Machine assisted identification of unconventional order and ground-state selection in a breathing pyrochlore magnet
ORAL
Abstract
Machine-learning is drawing enormous attention in physics and has proven useful for sovling various physical problems. However, there are still very few instances of such techniques being applied to hard problems and providing new insights. In this work, we apply the tensorial-kernel support-vector-machine (TK-SVM) method to a classical anti-ferromagnet with Dzyaloshinskii–Moriya interaction on the breathing pyrochlore lattice, where we uncover the nature of the q = W phase below a rank-2 U(1) spin liquid found in PRL 124, 127203 (2020). Our machine identifies the previously unknown order parameter of this phase and the constraint that selects the ground states, whose construction is sufficiently intricate and was not realized using traditional methods.
*Nicolas Sadoune, Ke Liu and Lode Pollet are supported by Grants FP7/ERC Consolidator Grant No. 771891 (QSIMCORR) and DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2111 – 390814868. Ludovic D. Jaubert acknowledges financial support by the “Agence Nationale de la Recherche” under Grant No. ANR-18-CE30-0011-01 as well as CNRS (PICS No. 228338). Han Yan and Nic Shannon are supported by the Theory of Quantum Matter Unit, OIST.
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Presenters
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Nicolas Sadoune
- Arnold Sommerfeld Center for Theoretical Physics, University of Munich
- Ludwig Maximilian University of Munich