Speaker
Description
The study of impurity effects on grain boundaries is a critical aspect of materials science, particularly in 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 contribution, we employed the on-the-fly machine learning to generate force fields, which were subsequently used for the calculation of 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 not only affects the segregation energy, but it can change the preferred segregation sites.
