Fatigue of Metallic Glasses
Machine-learning interatomic potentials for the microscopic mechanism of fatigue
Metallic glasses combine high strength with the absence of crystalline defects, yet their fatigue limits are low, and the general mechanism behind this remains unclear. Addressing this question requires simulations across a broad range of compositions with accuracy close to first-principles calculations.
To this end, I developed a machine-learning interatomic potential (MLIP) for Zr–Ni–Al metallic glasses trained on DFT data. Using MD simulations of cyclic loading, I am now studying how local atomic structure, such as icosahedral clusters and their networks, relates to atomic rearrangements, to uncover the microscopic mechanisms of fatigue in metallic glasses.