{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dscribe-library-of-descriptors-for-machine","title":"DScribe: Library of Descriptors for Machine Learning in Materials Science","arxiv_id":"1904.08875","date":"2019-04-18","proceeding":null,"authors":["Lauri Himanen","Marc O. J. Jäger","Eiaki V. Morooka","Filippo Federici Canova","Yashasvi S. Ranawat","David Z. Gao","Patrick Rinke","Adam S. Foster"],"abstract":"DScribe is a software package for machine learning that provides popular\nfeature transformations (\"descriptors\") for atomistic materials simulations.\nDScribe accelerates the application of machine learning for atomistic property\nprediction by providing user-friendly, off-the-shelf descriptor\nimplementations. The package currently contains implementations for Coulomb\nmatrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR),\nAtom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions\n(SOAP). Usage of the package is illustrated for two different applications:\nformation energy prediction for solids and ionic charge prediction for atoms in\norganic molecules. The package is freely available under the open-source Apache\nLicense 2.0.","url_abs":"http://arxiv.org/abs/1904.08875v1","url_pdf":"http://arxiv.org/pdf/1904.08875v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dscribe-library-of-descriptors-for-machine","repo_url":"https://github.com/SINGROUP/dscribe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"formation-energy","task_name":"Formation Energy"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.08875","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}