Deep analytics of atomically-resolved images: manifest and latent features

TL;DR

This paper introduces a deep learning framework that analyzes atomically-resolved images to identify structural and electronic features, enabling rapid defect detection and material characterization across various microscopy techniques.

Contribution

The authors develop a universal deep-learning method for analyzing atomic-scale images, capable of detecting anomalies, tracking distortions, and unifying data analysis across different microscopy modalities.

Findings

Successfully detects atomic defects and anomalies in diverse materials.

Tracks minute lattice distortions over time in 3D samples.

Provides a fast, unified approach for analyzing high-resolution microscopy data.

Abstract

Recent advances in scanning transmission electron and scanning tunneling microscopies allow researchers to measure materials structural and electronic properties, such as atomic displacements and charge density modulations, at an Angstrom scale in real space. At the same time, the ability to quickly acquire large, high-resolution datasets has created a challenge for rapid physics-based analysis of images that typically contain several hundreds to several thousand atomic units. Here we demonstrate a universal deep-learning based framework for locating and characterizing atomic species in the lattice, which can be applied to different types of atomically resolved measurements on different materials. Specifically, by inspecting and categorizing features in the output layer of a convolutional neural network, we are able to detect structural and electronic 'anomalies' associated with the presence of point defects in a tungsten disulfide monolayer, non-uniformity of the charge density distribution around specific lattice sites on the surface of strongly correlated oxides, and transition between different structural states of buckybowl molecules. We further extended our method towards tracking, from one image frame to another, minute distortions in the geometric shape of individual Si dumbbells in a 3-dimensional Si sample, which are associated with a motion of lattice defects and impurities. Due the applicability of our framework to both scanning tunneling microscopy and scanning transmission electron microscopy measurements, it can provide a fast and straightforward way towards creating a unified database of defect-property relationships from experimental data for each material.