English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Serial crystallography is the field of science that studies the structure andproperties of crystals via diffraction patterns. In this paper, we introduce anew serial crystallography dataset comprised of real and synthetic images; thesynthetic images are generated through the use of a simulator that is bothscalable and accurate. The resulting dataset is called DiffraNet, and it iscomposed of 25,457 512x512 grayscale labeled images. We explore severalcomputer vision approaches for classification on DiffraNet such as standardfeature extraction algorithms associated with Random Forests and Support VectorMachines but also an end-to-end CNN topology dubbed DeepFreak tailored to workon this new dataset. All implementations are publicly available and have beenfine-tuned using off-the-shelf AutoML optimization tools for a fair comparison.Our best model achieves 98.5% accuracy on synthetic images and 94.51% accuracyon real images. We believe that the DiffraNet dataset and its classificationmethods will have in the long term a positive impact in acceleratingdiscoveries in many disciplines, including chemistry, geology, biology,materials science, metallurgy, and physics.

DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning