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The complexity of the calculations usually requires\nintercommunication between the aforementioned tools, such communication is usually done\nthrough shell scripts that try to automate input/output actions like: launching\nthe computations in a cluster, reading the resulting output and feeding the relevant\nnumerical result to another program. Such scripts are difficult to maintain and extend,\nrequiring a significant programming expertise to work with them. Being then desirable a\nset of automatic and extensible tools that allows to perform complex simulations in\nheterogeneous hardware platforms.\n\nThis library tackles the construction and efficient execution of computational chemistry workflows.\nThis allows computational chemists to use the emerging massively parallel compute environments in\nan easy manner and focus on interpretation of scientific data rather than on tedious job submission\nprocedures and manual data processing.\n\nDescription\n===========\nThis library consists of a set of modules written in Python3 to\nautomate the following tasks:\n\n 1. Input generation.\n 2. Handle tasks dependencies (Noodles_).\n 3. Advanced molecular manipulation capabilities with (rdkit_).\n 4. Jobs failure detection and recovery.\n 5. Numerical data storage (h5py_).\n\nTutorial and Examples\n---------------------\nA tutorial written as a jupyter-notebook_ is available from: tutorial-qmflows_. You can\nalso access direclty more advanced examples_.\n\nInstallation\n============\n\n- Download miniconda for python3: miniconda_ (also you can install the complete anaconda_ version).\n\n- Install according to: installConda_.\n\n- Create a new virtual environment using the following commands:\n\n  - ``conda create -n qmflows``\n\n- Activate the new virtual environment\n\n  - ``source activate qmflows``\n\nTo exit the virtual environment type  ``source deactivate``.\n\n\n.. _dependecies:\n\nDependencies installation\n-------------------------\n\n- Type in your terminal:\n\n  ``conda activate qmflows``\n\nUsing the conda environment the following packages should be installed:\n\n\n- install rdkit_ and h5py_ using conda:\n\n  - ``conda install -y -q -c conda-forge rdkit h5py``\n\n  - Note that ``rdkit`` is optional for Python 3.7 and later.\n\n.. _installation:\n\nPackage installation\n--------------------\nFinally install the package:\n\n- Install **QMFlows** using pip:\n  - ``pip install qmflows``\n\nNow you are ready to use *qmflows*.\n\n\n  **Notes:**\n\n  - Once the libraries and the virtual environment are installed, you only need to type\n    ``conda activate qmflows`` each time that you want to use the software.\n\n\n.. _documentation: https://qmflows.readthedocs.io/en/latest/\n.. _miniconda: https://docs.conda.io/en/latest/miniconda.html\n.. _anaconda: https://www.anaconda.com/distribution/#download-section\n.. _installConda: https://conda.io/projects/conda/en/latest/user-guide/install/index.html\n.. _Noodles: http://nlesc.github.io/noodles/\n.. _h5py: http://www.h5py.org/\n.. _here: https://www.python.org/downloads/\n.. _rdkit: http://www.rdkit.org\n.. _jupyter-notebook: http://jupyter.org/\n.. _tutorial-qmflows: https://github.com/SCM-NV/qmflows/tree/master/jupyterNotebooks\n.. _examples: https://github.com/SCM-NV/qmflows/tree/master/src/qmflows/examples\n.. _PLAMS: https://github.com/SCM-NV/PLAMS\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscm-nv%2Fqmflows","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscm-nv%2Fqmflows","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscm-nv%2Fqmflows/lists"}