Artículo
Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
Fecha de publicación:
08/2021
Editorial:
Elsevier Science
Revista:
Computational Materials Science
ISSN:
0927-0256
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
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Articulos de INST.DE FISICA LA PLATA
Citación
Corte, Inés Raquel; Acevedo, Santiago Daniel; Arlego, Marcelo José Fabián; Lamas, Carlos Alberto; Exploring neural network training strategies to determine phase transitions in frustrated magnetic models; Elsevier Science; Computational Materials Science; 198; 110702; 8-2021; 1-10
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