{"paper":{"title":"Bloch oscillations in graphene from an artificial neural network study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"A. Raya, C. E. L\\'opez, J. A. Gonz\\'alez, M. Carrillo, S. Hern\\'andez-Ortiz","submitted_at":"2017-10-04T17:46:07Z","abstract_excerpt":"We develop an artificial neural network (ANN) approach to classify simulated signals corrsponding to the semi-classical description of Bloch oscillations in pristine graphene. After the ANN is properly trained, we consider the inverse problem of Bloch oscillations (BO),namely, a new signal is classified according to the external electric field strength oriented along either the zig-zag or arm-chair edges of the graphene membrane, with a correct classification that ranges from 82.6% to 99.3% depending on the accuracy of the predicted electric field. This approach can be improved depending on th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.01718","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"}