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Inelastic mechanical responses in solids, such as plasticity, damage andcrack initiation, are typically modeled in constitutive ways that displaymicrostructural and loading dependence. Nevertheless, {linear} elasticity atinfinitesimal deformations is used for microstructural properties. Wedemonstrate a framework that builds on sequences of microstructural images todevelop fingerprints of inelastic tendencies, and then use them for data-richpredictions of mechanical responses up to failure. In analogy to commonfingerprints, we show that these two-dimensional instability-precursorsignatures may be used to reconstruct the full mechanical response of unknownsample microstructures; this feat is achieved by reconstructing appropriateaverage behaviors with the assistance of a deep convolutional neural networkthat is fine-tuned for image recognition. We demonstrate basic aspects ofmicrostructural fingerprinting in a toy model of dislocation plasticity andthen, we illustrate the method's scalability and robustness in phase fieldsimulations of model binary alloys under mode-I fracture loading.

Microstructural Inelastic Fingerprints And Data-Rich Predictions of Plasticity and Damage in Solids