  Elastoplastic lattice models for the response of solids to deformation
typically incorporate structure only implicitly via a local yield strain that
is assigned to each site. However, the local yield strain can change in
response to a nearby or even distant plastic event in the system. This
interplay is key to understanding phenomena such as avalanches in which one
plastic event can trigger another, leading to a cascade of events, but
typically is neglected in elastoplastic models. To include the interplay one
could calculate the local yield strain for a given particulate system and
follow its evolution, but this is expensive and requires knowledge of particle
interactions, which is often hard to extract from experiments. Instead, we
introduce a structural quantity, "softness," obtained using machine learning to
correlate with imminent plastic rearrangements. We show that softness also
correlates with local yield strain. We incorporate softness to construct a
"structuro-elasto-plasticity" model that reproduces particle simulation results
quantitatively for several observable quantities, confirming that we capture
the influence of the interplay of local structure, plasticity, and elasticity
on material response.
