Publications

Abstract : In the present article, we explored the ballistic behaviour of bilayer graphene (BLG) by performing a series of molecular dynamics (MD) simulations. The computationally expensive nature of large scale MD simulations frequently hinders a thorough understanding of material characterization. To mitigate this lacuna we demonstrated the successful integration of MD simulation with the artificial neural network (ANN). In this regard, the considered input parameter [impact velocity (Vi)] is perturbed in the range of 1–7 km/s using the Monte Carlo sampling technique to construct the sample space with 128 instances. The BLG (size 200 Å × 200 Å) is impacted by a spherical diamond projectile (diameter 25 Å) in a series of MD simulations of high-velocity impact with varied impact velocities. As a response, the residual velocity of the projectile (Vr) and specific penetration energy (E∗p) of the BLG are determined for each instance. The deterministic responses revealed that with the increase in the impact velocity the Vr and E∗p values increases. Besides the numerical responses, the post-impact behaviour of BLG is also classified into four different stages viz. R, PP1, PP2 and CP, based on the extent of damage to the BLG and the post-impact trajectory of the projectile. The dataset generated with the MCS based MD simulation is further used to construct the ANN based regression and classification model. In this manner, the current article proposed a framework to accelerate the nanoscale material characterization by augmenting the ANN with MD simulations.