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

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

SUBMITTER: Huang W 

PROVIDER: S-EPMC11937378 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature