Compressibility of the polymer crystal

Estimation and Prediction of the Polymers' Physical Characteristics Using the Machine Learning Models

This article investigates the utility of machine learning (ML) methods for predicting and analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of polymers' characteristics, the study encompasses an extensive range of polymer properties, spanning compressive and tensile strength to thermal and electrical behaviors. Using various regression methods like Ensemble, Tree-based, Regularization, and Distance-based, the research undergoes thorough evaluation using the most common quality metrics. As a result of a series of experimental studies on the selection of effective model parameters, those that provide a high-quality solution to the stated problem were found. The best results were achieved by Random Forest with the highest R2 scores of 0.71, 0.73, and 0.88 for glass transition, thermal decomposition, and melting temperatures, respectively. The outcomes are intricately compared, providing valuable insights into the efficiency of distinct ML approaches in predicting polymer properties. Unknown values for each characteristic were predicted, and a method validation was performed by training on the predicted values, comparing the results with the specified variance values of each characteristic. The research not only advances our comprehension of polymer physics but also contributes to informed model selection and optimization for materials science applications.

Crystalline Moduli of Polymers, Evaluated from Density Functional Theory Calculations under Periodic Boundary Conditions

A theoretical methodology based on quantum chemistry to calculate mechanical properties of polymer crystals has been developed and applied to representative polymers. By density functional theory calculations including a dispersion force correction under three-dimensional periodic boundary conditions, crystal structures of poly(methylene oxide) (PMO), polyethylene (PE), poly(ethylene terephthalate) (PET), poly(trimethylene terephthalate) (PTT), and poly(butylene terephthalate) (PBT) were optimized and their mechanical properties, such as crystalline moduli and linear and volume compressibilities, were calculated. The optimized crystal structures were proved to be fully consistent with those determined by X-ray and neutron diffraction. The crystalline moduli (E _∥) parallel to the chain axis were calculated to be 114 GPa (PMO), 333 GPa (PE), 182 GPa (PET), 7.1 GPa (PTT), and 20.8 GPa (PBT) and compared with those determined from X-ray diffraction, Raman spectroscopy, and neutron inelastic scattering experiments. Herein, the E _∥ values thus determined are interpreted in terms of conformational characteristics of the polymeric chains and the validity of the homogeneous stress hypothesis adopted in the X-ray diffraction method is also discussed.