Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron Via Machine-Learned Force Fields

The study of the effects of impurity on grain boundaries is a critical aspect of materials science, particularly when it comes to understanding and controlling the properties of materials for specific applications. One of the related key issues is the segregation preference of impurity atoms in the grain boundary region. In this paper, we employed the on-the-fly machine learning to generate force fields, which were subsequently used to calculate the segregation energies of phosphorus and silicon in bcc iron containing the ∑5(310)[001] grain boundary. The generated force fields were successfully benchmarked using ab initio data. Our further calculations considered impurity atoms at a number of possible interstitial and substitutional segregation sites. Our predictions of the preferred sites agree with the experimental observations. Planar concentration of impurity atoms affects the segregation energy and, moreover, can change the preferred segregation sites.