mat-xrd-digitizer

Digitizing XRD plot images into numeric .xy data requires uploading the plot image so the agent can extract visible peak positions and intensities into a JSON list, then running the digitize_plot.py script to generate the final dataset.

The best way to extract XRD peaks from literature figures is using visual peak extraction via Vision/Language models to identify 2-theta positions and relative intensities, which are then reconstructed into a continuous synthetic profile using pseudo-Voigt peak shapes.

Yes, digitized XRD data can be used for phase identification. The Skill produces a downstream-ready .xy file of 2-theta versus intensity suitable for tools such as mat-xrd-phase-analysis, enabling automated phase matching from literature plots.

Yes, the XRD digitizer supports background and noise configuration. The digitize_plot.py generator accepts user-defined 2-theta bounds alongside optional background and noise parameters to accurately reconstruct the synthetic XRD profile.

Yes, numpy is required to run the XRD plot digitizer. It is the sole listed dependency needed to process visual peak extraction data and generate the pseudo-Voigt reconstructed .xy dataset.

Visually extracting XRD peaks from screenshots yields approximate peak data for downstream fitting rather than exact raw counts. The reconstructed pseudo-Voigt profile depends on visually identified 2-theta positions, meaning minor or overlapping peaks may lack precise accuracy.