Exploring neural network training strategies to determine phase transitions in frustrated magnetic models

• Autores: Corte, Inés Raquel; Acevedo, Santiago Daniel; Arlego, Marcelo José Fabián; Lamas, Carlos Alberto
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Ingeniería; Física; Frustrated magnetism; Machine learning; Ising model; Honeycomb lattice; Square lattice; Neural networks
• Institución: Universidad Nacional de La Plata

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.
Facultad de Ingeniería