Abstract Scope |
Grain boundary (GB) solute interaction influences many interfacial phenomena. While GB solute segregation has been the subject of active research, most work focuses on ground-state, i.e., lowest energy, GB structures. However, in a polycrystal, owing to various constraints, GBs can access metastable states. The effect of GB metastability on solute segregation has not been examined systematically. Herein, using atomistic simulations, we generate metastable structures for a series of [001] and [110] symmetric tilt GBs in a model Al-Mg system and quantify Mg segregation to these boundaries. Our results show large variations in the local atomic Voronoi volume due to GB metastability, which influences segregation energetics. The atomistic data are used to train a Gaussian Process machine learning model, which provides a probabilistic description of the segregation energy in terms of the local atomic environment. Our treatment extends existing models by accounting for variability introduced by GB metastability. |