  Nanoindentation, a common technique for probing the mechanical properties of
crystalline materials, exhibits both surface and bulk-dominated responses that
are linked to the parent crystal's elasticity and plasticity. For FCC crystals,
the nanoindentation response is primarily controlled by the indented-grain's
crystalline orientation and dislocation density. However, the indentation
properties, such as hardness, have ambiguous significance at the nanoscale due
to tip-specific size effects. Through extensive discrete dislocation dynamics
simulations of flat-punch nanoindentation on FCC single crystals, we show that
a one-to-one correspondence between an indented location and the pre-existing
dislocation density is possible with the help of statistical, machine-learning
algorithms. We cluster and classify various experimentally plausible ensembles
with varying dislocation density and/or crystalline orientation, using
nanoindentation force-depth curves or post-indent surface displacement images.
We use this classification to statistically predict the nanoindentation
response of a given location, through the average response of classified
load-displacement curves. In addition, we demonstrate that our simulation
results qualitatively agree with experimental data on common FCC single
crystals. Ultimately, we propose a novel pathway to uncover the depth
dependence of local surface material properties for more complete,
non-destructive, microstructural characterization, through nanoindentation.
