{"id":"doi:10.1016/j.physb.2026.418739","kind":"article","title":"Permutation-invariant deep learning for predicting superconducting critical temperatures from chemical composition","authors":["Tejasvini Ramaswamy","Marimuthu Krishnan"],"author_count":2,"year":2026,"published":"2026-05-02","venue":"Physica B Condensed Matter","cited_by":1,"doi":"10.1016/j.physb.2026.418739","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.physb.2026.418739","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.physb.2026.418739","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W7160287187","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0).","topics":[{"slug":"superconductor-discovery","name":"Computational & AI-Driven Superconductor Discovery","domain_slug":"superconductivity"}],"cited_in_dossiers":[]}