https://github.com/mrsamsonn/monolithic-polylithic-crystal-segmentation

A grid segmentation algorithm for clustering crystal structures using diffraction patterns. Useful in material science and nanotechnology, this code enables detailed analysis of crystals for research and industrial applications.
https://github.com/mrsamsonn/monolithic-polylithic-crystal-segmentation

clustering crystal-structure crystallography data-analysis diffraction-patterns grid-segmentation image-processing k-means machine-learning matertial-science nanotechnology python research-project research-tools scientific-computing

Last synced: 2 days ago
JSON representation

A grid segmentation algorithm for clustering crystal structures using diffraction patterns. Useful in material science and nanotechnology, this code enables detailed analysis of crystals for research and industrial applications.

• Host: GitHub
• URL: https://github.com/mrsamsonn/monolithic-polylithic-crystal-segmentation
• Owner: mrsamsonn
• Created: 2024-08-11T15:22:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-29T04:28:15.000Z (4 months ago)
• Last Synced: 2024-12-10T19:47:14.514Z (12 days ago)
• Topics: clustering, crystal-structure, crystallography, data-analysis, diffraction-patterns, grid-segmentation, image-processing, k-means, machine-learning, matertial-science, nanotechnology, python, research-project, research-tools, scientific-computing
• Language: Jupyter Notebook
• Homepage:
• Size: 14.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Monolithic-Polylithic-Crystal-Segmentation

| Documentation Report | Currently Unpublished |

| ------------- | ------------- |

## Example Output





# Grid Segmentation Overview 🧩

## 1. Introduction

	•	Purpose:

	•	Partition real space into grids to analyze and cluster crystal structures using diffraction patterns.

	•	Applications:

	•	Used for segmenting monolithic and polylithic crystals.

	•	Supports k-means clustering, similarity calculations, and visualization.

## 2. Setup

	•	Grid Partitioning:

	•	Divide real space into square grids.

	•	Map each grid to a region in diffraction space.

	•	Data Structures:

	•	Grids: Represents the grid layout.

	•	Features Array: Stores features like peak intensities.

	•	Similarity Matrix: Holds similarity scores for clustering.

## 3. Diffraction Pattern Analysis

	•	Collect Data:

	•	Obtain diffraction patterns for each grid.

	•	Peak Detection:

	•	Locate peaks in a defined region around expected locations.

	•	Calculate peak intensity, adjust for edge effects.

## 4. Similarity Calculation

	•	Feature Extraction:

	•	Extract key features from diffraction data.

	•	Measure Similarity:

	•	Use distance metrics to calculate similarity between grids.

	•	Normalize values for consistency in clustering.

## 5. Clustering

	•	K-Means Clustering:

	•	Apply k-means to group similar grids.

	•	Determine the optimal number of clusters using elbow method.

	•	Edge Cases:

	•	Handle grids near edges or isolated outliers.

## 6. Visualization

	•	Color Mapping:

	•	Use color codes  to distinguish clusters.

	•	Plotting:

	•	2D Plotting: Represent each grid as a colored rectangle.

	•	3D Plotting: Optionally visualize in 3D, excluding grids with zero similarity.

	•	Legend Creation:

	•	Generate a legend to label clusters for clear interpretation.

## 7. Practical Considerations

	•	Efficiency:

	•	Optimize for large datasets, possibly using parallel processing.

	•	Parameter Tuning:

	•	Experiment with grid sizes and clustering parameters to refine results.

	•	Extensions:

	•	Dynamic Grid Sizes: Implement adaptive grid sizing.

	•	Integration: Combine with other segmentation methods for better accuracy.

## 8. Conclusion

	•	Summary:

	•	Grid segmentation is key for detailed analysis and clustering of crystal structures.

	•	Future Work:

	•	Focus on refining techniques for better performance and new applications.