{"id":15132687,"url":"https://github.com/smahala02/materials-science-introduction","last_synced_at":"2026-02-09T02:32:02.851Z","repository":{"id":255739781,"uuid":"853510532","full_name":"smahala02/Materials-Science-Introduction","owner":"smahala02","description":"Introduction to Materials Science concepts using Python for array manipulation and visualization with NumPy and Matplotlib.","archived":false,"fork":false,"pushed_at":"2024-09-07T14:07:07.000Z","size":271,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:44:53.616Z","etag":null,"topics":["data-visualization","materials-science","matplotlib","numpy","python","scientific-computing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/smahala02.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-06T19:56:54.000Z","updated_at":"2024-09-07T14:07:10.000Z","dependencies_parsed_at":"2024-09-06T23:44:35.587Z","dependency_job_id":"b98e9657-7f05-4bb7-8a74-726461448954","html_url":"https://github.com/smahala02/Materials-Science-Introduction","commit_stats":{"total_commits":6,"total_committers":1,"mean_commits":6.0,"dds":0.0,"last_synced_commit":"eebdf519bb83c4903ec531ae93a98677d8a27ea3"},"previous_names":["smahala02/materials-science-introduction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FMaterials-Science-Introduction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FMaterials-Science-Introduction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FMaterials-Science-Introduction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FMaterials-Science-Introduction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/smahala02","download_url":"https://codeload.github.com/smahala02/Materials-Science-Introduction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406068,"owners_count":20933802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-visualization","materials-science","matplotlib","numpy","python","scientific-computing"],"created_at":"2024-09-26T04:22:18.693Z","updated_at":"2026-02-09T02:32:02.821Z","avatar_url":"https://github.com/smahala02.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Materials Science - Introduction\n\nThis repository introduces **Materials Science** concepts through **Python**. Specifically, it demonstrates how structured arrays can be created and visualized to represent different materials or patterns, providing insights into their structural behavior. We use Python libraries such as **NumPy** and **Matplotlib** for array manipulation and visualization.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Checkerboard and Structured Arrays](#checkerboard-and-structured-arrays)\n3. [Example Arrays](#example-arrays)\n4. [Getting Started](#getting-started)\n5. [Installation](#installation)\n6. [Contributing](#contributing)\n7. [License](#license)\n\n---\n\n## Introduction\n\nThis repository serves as an introductory guide to how arrays and patterns can be used to represent and simulate material structures in **Materials Science**. Arrays form the foundation for understanding atomic or molecular structures and periodicity in materials.\n\nBy the end of this module, you will:\n- Understand how to generate structured arrays in Python.\n- Learn to visualize 2D and 3D arrays that mimic common material patterns.\n- Gain experience in using `NumPy` and `Matplotlib` for scientific computing and visual representation.\n\n## Checkerboard and Structured Arrays\n\nStructured arrays are commonly used in materials science to represent periodic patterns or arrangements, such as atoms in a crystal lattice or alternating phases in a composite material. Some common patterns include checkerboards, stripes, and centered arrays.\n\nIn this repository, you will explore the following array patterns:\n\n1. **Checkerboard pattern**: This is useful for representing alternating material components, like a simple binary alloy structure.\n2. **Striped pattern**: These arrays can represent layered materials, with alternating stripes corresponding to different layers.\n3. **Centered block pattern**: This represents a core-shell structure, often used for representing particles embedded in a matrix.\n\n## Example Arrays\n\nBelow are visual representations of several array patterns that are useful for modeling different material structures:\n\n![Array Visualizations](./3d_array_example.png)\n\n- **arrayD**: A checkerboard pattern, typically used to visualize alternating atoms in a binary structure.\n- **arrayE**: A striped pattern, representing a layered material.\n- **arrayF**: A centered pattern, often used for core-shell structures, where the red core is embedded in a blue matrix.\n\nThese visualizations provide an intuitive understanding of how materials can be represented as periodic or structured arrays in a computational environment.\n\n## Getting Started\n\n### Prerequisites\n\nTo run the array simulations and visualizations, you will need:\n\n- Python 3.x\n- `NumPy` for array manipulations\n- `Matplotlib` for visualizing arrays\n- Jupyter Notebook (optional, for running `.ipynb` files)\n\n### Installation\n\n1. Clone the repository to your local machine:\n\n   ```bash\n   git clone https://github.com/smahala02/Materials-Science-Introduction.git\n   ```\n\n2. Install the required Python packages:\n\n   ```bash\n   pip install numpy matplotlib\n   ```\n\n3. If you want to run the notebook file:\n\n   ```bash\n   jupyter notebook 'Introduction to Python for Materials Science.ipynb'\n   ```\n\n## Contributing\n\nContributions to this repository are welcome! If you have ideas for new array patterns or material simulations, feel free to open a pull request:\n\n1. Fork the repository.\n2. Create a new branch (`git checkout -b new-feature`).\n3. Commit your changes (`git commit -m 'Add new feature'`).\n4. Push to the branch (`git push origin new-feature`).\n5. Open a pull request.\n\n## License\n\nThis repository is licensed under the MIT License. See the `LICENSE` file for more information.\n\n## Author\n- [smahala02](https://github.com/smahala02)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmahala02%2Fmaterials-science-introduction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmahala02%2Fmaterials-science-introduction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmahala02%2Fmaterials-science-introduction/lists"}