as ‒Si‒O‒, ‒Si‒C‒, and ‒Si‒N‒ in the backbone chain, N and O atom pairs, S and X (heteroatoms) atom pairs with a topological distance of 1, F‒X with a topological distance of 2 and CH<sub>2</sub>RX groups can bring down <i>T</i><sub>g</sub>s.</p><h3>Graphical Abstract</h3>\\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":520,\"journal\":{\"name\":\"Colloid and Polymer Science\",\"volume\":\"303 7\",\"pages\":\"1287 - 1297\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Colloid and Polymer Science\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00396-025-05421-8\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Colloid and Polymer Science","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00396-025-05421-8","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
摘要
本文提出了315种不同结构聚合物玻璃化转变温度(Tgs)的精确定量结构-性能关系(QSPR)模型。采用支持向量回归(SVR)与遗传算法相结合的方法建立SVR模型,该模型对测试集中79 Tgs聚合物的决定系数(R2)为0.937,均方根误差(rms)为25.047 K。研究表明,通过引入拓扑距离为9的苯、萘、蒽、吡啶、喹啉、亚胺和/或C - n原子对,增加增强邻接矩阵中C原子的百分比、分子极性、互补信息含量指数、光谱平均绝对偏差和特征值,可以获得高链刚性和高Tgs。相反,通过在主链中引入- si - O -、- si - c -、- si - N -等基团、N和O原子对、拓扑距离为1的S和X(杂原子)原子对、拓扑距离为2的F-X和CH2RX基团来增加自由体积和柔性段,可以降低Tgs。图形抽象
Predicting glass transition temperatures for structurally diverse polymers
This work proposes an accurate quantitative structure–property relationship (QSPR) model for glass transition temperatures (Tgs) of 315 polymers with diverse structures. Support vector regression (SVR), combined with a genetic algorithm, was used to develop the SVR model that achieved a determination coefficient (R2) of 0.937 and a root-mean-square (rms) error of 25.047 K for 79 Tgs of polymers in the test set. The investigation shows that increasing the percentage of C atoms, molecular polarity, complementary information content index, spectral mean absolute deviation and eigenvalue in augmented adjacency matrix by introducing benzene, naphthalene, anthracene, pyridine, quinoline, imide, and/or C‒N atom pairs with a topological distance of 9, can result in high chain rigidity and high Tgs. Conversely, increasing the free volume and flexible segments by introducing groups such as ‒Si‒O‒, ‒Si‒C‒, and ‒Si‒N‒ in the backbone chain, N and O atom pairs, S and X (heteroatoms) atom pairs with a topological distance of 1, F‒X with a topological distance of 2 and CH2RX groups can bring down Tgs.
期刊介绍:
Colloid and Polymer Science - a leading international journal of longstanding tradition - is devoted to colloid and polymer science and its interdisciplinary interactions. As such, it responds to a demand which has lost none of its actuality as revealed in the trends of contemporary materials science.