Farayandno

Farayandno

Accurate Prediction of Polymer Specific Heat Capacity Using Advanced Machine Learning Techniques

Document Type : Review

Authors
Zeynab Torabi 1
Elnaz Farzinfar 1
Fatemeh Sepahvand 1
Mohadeseh Sarlak 1
Alireza Baghban 2
1 Student, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran
2 National Iranian South Oilfeilds Company (NISOC), Ahvaz, Iran
10.22034/farayandno.2026.2070453.2013
Abstract
This study focuses on predicting the specific heat of polymers using experimental data and machine learning algorithms. A comprehensive dataset comprising physical and chemical properties of polymers was utilized as input for various models, including linear regression, Ridge, Lasso, ElasticNet, K-nearest neighbors, artificial neural networks, and decision trees. The performance of these models was evaluated using mean squared error (MSE) and R-squared (R²) metrics. The results indicate that the Lasso and ElasticNet models provide the best balance between accuracy and generalization to new data. Specifically, the Lasso model achieved an MSE of 728.77 and an R² of 0.95, while the ElasticNet model recorded an MSE of 604.34 and an R² of 0.95. This study emphasizes that employing regularization techniques and ensemble models can significantly enhance the accuracy and stability of predictions for the thermal properties of polymers, leading to substantial savings in both time and costs associated with experimental testing.
Keywords
Thermal property
Machine learning
Intelligent algorithms
Specific heat
Polymers
Subjects
[1] A. Patil, A. Patel, R. Purohit, An overview of polymeric materials for automotive applications, Materials Today: Proceedings, 4 (2017) 3807-3815.
[2] X. Wei, Z. Wang, Z. Tian, T. Luo, Thermal transport in polymers: a review, Journal of Heat Transfer, 143 (2021) 072101.
[3] E. Ghanbari, S.J. Picken, J.H. van Esch, Analysis of differential scanning calorimetry (DSC): determining the transition temperatures, and enthalpy and heat capacity changes in multicomponent systems by analytical model fitting: E. Ghanbari et al, Journal of Thermal Analysis and Calorimetry, 148 (2023) 12393-12409.
[4] R. Bhowmik, S. Sihn, R. Pachter, J.P. Vernon, Prediction of the specific heat of polymers from experimental data and machine learning methods, Polymer, 220 (2021) 123558.
[5] R.S. Bhoopal, P. Sharma, S. Kumar, A. Pandey, R. Beniwal, R. Singh, Prediction of effective thermal conductivity of polymer composites using an artificial neural network approach, Special Topics & Reviews in Porous Media: An International Journal,  3 (2012).
[6] R. Mortazavi, S. Mortazavi, A. Troncoso, Wrapper-based feature selection using regression trees to predict intrinsic viscosity of polymer, Engineering with Computers, 38 (2022) 2553-2565.
[7] C. Joo, H. Park, H. Kwon, J. Lim, E. Shin, H. Cho, J. Kim, Machine learning approach to predict physical properties of polypropylene composites: application of MLR, DNN, and random forest to industrial data, Polymers, 14 (2022) 3500.
[8] K. Ishikiriyama, Machine learning prediction of heat capacity of polymers as a function of temperature, Polymer, (2025) 129171.
[9] M.-X. Zhu, H.-G. Song, Q.-C. Yu, J.-M. Chen, H.-Y. Zhang, Machine-learning-driven discovery of polymers molecular structures with high thermal conductivity, International Journal of Heat and Mass Transfer, 162 (2020) 120381.
[10] G. Chen, R. Hassan, A. Baghban, Pioneering accurate hydrogen generation from ammonia using cutting-edge modeling techniques, Journal of Industrial and Engineering Chemistry, (2026).
[11] M. Gheytanzadeh, F. Rajabhasani, A. Baghban, S. Habibzadeh, O. Abida, A. Esmaeili, M.T. Munir, Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach, Scientific Reports, 12 (2022) 21902.
[12] R. Hassan, A. Baghban, Beyond the surface: Quasi-SMILES machine learning approaches for precise estimation of organic sorption, Materials Today Communications, (2025) 114126.
[13] R. Hassan, A. Baghban, Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques, Scientific reports, 15 (2025) 24410.
[14] R. Hassan, A. Baghban, AI-based algorithms for estimating hydrochar properties in terms of biomass ultimate analysis, Results in Engineering, 26 (2025) 105141.
[15] R. Soleimani, R. Hassan, A. Baghban, Towards Cleaner Water by Leveraging AI for Optimizing Coagulation Processes in Microplastic Removal, Journal of Environmental Chemical Engineering, (2025) 120725.
[16] R. Soleimani, R. Hassan, Z. Behtouei, A. Baghban, Data-driven prediction of thermal conductivity of soil using advanced computational schemes, International Communications in Heat and Mass Transfer, 169 (2025) 109780.
[17] R. Soleimani, M.M. Kouchi, Z. Behtouei, Z. Ghasemi, A. Baghban, Exploring the solubility potential of anti-cancer and supportive agents in supercritical CO2 through advanced computational intelligence techniques, Journal of CO2 Utilization, 102 (2025) 103227.
[18] Q. Xie, A. Baghban, Data-Driven strategies for predicting metal ion adsorption Behaviour in clay mineral frameworks, Minerals Engineering, 246 (2026) 110377.