  Crystal plasticity of sub-micron finite volumes is characterized by the flow
of emergent dislocation defects, giving rise to size effects in mechanical
properties and avalanche phenomena. In this chapter, we present a minimal model
for discrete edge dislocations in a finite volume, that has been benchmarked
against experimental data and displays known phenomenological trends. We
discuss how this model can explain seemingly disconnected effects of rate, size
and prior deformation on microcrystal plasticity. We demonstrate the
statistical features of dislocation ensembles for both stress and displacement
controlled loading conditions and explore in detail the connection between
loading rate and displacement bursts. Finally, we present model studies of
machine learning algorithms in microcrystal plasticity that both improve
understanding and clarify the range of such methods' usefulness. In this way,
we elucidate the role of prior deformation history on micro and nano-sized
specimens and we use this understanding to predict the mechanical response of
thin films through microstructural observations of pre-existing dislocation
configurations.
