{"paper":{"title":"Learning physical descriptors for materials science by compressed sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"(2) Charles University, 3), (3) Humboldt-Universit\\\"at zu Berlin, 4) ((1) Fritz-Haber-Institut der Max-Planck-Gesellschaft, (4) University of California - Santa Barbara, Berlin, Berlin-Dahlem, Biochemistry, CA, Claudia Draxl (1, Czech Republic, Department of Chemistry, Department of Mathematical Analysis, Emre Ahmetcik (1), Germany, Institut f\\\"ur Physik, IRIS Adlershof, Jan Vybiral (2), Luca M. Ghiringhelli (1), Materials Department, Matthias Scheffler (1, Prague, Runhai Ouyang (1), Santa Barbara, Sergey V. Levchenko (1), USA)","submitted_at":"2016-12-13T17:06:01Z","abstract_excerpt":"The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.04285","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}