  Predicting the behaviour of complex systems is one of the main goals of
science. An important example is plastic deformation of micron-scale crystals,
a process mediated by collective dynamics of dislocations, manifested as
broadly distributed strain bursts and significant sample-to-sample variations
in the response to applied loading. Here, by combining large-scale discrete
dislocation dynamics simulations and machine learning, we study the problem of
predicting the fluctuating stress-strain curves of individual small single
crystals subject to strain-controlled loading using features of the initial
dislocation configurations as input. Our results reveal an intriguing rate
dependence of deformation predictability: For small strains predictability
improves with increasing strain rate, while for larger strains the
predictability vs strain rate relation becomes non-monotonic. We show that for
small strains the rate-dependence of deformation predictability can be captured
by considering the fraction of dislocations moving against the direction
imposed by the external stress, serving as a measure of strain-rate-dependent
complexity of the dislocation dynamics. The non-monotonic predictability vs
strain rate relation for large strains is argued to be related to a transition
from fluctuating to smooth plastic flow when strain rate is increased.
