Combining machine learning and molecular dynamics to predict strength-toughness and energy dissipation mechanisms of hybrid double-crosslinked CNT networks

Cross-linking has a significant strengthening effect on the mechanical properties of carbon nanoparticles, but there are still great challenges in how to control their mechanical properties through precise cross-linking ratios. Therefore, we use coarse-grained molecular dynamics (CGMD) to simulate its mechanical properties as data samples and combine it with machine learning methods to predict the optimal strength and toughness of carbon nanotubes (CNTs) corresponding to optimal cross-linking conditions. We found that the cross-linking density range is 2.60 ∼ 2.75 × 10^-4/nm³, and the strong and weak cross-linking ratio range is 91 % S+9% W∼95 % S+5% W, which is the optimal range to achieve the mechanical properties of CNTs. Excessively high cross-link density and strong bond ratios can reduce the total number of cross-link bond breaks, thus decreasing their toughness. Meanwhile, high cross-linking density has a greater impact on the shear and slip between CNTs, tending to make CNTs brittle. Our proposed new approach for extracting data from coarse-grained models can obtain substantial optimization results without requiring much simulation computation. Furthermore, these cross-linking strategies and machine learning algorithms proposed in this work can provide a theoretical basis for controlling the mechanical properties of CNT materials, and also open up a new way for functional network materials other than CNTs.