Machine-learning interatomic potential for BaTiO₃: phase transitions, domain walls, and grain boundaries

A machine learning interatomic potential for BaTiO₃is presented based on the atomic cluster expansion formalism, enabling atomistic simulations of phase transitions, defect structures, and domain walls. Trained on a comprehensive dataset of density-functional theory calculations, the potential effectively captures the sequence of temperature-driven phase transitions from rhombohedral to orthorhombic, tetragonal, and cubic phases. In addition, the effect of pressure on these phase transitions is well described showing a decrease in the transition temperatures with increasing pressure as observed experimentally. The transferability of the potential is exemplified by accurately predicting 180^∘domain-wall structures and the energetics of symmetric tilt grain boundaries in the rhombohedral phase.