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

High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence