In this work, we presented a combined experimental/computational approach that allows us to characterize 254 the (complex) atomic dynamics present in Au NPs in realistic relevant conditions. Leveraging high- resolution ADF-STEM microscopy, we demonstrate the potential of this approach by obtaining ten images of a real Au NP every 0.6 seconds along a total of 6 seconds of experimental data acquisition. These images are then used to reconstruct as many atomic precise 3D structures of the Au NP, which are then used as a starting point for ten microseconds-long independent MD simulations. By integrating advanced descriptors of atomic environments with machine learning, we can track the atomic-scale rearrangements of the individual atoms on the NP over time, combining in this way the high spatiotemporal resolution of fully atomistic MD and the advantage of starting from multiple (time-decorrelated -experimental dt=0.6 s) NP structures. This, in fact, allows us to minimize the typical sampling limitations of MD simulations starting, e.g., from initially perfect (ideal) NP structures.