{"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/data-driven-physics-informed-descriptors-of","title":"Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides","arxiv_id":"2305.01806","date":"2023-05-02","proceeding":null,"authors":["Jiayu Peng","James Damewood","Rafael Gómez-Bombarelli"],"abstract":"The structural tunability and compositional diversity of multicomponent perovskite oxides have enabled their various applications, including catalysis and electronics. The cation ordering in these oxides, ranging from disordered (i.e., high-entropy) to ordered (e.g., rocksalt), profoundly influences their properties. While computational design tools can typically predict properties associated with a particular ordering, inferring which ordering -- if any -- will be observed in synthesized oxides remains challenging. Here, we leveraged first-principles simulations and machine learning to develop data-driven, physics-informed descriptors of experimental ordering in multicomponent perovskites and compared them with traditional physicochemical descriptors, e.g., ionic radii and oxidation states. The fitted low-dimensional classification models correctly rank up to 93% of compositions in an experimental dataset of 190 perovskites between cation-ordered and disordered, offering a rigorous benchmark between theory and experiments. Furthermore, these descriptors accelerate high-throughput virtual screening of multicomponent oxides by predicting their dominant ordering to avoid costly, exhaustive simulations of cation arrangements.","url_abs":"https://arxiv.org/abs/2305.01806v1","url_pdf":"https://arxiv.org/pdf/2305.01806v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"data-driven-physics-informed-descriptors-of","repo_url":"https://github.com/learningmatter-mit/perovskite-ordering-descriptors","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}