Optimization of Mechanical and Thermal Properties of Polymer Composites Reinforced with Carbon Nanotubes Using Deep Neural Networks and Molecular Dynamics
Subject Areas : Journal of New Applied and Computational Findings in Mechanical Systems
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Keywords: Deep Neural Networks, Molecular Dynamics, Polymer Nanocomposites, Carbon Nanotubes, Mechanical Properties, ,
Abstract :
This study introduces an innovative hybrid framework integrating Deep Neural Networks (DNN) and Molecular Dynamics (MD) to accurately predict the mechanical and thermal properties of polymer-carbon nanotube composites. The DNN model, trained on quantum mechanical data, was employed as a highly accurate force field within MD simulations. Results demonstrated the outstanding superiority of the proposed model, achieving a significant reduction in Root Mean Square Error (RMSE) for energy and force calculations to 2.1 meV/atom and 0.08 eV/Å, respectively. This high precision enabled successful prediction of macroscopic properties. The incorporation of just 5 wt% carbon nanotubes led to a remarkable 224% enhancement in Young's modulus and a 743% improvement in thermal conductivity. In terms of computational efficiency, simulation time for large-scale systems was reduced by up to 94%. Validation against experimental data confirmed a relative error of less than 5% for key properties. This framework provides a powerful and reliable tool for the engineered design of polymer nanocomposites, with significant applications in advanced industries such as aerospace, automotive, and electronics.
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