{"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/probing-hidden-spin-order-with-interpretable","title":"Probing hidden spin order with interpretable machine learning","arxiv_id":"1804.08557","date":"2018-04-23","proceeding":null,"authors":["Jonas Greitemann","Ke Liu","Lode Pollet"],"abstract":"The search of unconventional magnetic and nonmagnetic states is a major topic\nin the study of frustrated magnetism. Canonical examples of those states\ninclude various spin liquids and spin nematics. However, discerning their\nexistence and the correct characterization is usually challenging. Here we\nintroduce a machine-learning protocol that can identify general nematic order\nand their order parameter from seemingly featureless spin configurations, thus\nproviding comprehensive insight on the presence or absence of hidden orders. We\ndemonstrate the capabilities of our method by extracting the analytical form of\nnematic order parameter tensors up to rank 6. This may prove useful in the\nsearch for novel spin states and for ruling out spurious spin liquid\ncandidates.","url_abs":"http://arxiv.org/abs/1804.08557v5","url_pdf":"http://arxiv.org/pdf/1804.08557v5.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":"probing-hidden-spin-order-with-interpretable","repo_url":"https://gitlab.physik.uni-muenchen.de/tk-svm/tksvm-op","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"probing-hidden-spin-order-with-interpretable","repo_url":"https://github.com/jgreitemann/svm-order-params","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08557","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}