Research Article
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Year 2022, Volume: 35 Issue: 1, 235 - 254, 01.03.2022
https://doi.org/10.35378/gujs.810948

Abstract

References

  • [1] Raimbault, N., Grisafi, A., Ceriotti, M., Rossi, M., “Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals”, New Journal of Physics, 21: 105001, (2019).
  • [2] Pilania, G., Balachandran, P., Gubernatis, J. E., Lookman, T., “Classification of ABO3 perovskite solids: a machine learning study”, Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 71: 507-513, (2015).
  • [3] Pilania, G., Gubernatis, J. E., Lookman, T., “Structure classification and melting temperature prediction in octet AB solids via machine learning”, Physical Review B, 91: 214302, (2015).
  • [4] Huan, T.D., Mannodi-Kanakkithodi, A., Ramprasad, R., “Accelerated materials property predictions and design using motif-based fingerprints”, Physical Review B, 92: 014106, (2015).
  • [5] Rupp, M., Tkatchenko, A., Müller, K.-R., Von Lilienfeld, O.A., “Fast and accurate modeling of molecular atomization energies with machine learning”, Physical Review Letters, 108: 058301, (2012).
  • [6] Dey, P., Bible, J., Datta, S., Broderick, S., Jasinski, J., Sunkara, M., Menon, M., Rajan, K., “Informatics-aided bandgap engineering for solar materials”, Computational Materials Science, 83: 185-195, (2014).
  • [7] Lee, J., Seko, A., Shitara, K., Nakayama, K., Tanaka, I., “Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques”, Physical Review B, 93: 115104, (2016).
  • [8] Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., Ramprasad, R., “Accelerating materials property predictions using machine learning”, Scientific Reports, 3: 2810, (2013).
  • [9] Guo, D., Wang, Y., Nan, C., Li, L., Xia, J., “Application of artificial neural network technique to the formulation design of dielectric ceramics, Sensors and Actuators A: Physical, 102: 93-98, (2002).
  • [10] Habibi-Yangjeh, A., “Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks”, Physics and Chemistry of Liquids, 45: 471-478, (2007).
  • [11] Kılıç, M., Eyecioğlu, Ö., Özdemir, Z.G., Alkan, Ü. “DYPE/PANI kompozit filmlerin sıcaklığa ve PANI katkı konsantrasyonuna bağlı olarak dielektrik parametrelerinin GRSA ile tahmini”, Journal of The Faculty of Engineering and Architecture of Gazi University, 35: 1077-1088, (2020).
  • [12] Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T., Ramprasad, R., “Machine learning strategy for accelerated design of polymer dielectrics”, Scientific Reports, 6: 20952, (2016).
  • [13] Schütt, K. T., Glawe, H., Brockherde, F., Sanna, A., Müller, K.-R., Gross, E.K., “How to represent crystal structures for machine learning: Towards fast prediction of electronic properties”, Physical Review B, 89: 205118, (2014).
  • [14] Schmidt, E., Fowler A. T., Elliott, J. A., Paul D. Bristowe, P. D., “Learning models for electron densities with Bayesian regression”, Computational Materials Science, 149: 250-258, (2018).
  • [15] Owolabi, T. O., Akande, K. O., Olatunji, S. O., “Prediction of superconducting transition temperatures for Fe- based superconductors using support vector machine”, Advances in Physics Theories and Applications, 35: 12-26, (2014).
  • [16] Ponte, P., Melko, R. G., “Kernel methods for interpretable machine learning of order parameters”, Physical Review B, 96: 205146, (2017).
  • [17] Caro-Gutiérrez, J., González-Navarro, F. F., Curiel-Álvarez, M. A., Peréz-Landeros, O. M. B., Valdez-Salas, Radnev-Nedev, N., “Machine learning for predicting the average length of vertically aligned TiO2 nanotubes”, AIP Advances, 10: 075116, (2020).
  • [18] Zhang, Y., Xu, X., “Curie temperature modeling of magnetocaloric lanthanum manganites using Gaussian process regression”, Journal of Magnetism and Magnetic Materials, 512: 166998 (2020).
  • [19] Yang, K., Huang, X., Huang, Y., Xie, L., Jian, P., “Fluoro-polymer@ BaTiO3 hybrid nanoparticles prepared via RAFT polymerization: toward ferroelectric polymer nanocomposites with high dielectric constant and low dielectric loss for energy storage application”, Chemistry of Materials, 25: 2327-2338, (2013).
  • [20] Bishop, C. M., Pattern Recognition and Machine Learning, Springer, USA, (2006).
  • [21] Rasmussen, C. E., Williams, C. K., Gaussian Processes for Machine Learning, The MIT Press, USA, (2006).
  • [22] Rasmussen, C. E., Gaussian processes in machine learning, in: Summer School on Machine Learning, Springer, (2003).
  • [23] Neal, R. M., Bayesian learning for neural networks, Springer Science & Business Media, New York, (2012).
  • [24] Kılıç, M., “Natural additive material for desirable dielectric properties of polypyrrole: Limestone”, Synthetic Metals, 260: 116297, (2020).

Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations

Year 2022, Volume: 35 Issue: 1, 235 - 254, 01.03.2022
https://doi.org/10.35378/gujs.810948

Abstract

The present study deals with the application of the supervised machine learning regression algorithms known as Linear Regression (LR), Support Vector Machine (SVM), and Gaussian process regression (GPR) to the frequency and temperature-dependent dielectric parameters of polymer/inorganic film composites. The frequency and temperature-dependent experimental data set of the dielectric parameters (ε^' and ε^'') of Polypyrrole/Kufeki Stone (PPy/KS) has been utilized. ML models were compared based on their model performance and the most suitable was chosen. After choosing the most suitable ML model, at first, the predictions of the same dielectric parameters of the same samples for different temperatures have been made. Then, the predictions of temperature and frequency-dependent ε^' and ε^'' have been performed for the new PPy based composites consisting of different KS additives that were not produced experimentally. As a result of machine learning, the saturation for KS reinforcing material weight % for dielectric parameters has been determined for capacitor applications. In the light of experimental data and the estimations made by the GPR algorithm, some specific KS additive percentage, working temperature, and frequency ranges have been suggested for the capacitor applications of PPy. 

References

  • [1] Raimbault, N., Grisafi, A., Ceriotti, M., Rossi, M., “Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals”, New Journal of Physics, 21: 105001, (2019).
  • [2] Pilania, G., Balachandran, P., Gubernatis, J. E., Lookman, T., “Classification of ABO3 perovskite solids: a machine learning study”, Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 71: 507-513, (2015).
  • [3] Pilania, G., Gubernatis, J. E., Lookman, T., “Structure classification and melting temperature prediction in octet AB solids via machine learning”, Physical Review B, 91: 214302, (2015).
  • [4] Huan, T.D., Mannodi-Kanakkithodi, A., Ramprasad, R., “Accelerated materials property predictions and design using motif-based fingerprints”, Physical Review B, 92: 014106, (2015).
  • [5] Rupp, M., Tkatchenko, A., Müller, K.-R., Von Lilienfeld, O.A., “Fast and accurate modeling of molecular atomization energies with machine learning”, Physical Review Letters, 108: 058301, (2012).
  • [6] Dey, P., Bible, J., Datta, S., Broderick, S., Jasinski, J., Sunkara, M., Menon, M., Rajan, K., “Informatics-aided bandgap engineering for solar materials”, Computational Materials Science, 83: 185-195, (2014).
  • [7] Lee, J., Seko, A., Shitara, K., Nakayama, K., Tanaka, I., “Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques”, Physical Review B, 93: 115104, (2016).
  • [8] Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., Ramprasad, R., “Accelerating materials property predictions using machine learning”, Scientific Reports, 3: 2810, (2013).
  • [9] Guo, D., Wang, Y., Nan, C., Li, L., Xia, J., “Application of artificial neural network technique to the formulation design of dielectric ceramics, Sensors and Actuators A: Physical, 102: 93-98, (2002).
  • [10] Habibi-Yangjeh, A., “Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks”, Physics and Chemistry of Liquids, 45: 471-478, (2007).
  • [11] Kılıç, M., Eyecioğlu, Ö., Özdemir, Z.G., Alkan, Ü. “DYPE/PANI kompozit filmlerin sıcaklığa ve PANI katkı konsantrasyonuna bağlı olarak dielektrik parametrelerinin GRSA ile tahmini”, Journal of The Faculty of Engineering and Architecture of Gazi University, 35: 1077-1088, (2020).
  • [12] Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T., Ramprasad, R., “Machine learning strategy for accelerated design of polymer dielectrics”, Scientific Reports, 6: 20952, (2016).
  • [13] Schütt, K. T., Glawe, H., Brockherde, F., Sanna, A., Müller, K.-R., Gross, E.K., “How to represent crystal structures for machine learning: Towards fast prediction of electronic properties”, Physical Review B, 89: 205118, (2014).
  • [14] Schmidt, E., Fowler A. T., Elliott, J. A., Paul D. Bristowe, P. D., “Learning models for electron densities with Bayesian regression”, Computational Materials Science, 149: 250-258, (2018).
  • [15] Owolabi, T. O., Akande, K. O., Olatunji, S. O., “Prediction of superconducting transition temperatures for Fe- based superconductors using support vector machine”, Advances in Physics Theories and Applications, 35: 12-26, (2014).
  • [16] Ponte, P., Melko, R. G., “Kernel methods for interpretable machine learning of order parameters”, Physical Review B, 96: 205146, (2017).
  • [17] Caro-Gutiérrez, J., González-Navarro, F. F., Curiel-Álvarez, M. A., Peréz-Landeros, O. M. B., Valdez-Salas, Radnev-Nedev, N., “Machine learning for predicting the average length of vertically aligned TiO2 nanotubes”, AIP Advances, 10: 075116, (2020).
  • [18] Zhang, Y., Xu, X., “Curie temperature modeling of magnetocaloric lanthanum manganites using Gaussian process regression”, Journal of Magnetism and Magnetic Materials, 512: 166998 (2020).
  • [19] Yang, K., Huang, X., Huang, Y., Xie, L., Jian, P., “Fluoro-polymer@ BaTiO3 hybrid nanoparticles prepared via RAFT polymerization: toward ferroelectric polymer nanocomposites with high dielectric constant and low dielectric loss for energy storage application”, Chemistry of Materials, 25: 2327-2338, (2013).
  • [20] Bishop, C. M., Pattern Recognition and Machine Learning, Springer, USA, (2006).
  • [21] Rasmussen, C. E., Williams, C. K., Gaussian Processes for Machine Learning, The MIT Press, USA, (2006).
  • [22] Rasmussen, C. E., Gaussian processes in machine learning, in: Summer School on Machine Learning, Springer, (2003).
  • [23] Neal, R. M., Bayesian learning for neural networks, Springer Science & Business Media, New York, (2012).
  • [24] Kılıç, M., “Natural additive material for desirable dielectric properties of polypyrrole: Limestone”, Synthetic Metals, 260: 116297, (2020).
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Physics
Authors

