  High entropy alloys are a class of materials with many significant
improvements in terms of mechanical properties as compared to ``classical''
alloys. The corresponding structure-property relations are not yet entirely
clear, but it is commonly believed that the good mechanical performance is
strongly related to dislocation interactions with the complex energy landscape
formed due to alloying. Although in-situ Transmission Electron Microscopy (TEM)
allows high-resolution studies of the structure and dynamics of moving
dislocations and makes the local obstacle/energy ``landscape'' directly visible
in the geometry of dislocations; such observation, however, are merely
qualitative, and detailed three-dimensional analyses of the interaction between
dislocations and the energy landscape is still missing. In this work, we
utilized dislocations as ``probes'' for the local energy maxima which play the
role of pinning points for the dislocation movement. To this end, we developed
a unique data-mining approach that can perform coarse-grained spatio-temporal
analysis, making ensemble averaging of a considerable number of snapshots
possible. We investigate the effect of pinning points on the dislocation
gliding behavior of CoCrFeMnNi alloy during in-situ TEM straining and find that
(i) the pinning point strength changes when dislocations glide through and (ii)
the pinning point moves along the direction close to the Burgers vector
direction. Our data-mining method can be applied to dislocation motion in
general, making it a useful tool for dislocation research.
