10.18126/M2Z30Z
Yager, Kevin G.
Kevin G.
Yager
Lhermitte, Julien
Julien
Lhermitte
Yu, Dantong
Dantong
Yu
Wang, Boyu
Boyu
Wang
Guan, Ziqiao
Ziqiao
Guan
Liu, Jiliang
Jiliang
Liu
Dataset of Synthetic X-ray Scattering Images for Classification Using Deep Learning
Materials Data Facility
2017
Dataset
x-ray
xray
small-angle x-ray scattering
wide-angle x-ray scattering
saxs
waxs
convolutional neural network
CNN
convolutional autoencoders
Kevin Yager (kyager@bnl.gov)
2017-03-22T02:52:07Z
2017-03-22T02:52:07Z
2017-03-21
10.1109/WACV.2014.6836004
10.1109/NYSDS.2016.7747813
https://arxiv.org/abs/1611.03313
This dataset contains a large number of example x-ray scattering images; each image is tagged with a variety of attributes describing the data features appearing in the image ('rings', 'anisotropic', etc.) or describing the underlying material ('BCC', 'FCC', etc.). The main purpose of this dataset is as a training set for machine-learning methods. The images were generated synthetically, using a combination of ad hoc methods (e.g. superimposing features such as rings and halos) and simple simulations (e.g. generating realspace arrangements of nanoparticles, and then computing the far-field scattering pattern). The presented code iterates across a wide variety of input conditions, such that the output images cover a wide range of expected x-ray scattering image types. Experimentally-realistic artifacts, including masks, parasitic streaks, and Poisson noise, are also included.
Brookhaven National Laboratory