{"id":15175855,"url":"https://github.com/ecrl/graphchem","last_synced_at":"2025-04-13T08:22:36.232Z","repository":{"id":49536170,"uuid":"165424464","full_name":"ecrl/graphchem","owner":"ecrl","description":"Graph-based machine learning for chemical property prediction","archived":false,"fork":false,"pushed_at":"2025-02-13T19:43:34.000Z","size":2827,"stargazers_count":32,"open_issues_count":0,"forks_count":7,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-24T09:19:30.990Z","etag":null,"topics":["computational-chemistry","graph-neural-networks","message-passing","pytorch","pytorch-geometric","rdkit"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ecrl.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-01-12T18:56:26.000Z","updated_at":"2025-02-13T19:41:45.000Z","dependencies_parsed_at":"2025-02-09T23:11:02.370Z","dependency_job_id":"ed57b739-510e-411e-bd3b-50c45ef6f490","html_url":"https://github.com/ecrl/graphchem","commit_stats":{"total_commits":71,"total_committers":2,"mean_commits":35.5,"dds":0.04225352112676062,"last_synced_commit":"cce118d55b131da6aca6ab2949a65da2b68167df"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecrl%2Fgraphchem","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecrl%2Fgraphchem/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecrl%2Fgraphchem/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecrl%2Fgraphchem/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ecrl","download_url":"https://codeload.github.com/ecrl/graphchem/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248681704,"owners_count":21144739,"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":["computational-chemistry","graph-neural-networks","message-passing","pytorch","pytorch-geometric","rdkit"],"created_at":"2024-09-27T12:43:15.023Z","updated_at":"2025-04-13T08:22:36.207Z","avatar_url":"https://github.com/ecrl.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![UML Energy \u0026 Combustion Research Laboratory](https://sites.uml.edu/hunter-mack/files/2021/11/ECRL_final.png)](http://faculty.uml.edu/Hunter_Mack/)\n\n# GraphChem: Graph-based machine learning for chemical property prediction\n\n[![GitHub version](https://badge.fury.io/gh/ecrl%2FGraphChem.svg)](https://badge.fury.io/gh/ecrl%2FGraphChem)\n[![PyPI version](https://badge.fury.io/py/graphchem.svg)](https://badge.fury.io/py/graphchem)\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/ecrl/GraphChem/master/LICENSE.txt)\n[![Documentation Status](https://readthedocs.org/projects/graphchem/badge/?version=latest)](https://graphchem.readthedocs.io/en/latest/?badge=latest)\n\n**GraphChem** is an open source Python package for constructing graph-based machine learning models with a focus on fuel property prediction.\n\n# Installation:\n\n### Prerequisites:\n- Have Python 3.11+ installed\n\n### Method 1: pip\n```\n$ pip install graphchem\n```\n\n### Method 2: From Source\n```\n$ git clone https://github.com/ecrl/graphchem\n$ cd graphchem\n$ python -m pip install .\n```\n\nIf any errors occur when installing dependencies, namely with RDKit, PyTorch, or torch-geometric, visit their installation pages and follow the installation instructions: [RDKit](https://www.rdkit.org/docs/Install.html), [PyTorch](https://pytorch.org/get-started/locally/), [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)\n\n# Usage:\n\nFor advanced usage, head over to our [API documentation page](https://graphchem.readthedocs.io/en/latest/).\n\n# Examples\n\nTo view some examples of how GraphChem can be used, head over to our [examples](https://github.com/ecrl/graphchem/tree/master/examples) folder on GitHub.\n\n# Contributing, Reporting Issues and Other Support:\n\nTo contribute to GraphChem, make a pull request. Contributions should include tests for new features added, as well as extensive documentation.\n\nTo report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version.\n\nFor additional support/questions, contact Travis Kessler (Travis_Kessler@student.uml.edu).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecrl%2Fgraphchem","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fecrl%2Fgraphchem","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecrl%2Fgraphchem/lists"}