Corpus ID: 231839769

Machine Learning on Neutron and X-Ray Scattering

@inproceedings{Chen2021MachineLO,
  title={Machine Learning on Neutron and X-Ray Scattering},
  author={Zhantao Chen and Nina Andrejevic and Nathan C. Drucker and Th{\`a}nh Nguyen and R. Xian and T. Smidt and Yao Wang and R. Ernstorfer and A. Tennant and Maria Chan and Mingda Li},
  year={2021}
}
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials’ structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased… Expand

References

SHOWING 1-10 OF 243 REFERENCES
Learning to Predict Material Structure from Neutron Scattering Data
  • 4
Automated X-ray diffraction of irradiated materials
  • 1
Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy.
  • 4
  • PDF
Deep Learning Methods On Neutron Scattering Data
  • 1
  • PDF
X-Ray Scattering Image Classification Using Deep Learning
  • 19
  • PDF
Advances in neutron imaging
  • 46
  • PDF
A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra.
  • 5
...
1
2
3
4
5
...