  High-cycle fatigue is a critical performance metric of structural alloys for
many applications. The high cost, time, and labor involved in experimental
fatigue testing call for efficient and accurate computer models of fatigue
life. We present graph neural networks for polycrystals that, for the first
time, can (i) predict fatigue indicator parameters -- grain-level responses to
cyclic loading well beyond monotonic elastic and inelastic regimes reported in
literature; and (ii) generalize these predictions to large microstructure
volume elements with grain populations well beyond those used in training.
These advances can make significant contributions to statistically rigorous and
computationally efficient modeling of high-cycle fatigue -- a long-standing
challenge in the field.
