Predicting the Physicochemical Properties of Amorphous Polymer Mixtures with Atomistic Molecular Simulation and Data-driven Modeling

Abstract

Molecular dynamics (MD) simulations play a pivotal role in understanding the behavior of complex molecular systems, offering insights into the behavior of molecules at the atomic level, while their accuracy heavily depends on the force field parameters used. In this study, we present an investigation focusing on two distinct aspects: the validation of MD simulations for plasticizers, and the development of a quantitative structure property relationship (QSPR) model to fit data derived from these simulations. Our goal is to provide researchers with valuable insights into the choice of force fields to improve the accuracy of simulations in various scientific domains and the modeling of prediction of properties of plasticizers. In the first part, We explore various aspects of validation, including force field accuracy, equilibration protocols, and comparison of simulation results of plasticizers with experimental data. We begin by validating popular force fields: PCFF, SciPCFF and COMPASS. By examining the behavior of small molecules, we aim to ensure the reliability of force fields for these compounds with specific desired functional groups. Density, heat of vaporization and shear viscosity results are used for the validation of force fields. We compare various equilibration methods and their impact on simulation outcomes to address issues related to system stability and convergence, for enhancing the efficiency and accuracy of simulations. The second part of our research shifts focus to the prediction modeling of plasticizers, a class of chemical additives commonly used in the polymer industry to enhance the flexibility of plastic materials. We attempt to predict the solubility parameters of plasticizers by QSPR. Simple counts, Wiener Indices and Randic Branching Indices are used as descriptors in the QSPR. Our prediction model results show the dependence of plasticizers on the descriptors while the QSPR equation obtained from our current data-set with five descriptors has the R2 = 0.73. In conclusion, this comprehensive study bridges the gap between force field validation and equilibration for plasticizers. Moreover, the integration of QSPR models offers insights to a robust approach for predicting molecular behaviors.