Abstract: In epitaxial thin film systems, the crystal structure and its symmetry deviate from the bulk counterpart due to various mechanisms such as epitaxial strain and interfacial structural coupling, which induce an accompanying change in their properties. In perovskite materials, the crystal symmetry can be described by rotations of 6-fold coordinated transition metal oxygen octahedra, which are found to be altered at interfaces. Here, we unravel how the local oxygen octahedral coupling (OOC) at perovskite heterostructural interfaces initiates a different symmetry in epitaxial films and provide design rules to induce various symmetries in thin films by careful selecting appropriate combinations of substrate/buffer/film. Very interestingly we discovered that these combinations lead to symmetry changes throughout the full thickness of the film. Our results provide a deep insight into understanding the origin of induced crystal symmetry in a perovskite heterostructure and an intelligent route to achieve unique functional properties.

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.