M
Marcelo José Fabián Arlego
Researcher at National University of La Plata
Publications - 46
Citations - 330
Marcelo José Fabián Arlego is an academic researcher from National University of La Plata. The author has contributed to research in topics: Magnetization & Frustration. The author has an hindex of 9, co-authored 44 publications receiving 274 citations. Previous affiliations of Marcelo José Fabián Arlego include National University of Lomas de Zamora & National Scientific and Technical Research Council.
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Quantum phases of the planar antiferromagnetic J 1 − J 2 − J 3 Heisenberg model
Johannes Reuther,Peter Wölfle,R. Darradi,Wolfram Brenig,Marcelo José Fabián Arlego,Johannes Richter +5 more
TL;DR: In this paper, a complementary analysis of the frustrated planar spin-1/2 quantum antiferromagnet (AFM) is presented, which shows a large window of a quantum paramagnetic (QP) phase situated among the N\'eel, spiral, and collinear states.
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Quantum phases in the frustrated Heisenberg model on the bilayer honeycomb lattice
TL;DR: In this paper, a combination of analytical and numerical techniques were used to study the phase diagram of the frustrated Heisenberg model on the bilayer honeycomb lattice using the Schwinger-boson description of the spin operators followed by a mean-field decoupling.
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Plaquette order in the J 1 -J 2 -J 3 model: Series expansion analysis
TL;DR: In this article, series expansion based on the flow equation method is employed to study the zero temperature properties of the spin-1/2 J1-J2-J3 antiferromagnet in two dimensions.
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Teaching Basic Quantum Mechanics in Secondary School Using Concepts of Feynman Path Integrals Method
TL;DR: In this paper, the authors discuss the teaching of basic quantum mechanics in high school using simulation software and avoid sophisticated mathematical formalism, using Feynman's path integral method instead of the usual formalism.
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Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
TL;DR: In this article, the authors apply unsupervised learning techniques to classify the different phases of the antiferromagnetic Ising model on the honeycomb lattice using convolutional autoencoders.