DeepAI AI Chat
Log In Sign Up

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders

by   Maxim Ziatdinov, et al.

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we develop shift-invariant variational autoencoders (shift-VAE) that allow disentangling characteristic repeating features in the images, their variations, and shifts inevitable for random sampling of image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data.


page 9

page 11

page 12

page 13

page 15

page 17


Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders

Electron, optical, and scanning probe microscopy methods are generating ...

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

A shift-invariant variational autoencoder (shift-VAE) is developed as an...

Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis

Electron and scanning probe microscopy produce vast amounts of data in t...

Atomic structure generation from reconstructing structural fingerprints

Data-driven machine learning methods have the potential to dramatically ...

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

The design/discovery of new materials is highly non-trivial owing to the...