77 citations · 293 across the 17 of their papers we have counts for
17 papers
Minimalist Neural Networks training for phase classification in diluted-Ising models
G. L. Garcia Pavioni, M. Arlego, C. A. Lamas
In this article, we explore the potential of artificial neural networks, which are trained using an exceptionally simplified catalog of ideal configurations encompassing both order…
Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
S. Acevedo, M. Arlego, C. A. Lamas
We apply unsupervised learning techniques to classify the different phases of the antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram o…
Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
I. Corte, S. Acevedo, M. Arlego +1
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 e…
Magnon crystals and magnetic phases in a Kagomé-stripe antiferromagnet
S. Acevedo, C. A. Lamas, M. Arlego +1
In this work we analyze the magnetization properties of an antiferromagnetic Kagomé stripe lattice, motivated by the recent synthesis of materials exhibiting this structure. By emp…
Quantum phase diagram of a frustrated antiferromagnet on the bilayer honeycomb lattice
Hao Zhang, Carlos A. Lamas, Marcelo Arlego +1
We study the spin-1/2 Heisenberg antiferromagnet on a bilayer honeycomb lattice including interlayer frustration. Using a set of complementary approaches, namely Schwinger bosons,…
Self consistent study of the quantum phases in a frustrated antiferromagnet on the bilayer honeycomb lattice
M. Arlego, C. A. Lamas, H. Zhang
We study the frustrated Heisenberg model on the bilayer honeycomb lattice. The ground-state energy and spin gap are calculated, using different bosonic representations at mean fiel…