Identify
MOF Structures
from XRD Data

Upload XRD data files (CSV, Excel, TXT, RAW, XRDML) or XRD pattern images. Our engine compares peaks against a curated MOF database and returns match confidence with structural details — instantly.

Start Analysis → See how it works

14,072MOF References

98.6%Classifier Accuracy

5File Formats

Upload & Analyse

Upload your XRD data file or pattern image. We'll identify matching MOF structures.

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Drop your XRD data file here or click to browse

.csv .xlsx .xls .txt .xy .raw .xrdml .dat

🖼️

Drop your XRD pattern image here or click to browse

.png .jpg .jpeg .tiff .bmp .webp

Don't have a file?

Analysis Results

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Analysing...

0% Match

XRD Pattern — Detected Peaks

Your Pattern MOF Reference Detected Peaks

Detected Peaks

0 peaks found

#	2θ (°)	d-spacing (Å)	Intensity	Rel. Int. (%)	MOF Match	Status

Top Database Matches

📐 Estimated Structural Parameters

How It Works

A four-step pipeline from raw XRD data to MOF identification

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Upload Data

Upload XRD data files (CSV, Excel, TXT, RAW, XRDML) or a screenshot/scan of your diffraction pattern.

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Peak Detection

Automatic peak finding using a derivative-based algorithm with noise filtering. Peaks are converted to d-spacing via Bragg's Law.

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Database Matching

Peaks are matched against 14,072 CSD MOF reference structures stored in Google Sheets. Cell parameters and space groups are compared.

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Confidence Score

A weighted similarity score is calculated. Results show match %, top candidates, detected peaks, and estimated unit cell parameters.

Google Sheets DB Apps Script API Bragg's Law Peak Detection Algorithm Image Digitization Vercel Hosting

About the Database

The reference database contains 14,072 true MOF structures from the Cambridge Structural Database (CSD) MOF subset, augmented with 20,276 charge-reconstructed variants.

Each structure includes unit cell parameters (a, b, c, α, β, γ), space group, metal nodes, formula, and DDEC6 partial charges where available. The database is hosted in Google Sheets and queried via Google Apps Script.

This tool was developed alongside a publication on identifying fake MOFs using DDEC6 charge anomaly detection — achieving 98.6% classification accuracy with Random Forest.

View on GitHub Try the Tool

14,072 True MOF structures

20,276 Modified variants

868 Non-MOF references

98.6% Classifier accuracy

Identify MOF Structures from XRD Data