{"id":27267805,"url":"https://github.com/aronwalsh/MLforMaterials","last_synced_at":"2025-04-11T10:02:20.711Z","repository":{"id":191570446,"uuid":"673835680","full_name":"aronwalsh/MLforMaterials","owner":"aronwalsh","description":"Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)","archived":false,"fork":false,"pushed_at":"2025-02-14T07:48:51.000Z","size":88427,"stargazers_count":70,"open_issues_count":0,"forks_count":11,"subscribers_count":2,"default_branch":"2025","last_synced_at":"2025-02-14T08:32:46.172Z","etag":null,"topics":["machine-learning","materials-chemistry","materials-informatics","materials-science"],"latest_commit_sha":null,"homepage":"https://aronwalsh.github.io/MLforMaterials","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aronwalsh.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":"2023-08-02T14:29:12.000Z","updated_at":"2025-02-14T07:47:59.000Z","dependencies_parsed_at":null,"dependency_job_id":"4c01953d-1313-4a4e-bf2a-c9934f1584d6","html_url":"https://github.com/aronwalsh/MLforMaterials","commit_stats":null,"previous_names":["aronwalsh/mlformaterials"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aronwalsh%2FMLforMaterials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aronwalsh%2FMLforMaterials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aronwalsh%2FMLforMaterials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aronwalsh%2FMLforMaterials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aronwalsh","download_url":"https://codeload.github.com/aronwalsh/MLforMaterials/tar.gz/refs/heads/2025","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248372335,"owners_count":21093134,"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","materials-chemistry","materials-informatics","materials-science"],"created_at":"2025-04-11T10:01:28.596Z","updated_at":"2025-04-11T10:02:20.699Z","avatar_url":"https://github.com/aronwalsh.png","language":"Jupyter Notebook","funding_links":[],"categories":["Educational Resources","Core Topics in Digital Chemistry"],"sub_categories":["Machine Learning and Materials Informatics"],"readme":"[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge\u0026logo=Jupyter)](https://jupyter.org/try)\n\n[![deploy-book](https://github.com/aronwalsh/MLforMaterials/actions/workflows/deploy.yml/badge.svg)](https://github.com/aronwalsh/MLforMaterials/actions/workflows/deploy.yml)\n[![made-with-Markdown](https://img.shields.io/badge/Made%20with-Markdown-1f425f.svg)](http://commonmark.org)\n[![CC-BY license](https://img.shields.io/badge/License-CC--BY-blue.svg)](https://creativecommons.org/licenses/by/4.0)\n\n# Machine Learning for Materials\n\nOnline resource of a practical machine learning course in the Department of Materials at Imperial College London.\n\nYou have the option to browse the files or download the complete folder using the green `clone or download` button on the top right of the screen ([zip file](https://github.com/aronwalsh/MLforMaterials/archive/master.zip)).\n\n## Course Description\n\n_Machine Learning for Materials_ (MATE70026) provides an introduction to statistical research tools for materials theory and simulation. It is aimed at senior undergraduate or junior postgraduate students. \n\nYou will consider how composition-structure-property information in materials science can be represented in a form suitable for machine learning. You will then build, train, and evaluate your own models using public tools and open datasets. \n\nA hybrid teaching style will be followed with a mixture of lectures and assignments. The course assumes a basic working knowledge of the Python 3 programming language.\n\n[Lecture Slides](./slides)\n\n[Post a Query](https://github.com/aronwalsh/MLforMaterials/issues)\n\n## Course Website\n\nYou can view the site at [https://aronwalsh.github.io/MLforMaterials](https://aronwalsh.github.io/MLforMaterials)\n\nTo build a local copy, first install [Jupyter Book](https://jupyterbook.org):\n\n`pip install -U jupyter-book`\n\nthen enter the repository and run \n\n`jupyter-book build .`\n\n## Acknowledgements\n\nThis module was developed by Aron Walsh with the assistance of [Anthony Onwuli](https://github.com/AntObi) and [Zhenzhu Li](https://github.com/lizhenzhupearl).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faronwalsh%2FMLforMaterials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faronwalsh%2FMLforMaterials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faronwalsh%2FMLforMaterials/lists"}