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Phase diagram of a distorted kagome antiferromagnet and application to Y-kapellasite 被引量:1
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作者 Max Hering francesco ferrari +4 位作者 Aleksandar Razpopov Igor I.Mazin Roser Valentí Harald O.Jeschke Johannes Reuther 《npj Computational Materials》 SCIE EI CSCD 2022年第1期79-88,共10页
We investigate the magnetism of a previously unexplored distorted spin-1/2 kagome model consisting of three symmetry-inequivalent nearest-neighbor antiferromagnetic Heisenberg couplings J_(■),J,and J′,and uncover a ... We investigate the magnetism of a previously unexplored distorted spin-1/2 kagome model consisting of three symmetry-inequivalent nearest-neighbor antiferromagnetic Heisenberg couplings J_(■),J,and J′,and uncover a rich ground state phase diagram even at the classical level.Using analytical arguments and numerical techniques we identify a collinear Q^(→)=0 magnetic phase,two unusual non-collinear coplanar Q^(→)=(1/3,1/3)phases and a classical spin liquid phase with a degenerate manifold of non-coplanar ground states,resembling the jammed spin liquid phase found in the context of a bond-disordered kagome antiferromagnet.We further show with density functional theory calculations that the recently synthesized Y-kapellasite Y_(3)Cu_(9)(OH)_(19)C_(l8) is a realization of this model and predict its ground state to lie in the region of Q^(→)=(1/3,1/3)order,which remains stable even after the inclusion of quantum fluctuation effects within variational Monte Carlo and pseudofermion functional renormalization group.The presented model opens a new direction in the study of kagome antiferromagnets. 展开更多
关键词 GROUP distorted DIAGRAM
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Statistical learning of engineered topological phases in the kagome superlattice of AV_(3)Sb_(5)
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作者 Thomas Mertz Paul Wunderlich +2 位作者 Shinibali Bhattacharyya francesco ferrari Roser Valentí 《npj Computational Materials》 SCIE EI CSCD 2022年第1期624-629,共6页
Recent experimental findings have reported the presence of unconventional charge orders in the enlarged(2×2)unit-cell of kagome metals AV3Sb5(A=K,Rb,Cs)and hinted towards specific topological signatures.Motivated... Recent experimental findings have reported the presence of unconventional charge orders in the enlarged(2×2)unit-cell of kagome metals AV3Sb5(A=K,Rb,Cs)and hinted towards specific topological signatures.Motivated by these discoveries,we investigate the types of topological phases that can be realized in such kagome superlattices.In this context,we employ a recently introduced statistical method capable of constructing topological models for any generic lattice.By analyzing large data sets generated from symmetry-guided distributions of randomized tight-binding parameters,and labeled with the corresponding topological index,we extract physically meaningful information.We illustrate the possible real-space manifestations of charge and bond modulations and associated flux patterns for different topological classes,and discuss their relation to present theoretical predictions and experimental signatures for the AV_(3)Sb_(5)family.Simultaneously,we predict higher-order topological phases that may be realized by appropriately manipulating the currently known systems. 展开更多
关键词 TOPOLOGICAL SIGNATURE meaningful
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Ensemble Machine Learning greatly improves ERA5 skills for wind energy applications
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作者 Mattia Cavaiola Peter Enos Tuju +2 位作者 francesco ferrari Gabriele Casciaro Andrea Mazzino 《Energy and AI》 2023年第3期197-206,共10页
The skill of ERA5 has been assessed in relation to the prediction of the wind energy associated with 28 SYNOP stations located in Italy for a time span of 20 years(2001–2020).For comparison,a WRF-based high-resolutio... The skill of ERA5 has been assessed in relation to the prediction of the wind energy associated with 28 SYNOP stations located in Italy for a time span of 20 years(2001–2020).For comparison,a WRF-based high-resolution downscaling(3 km horizontally)was also produced for the same period.We found that simple predictions based on materialized past wind measures outperform the wind energy predictions from ERA5.This result can be ascribed to the particularly complex characteristics of the Italian territory.Motivated by this expected behavior,we have implemented a Quantile Random Forest(QRF)calibration which greatly alleviates the problems encountered in the ERA5 reanalysis dataset.This technique provides a calibrated ensemble prediction system for the wind speed at the station.Surprisingly,the calibrated ERA5 outperforms wind energy estimations from the high-resolution 3-km downscaling.Once properly calibrated,the high-resolution downscaling provides predictions very similar to the calibrated ERA5.Limiting our conclusions to the estimation of wind energy over a long time span(here 20 years),having at disposal a high-resolution wind-field dataset does not necessarily mean greater accuracy.A careful calibration of the original coarser wind-field dataset produces better results than the raw high-resolution dataset. 展开更多
关键词 Wind energy ERA5 reanalysis Quantile random forest Machine learning calibration
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