Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications.On the other hand, an accurate and efficient theoretical method is highly demanded for determining crit...Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications.On the other hand, an accurate and efficient theoretical method is highly demanded for determining critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks(GNNs) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNNs on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us to obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.展开更多
We investigate the collective plasma oscillations theoretically in multilayer 8-Pmmn borophene structures,where the tilted Dirac electrons in spatially separated layers are coupled via the Coulomb interaction.We calcu...We investigate the collective plasma oscillations theoretically in multilayer 8-Pmmn borophene structures,where the tilted Dirac electrons in spatially separated layers are coupled via the Coulomb interaction.We calculate the energy dispersions and Landau dampings of the multilayer plasmon excitations as a function of the total number of layers,the interlayer separation,and the different orientations.Like multilayer graphene,the plasmon spectrum in multilayer borophene consists of one in-phase optical mode and N-1 out-of-phase acoustical modes.We show that the plasmon modes possess kinks at the boundary of the interband single-particle continuum and the apparent anisotropic behavior.All the plasmon modes approach the same dispersion at a sufficiently large interlayer spacing in the short-wavelength limit.Especially along specific orientations,the optical mode could touch an energy maximum in the nondamping region,which shows non-monotonous behavior.Our work provides an understanding of the multilayer borophene plasmon and may pave the way for multilayer borophene-based plasmonic devices.展开更多
We theoretically investigate the Ruderman–Kittel–Kasuya–Yosida(RKKY) interaction in helical higher-order topological insulators(HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. O...We theoretically investigate the Ruderman–Kittel–Kasuya–Yosida(RKKY) interaction in helical higher-order topological insulators(HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. Our findings show that hinge-mediated interactions consist of Heisenberg, Ising, and Dzyaloshinskii–Moriya(DM) terms, exhibiting a decay with impurity spacing z and oscillations with Fermi energy εF. These interactions demonstrate ferromagnetic behaviors for the Heisenberg and Ising terms and alternating behavior for the DM term. In contrast, bulk-mediated interactions include Heisenberg, twisted Ising, and DM terms, with a conventional cubic oscillating decay. This study highlights the nuanced interplay between hinge and bulk RKKY interactions in HOTIs, offering insights into designs of next-generation quantum devices based on HOTIs.展开更多
基金supported by the Scientific Research Program from Science and Technology Bureau of Chongqing City (Grant No. cstc2020jcyj-msxm X0684)the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202000639)in part by the National Natural Science Foundation of China (Grant No. 12147102)
文摘Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications.On the other hand, an accurate and efficient theoretical method is highly demanded for determining critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks(GNNs) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNNs on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us to obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.
基金supported by the Scientific Research Program from Science and Technology Bureau of Chongqing City(Grant No.cstc2020jcyj-msxmX0684)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202000639)in part by the National Natural Science Foundation of China(Grant No.12147102)。
文摘We investigate the collective plasma oscillations theoretically in multilayer 8-Pmmn borophene structures,where the tilted Dirac electrons in spatially separated layers are coupled via the Coulomb interaction.We calculate the energy dispersions and Landau dampings of the multilayer plasmon excitations as a function of the total number of layers,the interlayer separation,and the different orientations.Like multilayer graphene,the plasmon spectrum in multilayer borophene consists of one in-phase optical mode and N-1 out-of-phase acoustical modes.We show that the plasmon modes possess kinks at the boundary of the interband single-particle continuum and the apparent anisotropic behavior.All the plasmon modes approach the same dispersion at a sufficiently large interlayer spacing in the short-wavelength limit.Especially along specific orientations,the optical mode could touch an energy maximum in the nondamping region,which shows non-monotonous behavior.Our work provides an understanding of the multilayer borophene plasmon and may pave the way for multilayer borophene-based plasmonic devices.
基金supported by the research foundation of Institute for Advanced Sciences of CQUPT(Grant No.E011A2022328)。
文摘We theoretically investigate the Ruderman–Kittel–Kasuya–Yosida(RKKY) interaction in helical higher-order topological insulators(HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. Our findings show that hinge-mediated interactions consist of Heisenberg, Ising, and Dzyaloshinskii–Moriya(DM) terms, exhibiting a decay with impurity spacing z and oscillations with Fermi energy εF. These interactions demonstrate ferromagnetic behaviors for the Heisenberg and Ising terms and alternating behavior for the DM term. In contrast, bulk-mediated interactions include Heisenberg, twisted Ising, and DM terms, with a conventional cubic oscillating decay. This study highlights the nuanced interplay between hinge and bulk RKKY interactions in HOTIs, offering insights into designs of next-generation quantum devices based on HOTIs.
