Detailed Data Analysis for QM2 Dataset

Make sure your dataset is completely processed with QM2 in-house software or Nxrefine software.Once you have HKLI.nxs files, you can start start to perform in-depth data analysis.

In-depth data analysis (three different github links are below) Different github links are provided below for the in-depth QM2 data analysis, if you have any questions, please send an email to beamlist scientist

NXS-ANALYSIS-TOOL

Detailed Data Analysis of QM2 dataset - NXS-ANALYSIS-TOOL

Once you have received the HKLI data from the QM2 beamline, you can visualize it using Jupyter Notebook. Additionally, you can perform various data analyses with nxs-analysis-tool develped by Dr. Steven Gomez.

For 3D-PDF details, you can watch the youtube video by Dr. Steven Gomez.

The software enables you to:

i)   Load data into Jupyter Notebook.
ii)  Perform different slicing and visualization techniques.
iii) Create line cuts
iv)  Visualize temperature-dependent data.
v)   Extract order parameters and 
vi)   Symmetrize data
vii)  Extract 3D-delta PDF.

For access to the orGUI software, visit their GitHub repository: 
                https://zenodo.org/records/14271047.

If you use the software, please cite the software package:

Steven Gomez Alvarado, & Soren Bear. (2025). stevenjgomez/nxs_analysis_tools: v0.1.3 (v0.1.3). Zenodo. https://doi.org/10.5281/zenodo.15206517

orGUI

Thin-film data analysis - orGUI

A team led by Dr. Timo Fuch developed orGUI is a software that can be used to determine the orientation of single crystal samples in X-ray diffraction experiments. Recently it is implemented at QM2 for thin-film sample for CTR meausrement. 

For access to the orGUI software, visit their GitHub repository:
             https://zenodo.org/records/14271047.

If you use the software, please cite the software package:

Fuchs, T. (2024). orGUI: Orientation and Integration with 2D detectors (1.2.0). Zenodo. https://doi.org/10.5281/zenodo.14271047

X-TEC

Unsupervised Machine Learning with QM2 data - XTEC

A team led by Dr. Kim, including Cornell physicists and computer scientists in collaboration with Dr. Jacob Ruff, has developed an innovative and interpretable machine learning algorithm for the Quantum Materials beamline at CHESS. This algorithm, known as XRD Temperature Clustering (X-TEC), is designed to analyze and interpret big data from modern X-ray diffraction experiments.

You can find more details in their publication: Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction.

For access to the X-TEC software, visit their GitHub repository:
             https://github.com/KimGroup/XTEC.

Structural Refinement