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Fundamental scientific algorithms in Julia

SciAlgs

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SciAlgs is a compilation of fundamental scientific algorithms. This collection does not intended to be exhaustive but to offer clear and concise implementations.

Why Julia?

Purpose

Some of the aims would be:

  • Concise and pedagogical implementations
  • Make algorithms accessible to researchers without expertise in computer programming.
  • Rewrite legacy programs more clearly (e.g. no spaghetti code)
  • Provide useful functions to perform basic operations
  • Integrate modern development practices: tests, continuous integration, collaborative development, ...
  • Enrich Julia's package ecosystem with new functionalities
  • Reproduce figures/data of published papers (mainly educative ones).

Some of the topics will be covered are:

  • Crystallography
  • Quantum Chemistry
  • Condensed Matter Physics
  • Quantum Mechanics models
  • Numerical methods

NOTE: Tutorials, tutorial_<name>.jl, can be translated with jupytext --sync tutorial_<name>.jl to Jupyter notebooks tutorial_<name>.ipynb, and be executed in the browser.

Algorithms