Önder Eyecioglu 0000-0001-7770-2826

Yaşar Karabul 0000-0002-0789-556X

Mehmet Kılıç 0000-0003-1882-0405

Zeynep Güven Özdemir 0000-0001-5085-5814

Publication Date March 1, 2022
Published in Issue Year 2022 Volume: 35 Issue: 1

Cite

APA Eyecioglu, Ö., Karabul, Y., Kılıç, M., Güven Özdemir, Z. (2022). Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science, 35(1), 235-254. https://doi.org/10.35378/gujs.810948
AMA Eyecioglu Ö, Karabul Y, Kılıç M, Güven Özdemir Z. Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science. March 2022;35(1):235-254. doi:10.35378/gujs.810948
Chicago Eyecioglu, Önder, Yaşar Karabul, Mehmet Kılıç, and Zeynep Güven Özdemir. “Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations”. Gazi University Journal of Science 35, no. 1 (March 2022): 235-54. https://doi.org/10.35378/gujs.810948.
EndNote Eyecioglu Ö, Karabul Y, Kılıç M, Güven Özdemir Z (March 1, 2022) Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science 35 1 235–254.
IEEE Ö. Eyecioglu, Y. Karabul, M. Kılıç, and Z. Güven Özdemir, “Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations”, Gazi University Journal of Science, vol. 35, no. 1, pp. 235–254, 2022, doi: 10.35378/gujs.810948.
ISNAD Eyecioglu, Önder et al. “Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations”. Gazi University Journal of Science 35/1 (March 2022), 235-254. https://doi.org/10.35378/gujs.810948.
JAMA Eyecioglu Ö, Karabul Y, Kılıç M, Güven Özdemir Z. Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science. 2022;35:235–254.
MLA Eyecioglu, Önder et al. “Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations”. Gazi University Journal of Science, vol. 35, no. 1, 2022, pp. 235-54, doi:10.35378/gujs.810948.
Vancouver Eyecioglu Ö, Karabul Y, Kılıç M, Güven Özdemir Z. Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science. 2022;35(1):235-54.