Thermal and Size Effects in Ferroelastic Domains by Machine Learning
Ferroelastic domain walls (DWs) underpin key functionalities in complex oxides. In free-standing ferroic thin films, where elastic interactions are highly thickness dependent, understanding DW behaviour across length scales and external stimuli is crucial. A thickness-dependent monopolar-to-dipolar crossover in elastic DW behaviour has been reported; however, how temperature influences this regime remains unexplored. Here, LaAlO3 thin films spanning the dipolar ($<200$ nm) and crossover (200-300 nm) regimes are investigated using in situ heating scanning transmission electron microscopy (STEM) and a machine-learning-driven image analysis approach. By tracking DW curvature and density from above $T_C$ (approximately $550,^\circ$C) to room temperature (RT), a distinct interplay between temperature and thickness is identified. In the dipolar regime, DWs are mobile and curved near $T_C$ and gradually freeze upon cooling, consistent with the well-known temperature freezing regime. In contrast, within the crossover regime, DWs are nearly static, with minimal reconfiguration through cooling and curvature an order of magnitude lower at RT. These results map the evolution of DWs across the thermally driven super-elastic to freezing regimes, revealing how thickness and temperature govern DW morphology and dynamics, and providing insight relevant for domain engineering in free-standing oxide thin films.