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CONTRIBUTING.md

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aviary is an open-source machine learning library for materials discovery. It aims to be modular and easy to understand.

Contributing Guidelines:

  1. Code should be written in Python and adhere to PEP8 style guidelines. We provide pre-commit hooks and run pre-commit.ci to on PRs.
  2. All code contributions should be accompanied by unit tests.
  3. Use descriptive and meaningful variable and function names.
  4. Include doc strings for all functions and classes.
  5. Keep the codebase simple and easy to understand.
  6. Use Git and GitHub for version control and pull requests.
  7. All contributions must be released under the MIT license.
  8. Before submitting a pull request, make sure all tests pass and that your code is well-documented.
  9. Avoid using external libraries unless they are necessary for the functionality of the library.
  10. Follow the issues and pull requests to keep track of the development of the library and contribute where you can. Don't be afraid to ask for help!

Thank you for your interest in contributing to aviary! We look forward to your contributions.