mat-xrd-digitizer

Digitize XRD plot images into numeric .xy data via pseudo-Voigt reconstruction.

144|21|Updated Jan 8, 2026

Search Tags:#data synthesis #phase analysis #xrd #digitization #materials characterization #peak extraction #pseudo-voigt

Ready to run

npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mat-xrd-digitizer
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-xrd-digitizer
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-xrd-digitizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Converts an image or screenshot of an X-Ray Diffraction (XRD) plot into a digitized numeric .xy dataset that downstream phase-matching and refinement tools can use.

Core Features & Use Cases

Visual peak extraction: Uses the agent’s Vision/Language Model to identify major (and minor) peak positions (2θ) and approximate relative intensities from a provided plot image.
Pseudo-Voigt reconstruction: Translates extracted peaks into a continuous synthetic XRD profile using pseudo-Voigt peak shapes plus configurable background and noise.
Downstream-ready output: Produces an .xy file (2θ vs intensity/counts) suitable for tools such as mat-xrd-phase-analysis, enabling workflow automation from literature plots to computable data.

Quick Start

Upload the XRD plot image, instruct the agent to extract all visible peaks into a peaks.json file, then run the digitize_plot.py script to generate digitized_plot.xy for use in mat-xrd-phase-analysis.

Frequently Asked Questions about mat-xrd-digitizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert an XRD image into numeric .xy data?▼

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.

What is the best way to extract XRD peak positions from a literature figure?▼

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.

Can I use digitized XRD data for phase identification?▼

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.

Does the XRD digitizer support background and noise configuration?▼

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.

Do I need numpy to run the XRD plot digitizer?▼

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.

What are the limitations of visually extracting XRD peaks from screenshots?▼

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.