Mechanism‐Aware Digital Twin for High‐Temperature Creep Prediction in Mo–Re Alloys

Abstract Predicting the long‐term deformation of structural materials under extreme conditions remains a grand challenge in materials science, especially for refractory alloys, where high‐temperature creep limits performance and service life. Here, a physics‐informed digital twin framework is developed that integrates a viscoplastic self‐consistent (VPSC) model, real‐time high‐temperature creep experiments, and a calibration neural network to predict and elucidate the creep behavior of Mo‐14Re alloys. The digital twin accurately reproduces creep curves across 1000–1200 °C and 60–150 MPa, achieving <5% deviation from experiments. Crucially, the learned parameter trajectories uncover a previously unrecognized mechanism: Re solute atoms are dragged by gliding dislocations (“solute‐drag” effect), leading to rhenium segregation at grain boundaries and compromised creep strength. This is corroborated by post‐mortem TEM and molecular dynamics simulations. Furthermore, the model‐guided strategy reveals that nanoscale La ₂ O ₃ precipitates can pin dislocations and suppress Re segregation, significantly improving creep resistance. This work advances the mechanistic understanding of refractory alloy creep and demonstrates a transferable AI‐enabled digital twin approach for materials design under extreme environments.