Auto-resolving the atomic structure at van der Waals interfaces using a generative model

TL;DR

This paper introduces a generative model-based method for automatic, high-precision atomic structure resolution at van der Waals interfaces, leveraging simulated and experimental data to analyze complex stacking patterns.

Contribution

It presents a novel disentangled representation learning approach combined with a residual neural network for accurate atomic structure inference from microscopy images.

Findings

Achieves picometer-scale accuracy in deducing interlayer slip and rotation.

Robustly handles defects, noise, and surface contamination.

Identifies pattern transitions and quantifies subtle motif variations.

Abstract

The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. However, a rapid, accurate, and robust approach to automatically resolve complex patterns in atomic-resolution microscopy remains difficult to implement. Here, we present a Trident strategy-enhanced disentangled representation learning method (a generative model), which utilizes a few unlabelled experimental images with abundant low-cost simulated images to generate a large corpus of annotated simulation data that closely resembles experimental results, producing a high-quality large-volume training dataset. A structural inference model is then trained via a residual neural network which can directly deduce the interlayer slip and rotation of diversified and complicated stacking patterns at van der Waals interfaces with picometer-scale accuracy across various materials (e.g. MoS2, WS2, ReS2, ReSe2, and 1T'-MoTe2) with different layer numbers , demonstrating robustness to defects, imaging quality, and surface contaminations. The framework can also identify pattern transition interfaces, quantify subtle motif variations, and discriminate moire patterns that are difficult to distinguish in frequency domains. Finally, the high-throughput processing ability of our method provides insights into a van der Waals epitaxy mode where various thermodynamically favorable slip stackings can coexist.