The data and source codes for the paper [J. Lee, C. Jeong and Y. Yang, "Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography", Nat. Commun. 12, 1962 (2021)] are posted below.
Functional properties of nanomaterials strongly depend on their surface atomic structures, but they often become largely different from their bulk structures, exhibiting surface reconstructions and relaxations. However, most of the surface characterization methods are either limited to 2-dimensional measurements or not reaching to true 3D atomic-scale resolution, and single-atom level determination of the 3D surface atomic structure for general 3D nanomaterials still remains elusive. Here we demonstrate the measurement of 3D atomic structure at 15 pm precision using a Pt nanoparticle as a model system. Aided by a deep learning-based missing data retrieval combined with atomic electron tomography, the surface atomic structure was reliably measured. We found that <100> and <111> facets contribute differently to the surface strain, resulting in anisotropic strain distribution as well as compressive support boundary effect. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance.
1. Denoised and aligned tilt series images and finalized tilt angles.
2. 3D tomographic reconstruction and 3D tomographic reconstruction with real interpolation applied (DL augmentation input data) of a Pt nanoparticle before DL augmentation.
3. 3D tomographic reconstruction of a Pt nanoparticle after DL augmentation.
4. Source codes for the DL augmentation, which includes generation of simulated tomograms as well as the neural network architecture.
5. final atomic structure of a Pt nanoparticle after DL augmentation.
- DL augmentation trained by other atomic model and other Bfactor -
6. 3D tomographic reconstruction and final atomic structure of a Pt nanoparticle after DL augmentation trained by amorphous atomic models with Bfactor 5.
7. 3D tomographic reconstruction and final atomic structure of a Pt nanoparticle after DL augmentation trained by f.c.c.-based atomic models with Bfactor 3.2.