A Simple and Effective Boundary Model in Nonequilibrium Molecular Dynamics Method

Abstract We propose a simple and effective boundary model in a nonequilibrium molecular dynamics （NEMD） simulation to study the out-of-equilibrium dynamics of polymer fluids. The present boundary model can effectively weaken the depletion effect and the slip effect near the boundary, and remove the unwanted heat instantly. The validity of the boundary model is checked by investigating the flow behavior of dilute polymer solution driven by an external force. Reasonable density distributions of both polymer and solvent particles, velocity profiles of the solvent and temperature profiles of the system are obtained. Furthermore, the studied polymer chain shows a cross-streaming migration towards center of the tube, which is consistent with that predicted in previous literatures. These numerical results give powerful evidences for the validity of the present boundary model. Besides, the boundary model can also be used in other flows in addition to the Poiseuille flow.

Probing Intermittent Motion of Polymer Chains in Weakly Attractive Nanocomposites

In this study,we investigate the motion of polymer segments in polymer/nanoparticle composites by varying nanoparticle(NP)volume fractions.By studying the probability distribution of segment displacement,segment trajectory,and the square displacement of segment,we find the intermittent motion of segments,accompanied with the coexistence of slow and fast segments in polymer nanocomposites(PNCs).The displacement distribution of segments exhibits an exponential tail,rather than a Gaussian form.The intermittent dynamics of chain segments is comprised of a long-range jump motion and a short-range localized motion,which is mediated by the weakly attractive interaction between NP and chain segment and the strong confinement induced by NPs.Meanwhile,the intermittent motion of chain segments can be described by the adsorption-desorption transition at low particle loading and confinement effect at high particle loading.These findings may provide important information for understanding the anomalous motion of polymer chains in the presence of NPs.

Data-Driven Exploration of Polymer Processing Effects on the Mechanical Properties in Carbon Black-Reinforced Rubber Composites

The performance and corresponding applications of polymer nanocomposites are highly dominated by the choice of base material,type of fillers,and the processing ways.Carbon black-filled rubber composites(CRC)exemplify this,playing a crucial role in various industries.However,due to the complex interplay between these factors and the resulting properties,a simple yet accurate model to predict the mechanical properties of CRC,considering different rubbers,fillers,and processing techniques,is highly desired.This study aims to predict the dispersion of fillers in CRC and forecast the resultant mechanical properties of CRC by leveraging machine learning.We selected various rubbers and carbon black fillers,conducted mixing and vulcanizing,and subsequently measured filler dispersion and tensile performance.Based on 215 experimental data points,we evaluated the performance of different machine learning models.Our findings indicate that the manually designed deep neural network(DNN)models achieved superior results,exhibiting the highest coefficient of determination(R^(2))values(>0.95).Shapley additive explanations(SHAP)analysis of the DNN models revealed the intricate relationship between the properties of CRC and process parameters.Moreover,based on the robust predictive capabilities of the DNN models,we can recommend or optimize CRC fabrication process.This work provides valuable insights for employing machine learning in predicting polymer composite material properties and optimizing the fabrication of high-performance CRC.