STM Image Analysis using Autoencoders
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
This study explores the application of Convolutional Autoencoders (CAEs) for
analyzing and reconstructing Scanning Tunneling Microscopy (STM) images of
various crystalline lattice structures. We developed two distinct CAE
architectures to process simulated STM images of simple cubic, body-centered
cubic (BCC), face-centered cubic (FCC), and hexagonal lattices. Our models were
trained on $17\times17$ pixel patches extracted from $256\times256$ simulated
STM images, incorporating realistic noise characteristics. We evaluated the
models' performance using Mean Squared Error (MSE) and Structural Similarity
(SSIM) index, and analyzed the learned latent space representations. The
results demonstrate the potential of deep learning techniques in STM image
analysis, while also highlighting challenges in latent space interpretability
and full image reconstruction. This work lays the foundation for future
advancements in automated analysis of atomic-scale imaging data, with potential
applications in materials science and nanotechnology.
