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

TL;DR

This paper presents a neural network-assisted method to determine the 3D surface atomic structure of nanomaterials at single-atom resolution, revealing detailed surface strain distributions and enabling advanced material design.

Contribution

It introduces a deep learning approach for 3D atomic surface reconstruction with 15 pm precision, surpassing previous limitations in surface characterization.

Findings

Achieved 15 pm precision in 3D atomic surface measurement.

Discovered anisotropic strain distribution on nanoparticle surfaces.

Revealed different contributions of facets to surface strain.

Abstract

Functional properties of nanomaterials strongly depend on their surface atomic structure, but they often become largely different from their bulk structure, 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 show the measurement of 3D atomic structure of a Pt nanoparticle at 15 pm precision, aided by a deep learning-based missing data retrieval. The surface atomic structure was reliably measured, and we find 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.