Polymer Nanocomposites (PNCs) have become an emerging class of materials due to their advanced thermal, transport, electrical, and mechanical properties. It has been shown in many experiments and simulations that nanoparticles in the polymer matrix can induce a 'bound layer' of polymers, where the density is higher and the dynamics are much slower than the bulk. This bound layer is widely believed to be responsible for many of the desirable properties of PNCs. Intuitively, the slow dynamics should originate from the compacted structure, however, many studies have shown that the dynamics are usually modified over a wider range than the structure. Thus, there should be at least a second process affecting polymer dynamics in addition to the structure itself. An explicit relationship between the structure and dynamics in the PNCs, especially in the bound layer, is still missing. Moreover, most applications of PNCs require the system to be in a glassy state and under external loads, making this question even more challenging because: (i) the relationship between glassy structure and dynamics is still an open question; (ii) glassy dynamics can be accelerated by external loads and the material can exhibit either brittle or ductile responses before the failure. In this dissertation, we use Molecular Dynamics simulation combined with machine learning analysis and study five different polymer systems. We propose three physical models: the Structure-dependent Eyring Model (StEM), the Interfacial Dynamical Decomposition (IDD) Model, and the Structuro-elasto-plasticity (StEP) Model to describe how the dynamics of PNCs are affected by a variety of processing conditions. These models incorporate a machine-learned structural field, softness, and connect the microscopic structure to dynamics and mechanical responses for both neat polymers and PNCs under active deformation. In addition to these models, we also study the formation process and the exchange dynamics of the bound layer in PNCs, which is critical for tuning the PNCs' properties but where many outstanding questions remain.