Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography

Abstract Functional properties of nanomaterials strongly depend on their surface atomic structures, but they often become largely different from bulk exhibiting reconstructions and relaxations. However, most the characterization methods are either limited to 2D measurements or not reaching true 3D atomic-scale resolution, single-atom level determination structure for general still remains elusive. Here we demonstrate measurement at 15 pm precision using a Pt nanoparticle as model system. Aided by deep learning-based missing data retrieval combined with electron tomography, was reliably measured. We found that < $$100$$ 100 > $$111$$ 111 facets contribute differently strain, resulting in anisotropic strain distribution well compressive support boundary effect. The capability will only deepen our understanding functional also open new door fine tailoring performance.