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MDCraft\n\n\u003cimg src=\"https://raw.githubusercontent.com/bbye98/mdcraft/main/assets/logo.png\"\n align=\"right\" width=\"256\"/\u003e\n\n[![continuous-integration](\nhttps://github.com/bbye98/mdcraft/actions/workflows/ci.yml/badge.svg)](\nhttps://github.com/bbye98/mdcraft/actions/workflows/ci.yml)\n[![Documentation Status](https://readthedocs.org/projects/mdcraft/badge/?version=latest)](\nhttps://mdcraft.readthedocs.io/en/latest/?badge=latest)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13308642.svg)](https://doi.org/10.5281/zenodo.13308642)\n\nMDCraft is a toolkit of analysis modules and helper functions for\nmolecular dynamics (MD) simulations.\n\n* **Documentation**: https://mdcraft.readthedocs.io/\n* **Conda**: https://anaconda.org/bbye98/mdcraft\n* **Python Package Index (PyPI)**: https://pypi.org/project/mdcraft/\n\n## Features\n\n* [`algorithm`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/algorithm):\nEfficient Numba, NumPy, and SciPy algorithms for data wrangling and\nevaluating structural and dynamical properties.\n* [`analysis`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/analysis):\nSerial and parallel data analysis tools built on top of the MDAnalysis\nframework.\n* [`fit`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/fit):\nTwo-dimensional curve fitting models for use with SciPy.\n* [`lammps`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/lammps):\nHelper functions for setting up LAMMPS simulations.\n* [`openmm`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/openmm):\nExtensions to the high-performance OpenMM toolkit, such as custom\nbond/pair potentials, support for NetCDF trajectories, and much more.\n* [`plot`](https://github.com/bbye98/mdcraft/tree/main/src/mdcraft/plot):\nSettings and additional functionality for Matplotlib figures.\n\n### Benchmarks\n\nThe novel forcefield provided by MDCraft, Gaussian Core Model with smeared electrostatics (GCMe), provides multiple benefits discussed in more detail within our recent [publication](https://doi.org/10.1021/acs.jctc.4c00603). Of note is the computational speed-up obtained from GCMe, especially when used in OpenMM:\n\n![benchmarks](/assets/benchmarks.png)\n\nThe codes used to generate these benchmarks are provided in the associated [repository](https://github.com/bbye98/gcme).\n## Getting started\n\n### Prerequisites\n\nIf you use pip to manage your Python packages and plan on using the\nOpenMM simulation toolkit, you must compile and install OpenMM manually\nsince OpenMM is not available in PyPI. See the\n[\"Compiling OpenMM from Source Code\"](\nhttp://docs.openmm.org/latest/userguide/library/02_compiling.html)\nsection of the OpenMM User Guide for more information.\n\nIf you use Conda, it is recommended that you use the conda-forge\nchannel to install dependencies. To make conda-forge the default\nchannel, use\n\n    conda config --add channels conda-forge\n\n### Installation\n\nMDCraft requires Python 3.9 or later.\n\nFor the most up-to-date version of MDCraft, clone the repository and\ninstall the package using pip:\n\n    git clone https://github.com/bbye98/mdcraft.git\n    cd mdcraft\n    python3 -m pip install -e .\n\nAlternatively, MDCraft is available on Conda:\n\n    conda install bbye98::mdcraft\n\nand PyPI:\n\n    python3 -m pip install mdcraft\n\n### Postrequisites\n\nTo use the method of image charges\n(`mdcraft.openmm.system.add_image_charges()`) in your OpenMM\nsimulations, you must compile and install [`constvplugin`](\nhttps://github.com/scychon/openmm_constV) or [`openmm-ic-plugin`](\nhttps://github.com/bbye98/mdcraft/tree/main/lib/openmm-ic-plugin).\n\n### Tests\n\nAfter installing, to run the MDCraft tests locally, use `pytest`:\n\n    pip install pytest\n    cd mdcraft\n    pytest\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbbye98%2Fmdcraft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbbye98%2Fmdcraft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbbye98%2Fmdcraft/lists"}