  Plastic deformation of micron-scale crystalline solids exhibits stress-strain
curves with significant sample-to-sample variations. It is a pertinent question
if this variability is purely random or to some extent predictable. Here we
show, by employing machine learning techniques such as regression neural
networks and support vector machines that deformation predictability evolves
with strain and crystal size. Using data from discrete dislocations dynamics
simulations, the machine learning models are trained to infer the mapping from
features of the pre-existing dislocation configuration to the stress-strain
curves. The predictability vs strain relation is non-monotonic and exhibits a
system size effect: larger systems are more predictable. Stochastic deformation
avalanches give rise to fundamental limits of deformation predictability for
intermediate strains. However, the large-strain deformation dynamics of the
samples can be predicted surprisingly well.
