Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

Abstract

A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations.

All-atom molecular dynamics simulation of structure, dynamics and mechanics of elastomeric polymer materials in a wide range of pressure and temperature

“…Through the utilization of molecular simulation techniques, researchers have delved into the inherent nature and distinctive characteristics of rubber materials, providing indispensable theoretical support for their design, development and practical applications. [9][10][11][12] Liu et al 13 utilized a coarse-grained (CG) model to construct an idealized elastomer model, examining the impact of pressure on the glass transition, structural characteristics, and dynamic behavior of the model elastomer. Hooper et al 14 employed MD simulation to investigate the isothermal compression behavior of PDMS, 1,4-polybutadiene and Estane® models under high-pressure conditions, with a subsequent comparison to experimental data.…”