Reconstructing the exit wave in high-resolution transmission electron microscopy using machine learning

TL;DR

This paper demonstrates that convolutional neural networks can effectively reconstruct the exit wave in high-resolution transmission electron microscopy, enabling atomic-scale imaging of 2D materials with high fidelity.

Contribution

The study introduces a U-Net based neural network approach for reconstructing exit waves from HRTEM images, trained on simulated data and successfully applied to experimental samples.

Findings

Neural network achieves reconstruction fidelity comparable to traditional methods.

Successfully applied to experimental images of MoS₂ and graphene.

Can be trained on a large database of 2D materials for broad applicability.

Abstract

Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS$_2$ nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.