Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (08:55:58 CEST)
How to cite:
Ascencio Medina, E.; He, S.; Daghighi, A.; Iduoku, K.; Casanola-Martin, G. M.; Arrasate, S.; Gonzalez-Diaz, H.; Rasulev, B. Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR). Preprints2024, 2024080884. https://doi.org/10.20944/preprints202408.0884.v1
Ascencio Medina, E.; He, S.; Daghighi, A.; Iduoku, K.; Casanola-Martin, G. M.; Arrasate, S.; Gonzalez-Diaz, H.; Rasulev, B. Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR). Preprints 2024, 2024080884. https://doi.org/10.20944/preprints202408.0884.v1
Ascencio Medina, E.; He, S.; Daghighi, A.; Iduoku, K.; Casanola-Martin, G. M.; Arrasate, S.; Gonzalez-Diaz, H.; Rasulev, B. Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR). Preprints2024, 2024080884. https://doi.org/10.20944/preprints202408.0884.v1
APA Style
Ascencio Medina, E., He, S., Daghighi, A., Iduoku, K., Casanola-Martin, G. M., Arrasate, S., Gonzalez-Diaz, H., & Rasulev, B. (2024). Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR). Preprints. https://doi.org/10.20944/preprints202408.0884.v1
Chicago/Turabian Style
Ascencio Medina, E., Humerto Gonzalez-Diaz and Bakhtiyor Rasulev. 2024 "Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)" Preprints. https://doi.org/10.20944/preprints202408.0884.v1
Abstract
The dielectric constant (ε) reflects the ability of a material to align and orient electrical dipoles within its structure in response to an externally applied electric field; the greater the polarizability of molecules, the greater the value of dielectric constant. In this study a data set of 86 polymers was investigated to develop a structure-property quantitative relations (QSPR) model to predict the dielectric constant in polymers. An initial set of 1273 descriptors was used to select a best set of descriptors and to construct two machine learning models with Gradient Boosting Regressor (GB_A and GB_B). The best performing model (GB_A) with 8 descriptors, exhibited a performance of (R2train) = 0.938 and (R2test) = 0.802. The models were internally validated by 5 folds cross-validation, demonstrating robustness. Additionally, using the Accumulative Local Effect (ALE) technique, we analyzed the relationship between the 8 descriptors involved and the impact of these descriptors to dielectric constant of polymers.
Keywords
dielectric constant; polymers; QSPR; Gradient Boosting Regressor; Accumulated Local Effect
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
