Статья

Quantitative Structure─Permittivity Relationship Study of a Series of Polymers

Yevhenii ZhuravskyiDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesKweeni IduokuDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesMeade EricksonDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesAnas KaruthDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesDurbek UsmanovDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesGerardo M. Casañola‐MartínDepartment of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United StatesMaqsud SayfiyevDepartment of Chemistry, National University of Uzbekistan, Tashkent 100174, UzbekistanDilshod ZiyaevDepartment of Chemistry, National University of Uzbekistan, Tashkent 100174, UzbekistanZulayho SmanovaDepartment of Chemistry, National University of Uzbekistan, Tashkent 100174, UzbekistanAlicja MikołajczykLaboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, PolandBakhtiyor RasulevDepartment of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan

ACS Materials Aujournal2024en

ABI

Аннотация

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, with R2 values 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.

Темы

Dielectric materials and actuators Polymer Nanocomposites and Properties Synthesis and properties of polymers

Идентификаторы

DOI: 10.1021/acsmaterialsau.3c00079

Цитирования и источники

Цитирований: 0Использованных источников: 51

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