  High performance materials, from natural bone over ancient damascene steel to
modern superalloys, typically possess a complex structure at the microscale.
Their properties exceed those of the individual components and their
knowledge-based improvement therefore requires understanding beyond that of the
components' individual behaviour. Electron microscopy has been instrumental in
unravelling the most important mechanisms of co-deformation and in-situ
deformation experiments have emerged as a popular and accessible technique.
However, a challenge remains: to achieve high spatial resolution and
statistical relevance in combination. Here, we overcome this limitation by
using panoramic imaging and machine learning to study damage in a dual-phase
steel. This high-throughput approach not only gives us strain and
microstructure dependent insights across a large area of this heterogeneous
material, but also encourages us to expand current research past interpretation
of exemplary cases of distinct damage sites towards the less clear-cut reality.
