Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
Year: 2026
Type: preprint
Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them. We address this challenge across two settings, offline dataset exploration and live on-the-fly analysis. We train a domain-specific attention-based Convolutional Variational Autoencoder (C-VAE) on 1.5 million X-ray scattering images to learn low-dimensional representations capturing structural variation across diverse experimental conditions. The learned latent space reveals well-organized clusters and smooth trajectories reflecting experimental progression. It further supports controlled synthetic scattering image generation across diverse structural states. When deployed without retraining, the model organizes time-resolved film formation experiments at two synchrotron facilities into interpretable latent structures. Benchmarking against DINOv3 (ViT-7B), a general-purpose vision foundation model, demonstrates that domain-specific training yields more interpretable latent organization for scattering data. Both workflows are integrated within Latent Space Explorer, a component of the MLExchange platform, supporting interactive structural exploration across archived datasets and live experiments.
Source: arXiv (Cornell University)
Authors: Choudhary, Monika, Chong, Xiaoya, Jiang, Runbo, Koepp, Wiebke, Zwart, Petrus H., English, Damon, Su, Gregory M., Schaible, Eric, Zhu, Chenhui, Nassr, Mostafa, Wamble, Noah P., Li, Kelvin Kam-Yun, Chan, Jonathan M., Diaz, Jose Carlos, McKay, Cameron, Katz, Lynn, Freeman, Benny, Freychet, Guillaume, Matviychuk, Yevgen, Gann, Eliot, Allan, Daniel B., Sochor, Benedikt, Schluenzen, Frank, Roth, Stephan V., Crumlin, Ethan J., McReynolds, Dylan, Chavez, Tanny, Hexemer, Alexander
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7164918276