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dc.date.accessioned | 2022-03-04T17:42:33Z | |
dc.date.available | 2022-03-04T17:42:33Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | http://sedici.unlp.edu.ar/handle/10915/131979 | |
dc.description.abstract | The transfer learning of a neural network is one of its most outstanding aspects and has given supervised learning with neural networks a prominent place in data science. Here we explore this feature in the context of strongly interacting many-body systems. Through case studies, we test the potential of this deep learning technique to detect phases and their transitions in frustrated spin systems, using fully-connected and convolutional neural networks. In addition, we explore a recently-introduced technique, which is at the middle point of supervised and unsupervised learning. It consists in evaluating the performance of a neural network that has been deliberately “confused” during its training. To properly demonstrate the capability of the “confusion” and transfer learning techniques, we apply them to a paradigmatic model of frustrated magnetism in two dimensions, to determine its phase diagram and compare it with high-performance Monte Carlo simulations. | en |
dc.language | en | es |
dc.subject | Frustrated magnetism | es |
dc.subject | Machine learning | es |
dc.subject | Ising model | es |
dc.subject | Honeycomb lattice | es |
dc.subject | Square lattice | es |
dc.subject | Neural networks | es |
dc.title | Exploring neural network training strategies to determine phase transitions in frustrated magnetic models | en |
dc.type | Articulo | es |
sedici.identifier.other | doi:10.1016/j.commatsci.2021.110702 | es |
sedici.identifier.other | arXiv:2009.00661 | es |
sedici.identifier.issn | 0927-0256 | es |
sedici.creator.person | Corte, Inés Raquel | es |
sedici.creator.person | Acevedo, Santiago Daniel | es |
sedici.creator.person | Arlego, Marcelo José Fabián | es |
sedici.creator.person | Lamas, Carlos Alberto | es |
sedici.subject.materias | Ingeniería | es |
sedici.subject.materias | Física | es |
sedici.description.fulltext | true | es |
mods.originInfo.place | Facultad de Ingeniería | es |
sedici.subtype | Articulo | es |
sedici.rights.license | Creative Commons Attribution 4.0 International (CC BY 4.0) | |
sedici.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
sedici.description.peerReview | peer-review | es |
sedici.relation.journalTitle | Computational Materials Science | es |
sedici.relation.journalVolumeAndIssue | vol. 198 | es |