{"id":18638384,"url":"https://github.com/nlesc-nano/swan","last_synced_at":"2025-04-11T10:31:27.312Z","repository":{"id":35008144,"uuid":"191957101","full_name":"nlesc-nano/swan","owner":"nlesc-nano","description":"Statistical models to predict new materials","archived":false,"fork":false,"pushed_at":"2023-05-09T13:36:29.000Z","size":158941,"stargazers_count":14,"open_issues_count":7,"forks_count":1,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-03-25T12:22:04.277Z","etag":null,"topics":["machine-learning","material-science","python","quantum-chemistry"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nlesc-nano.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.rst","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-06-14T14:29:57.000Z","updated_at":"2023-06-30T20:45:53.000Z","dependencies_parsed_at":"2024-11-07T05:51:20.286Z","dependency_job_id":null,"html_url":"https://github.com/nlesc-nano/swan","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nlesc-nano%2Fswan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nlesc-nano%2Fswan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nlesc-nano%2Fswan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nlesc-nano%2Fswan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nlesc-nano","download_url":"https://codeload.github.com/nlesc-nano/swan/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248374722,"owners_count":21093372,"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":["machine-learning","material-science","python","quantum-chemistry"],"created_at":"2024-11-07T05:41:11.975Z","updated_at":"2025-04-11T10:31:22.301Z","avatar_url":"https://github.com/nlesc-nano.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n.. image:: https://github.com/nlesc-nano/swan/workflows/build%20with%20conda/badge.svg\n   :target: https://github.com/nlesc-nano/swan/actions\n.. image:: https://codecov.io/gh/nlesc-nano/swan/branch/main/graph/badge.svg?token=1527ficjjx\n   :target: https://codecov.io/gh/nlesc-nano/swan\n.. image:: https://zenodo.org/badge/191957101.svg\n   :target: https://zenodo.org/badge/latestdoi/191957101\n.. image:: https://readthedocs.org/projects/swan/badge/?version=latest\n   :target: https://swan.readthedocs.io/en/latest/?badge=latest\n\t    \n#####################################\nScreening Workflows And Nanomaterials\n#####################################\n\n🦢 **Swan** is a Python pacakge to create statistical models using machine learning to predict molecular properties. See Documentation_.\n\n\n🛠 Installation\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 swan``\n\n- Activate the new virtual environment\n\n  - ``conda activate swan``\n\nTo exit the virtual environment type  ``conda deactivate``.\n\n\n.. _dependecies:\n\nDependencies installation\n-------------------------\n\n- Type in your terminal:\n\n  ``conda activate swan``\n\nUsing the conda environment the following packages should be installed:\n\n\n- install RDKit_ and H5PY_:\n\n  - `conda install -y -q -c conda-forge h5py rdkit`\n\n- install Pytorch_ according to this_ recipe\n\n- install `Pytorch_Geometric dependencies \u003chttps://github.com/rusty1s/pytorch_geometric#installation\u003e`_.\n\n- install `DGL using conda \u003chttps://www.dgl.ai/pages/start.html\u003e`_\n\n\n.. _installation:\n\nPackage installation\n--------------------\nFinally install the package:\n\n- Install **swan** using pip:\n  - ``pip install git+https://github.com/nlesc-nano/swan.git``\n\nNow you are ready to use *swan*.\n\n\n  **Notes:**\n\n  - Once the libraries and the virtual environment are installed, you only need to type\n    ``conda activate swan`` each time that you want to use the software.\n\n.. _Documentation: https://swan.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.. _Pytorch: https://pytorch.org\n.. _RDKit: https://www.rdkit.org\n.. _H5PY: https://www.h5py.org/\n.. _this: https://pytorch.org/get-started/locally/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnlesc-nano%2Fswan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnlesc-nano%2Fswan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnlesc-nano%2Fswan/lists"}