Data from: Ab initio grand canonical Monte Carlo calculation of grain boundary composition and structure

The prediction of grain boundary structure has gained great attention in
materials science because grain boundaries have a significant effect on
material properties. However, the prediction of grain boundaries by
multi-elemental systems has been difficult so far because the number of
atoms and compositions are determined by a delicate energy balance between
elements, which requires high calculation accuracy. Here, we have
developed the ab initio grand canonical Monte Carlo (ai-GCMC) theory to
grain boundaries, combining density functional theory and grand canonical
Monte Carlo to overcome this problem, and apply this methodology to
predict Ti segregation patterns in Ti-doped Σ13[1210](1014) α-Al2O3 grain
boundaries. That is, we generated tons of Ti-segregated Al2O3 grain
boundaries with DFT accuracy by GCMC and compare their free energy to
determine the thermodynamically stable structure. Our prediction
successfully corresponds with the experimentally observed structure, while
providing precise chemical compositions. This dataset includes those Al2O3
grain boundaries generated by ab initio GCMC.