Accurate simulations of metal under heavy shocks, leading to fragmentation and ejection of particles, cannot be achieved by simply hydrodynamic models and require to be performed at atomic scale using molecular dynamics methods. In order to cope with billions of particles exposed to short range interactions, such molecular dynamics methods need to be highly optimized over massively parallel supercomputers. In this paper, we propose to leverage Adaptive Mesh Refinement techniques to improve efficiency of molecular dynamics code on highly heterogeneous particle configurations. We introduce a series of techniques that optimize the force computation loop using multi-threading and vectorization-friendly data structures. Our design is guided by the need for load balancing and adaptivity raised by highly dynamic particle sets. We analyze performance results on several simulation scenarios, such as the production of an ejecta cloud from shock-loaded metallic surfaces, using a large number of nodes equipped by Intel Xeon Phi Knights Landing processors. Performance obtained with our new Molecular Dynamics code achieves speedups greater than 1.38 against the state-of-the-art LAMMPS implementation.