2018
DOI: 10.1103/physrevb.97.235123
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Nematic quantum phases in the bilayer honeycomb antiferromagnet

Abstract: The spin−1/2 Heisenberg antiferromagnet on the honeycomb bilayer lattice is shown to display a rich variety of semiclassical and genuinely quantum phases, controlled by the interplay between intralayer frustration and interlayer exchange. Employing a complementary set of techniques, comprising spin rotationally invariant Schwinger boson mean field theory, bond operators, and series expansions we unveil the quantum phase diagram, analyzing low-energy excitations and order parameters. By virtue of Schwinger boso… Show more

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Cited by 5 publications
(4 citation statements)
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“…Combining this with the critical line from the resummed SE, it is very tempting to speculate that the single-layer QSL, anticipated on the line 1 J1 = 0, is confined to the lower part of this hourglass and terminates within its constriction. This is very reminiscent of a somewhat similar situation of a QSL surrounded by a QDM and two reentrant LRO phases in the frustrated honeycomb bilayer Heisenberg model [50].…”
Section: Resultsmentioning
confidence: 81%
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“…Combining this with the critical line from the resummed SE, it is very tempting to speculate that the single-layer QSL, anticipated on the line 1 J1 = 0, is confined to the lower part of this hourglass and terminates within its constriction. This is very reminiscent of a somewhat similar situation of a QSL surrounded by a QDM and two reentrant LRO phases in the frustrated honeycomb bilayer Heisenberg model [50].…”
Section: Resultsmentioning
confidence: 81%
“…4 (b). Even more surprising, such a phase, separating QDM from magnetic spiral states has been found also in the frustrated honeycomb bilayer Heisenberg model [50], where the intervening phase displayed nematic character. Consolidating our present findings by resummation or higher-order SE remains an open question beyond this work.…”
Section: B Two-particle Excitationsmentioning
confidence: 87%
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“…The transfer learning results presented in this work indicate that neural networks, in particular convolutional networks, can be adequate generic classifiers, exhibiting high performance when properly trained in minimal architectures, even in cases of high degeneracy such as the frustrated systems already analyzed. We plan to apply similar methods to other frustrated models at classical [44] and quantum level [45][46][47].…”
Section: Discussionmentioning
confidence: 99%