Next generation models to support design of fracture-resistant materials must directly incorporate information related to interatomic bonding and structure of interfaces at nanoscales. The challenge is to develop methods that can address behavior at such small scales yet still account for aspects of average behavior at much higher length scales in structural applications such as automobiles, aircraft, tennis rackets, and so forth. This project will address behavior by linking defects analyzed at the nanoscale using atomistics (molecular dynamics) near the tips of cracks or at interfaces with long range fields that average effects of large numbers of atoms to achieve a so-called ?coarse grain? description. In this way, simulations can be undertaken at high enough length and time scales to provide meaningful information regarding behavior of structures, while the role of interfaces is treated with atomic-scale resolution. This research is expected to substantially impact the ability to tailor the structure of grain boundaries and fine scale microstructure to affect improved fatigue and fracture resistance of metal alloys. Fatigue is a failure mode in metals subjected to cyclic loading that is responsible for costly failures such as highway bridges, airplane tail sections, engine components, railway derailments, and many other examples. By developing more predictive tools to address the large gap in modeling and simulation tools between atomic scales and scales of grains in polycrystals (typically five orders of magnitude), the societal benefits in terms of energy savings and safety derived from more lightweight and durable materials can be substantial. Research results will be integrated into both an undergraduate course in computational materials science as well as graduate courses in defects and mechanical properties of materials taught by the PI and co-PI, accelerating the application of the technology developed in this project.
