Quantitative Structure─Permittivity Relationship Study of a Series of Polymers
Quantitative Structure─Permittivity Relationship Study of a Series of Polymers
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系列聚合物的定量结构—介电常数关系研究
DOI：
10.1021/acsmaterialsau.3c00079
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Mikolajczyk, Alicja
中科院分区：
文献类型：
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作者：
Zhuravskyi, Yevhenii;Iduoku, Kweeni;Erickson, Meade E.;Karuth, Anas;Usmanov, Durbek;Casanola-Martin, Gerardo;Sayfiyev, Maqsud N.;Ziyaev, Dilshod A.;Smanova, Zulayho;Mikolajczyk, Alicja
介电常数是表征物质在外电场作用下极化程度的一个重要性质，在许多科学领域中有着广泛的应用。在这项工作中，不同的聚合物的介电常数（ε）的结构-性能关系进行了研究。一个透明的机械模型开发与应用的机器学习方法，结合遗传算法和多元线性回归分析，以获得一个机械解释和透明的模型。基于使用各种验证标准进行的评价，提出了四变量和八变量模型。最佳模型对训练集和测试集的预测性能较高，R2值分别为0.905和0.812。对所获得的统计性能结果和最佳模型中所选的描述符进行了分析和讨论。应用验证程序，该模型被证明具有良好的预测能力和鲁棒性，进一步应用于聚合物介电常数预测。
Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure–property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, withR2values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.