Magnon spin currents in insulating magnets are useful for low-power spintronics. However, in magnets stacked by antiferromagnetic(AFM) exchange coupling, which have recently aroused significant interest for potential ...Magnon spin currents in insulating magnets are useful for low-power spintronics. However, in magnets stacked by antiferromagnetic(AFM) exchange coupling, which have recently aroused significant interest for potential applications in spintronics, Bose–Einstein distribution populates magnon states across all energies from opposite eigenmodes, and hence the magnon spin current is largely compensated. Contrary to this common observation,here, we show that magnets with X-type AFM stacking, where opposite magnetic sublattices form orthogonal intersecting chains, support giant magnon spin currents with minimal compensation. Our model Hamiltonian calculations predict magnetic chain locking of magnon spin currents in these X-type magnets, significantly reducing their compensation ratio. In addition, the one-dimensional nature of the chain-like magnetic sublattices enhances magnon spin conductivities surpassing those of two-dimensional ferromagnets and canonical altermagnets. Notably, uncompensated X-type magnets, such as odd-layer antiferromagnets and ferrimagnets, can exhibit magnon spin currents polarized opposite to those expected by their net magnetization. These unprecedented properties of X-type magnets, combined with their inherent advantages resulting from AFM coupling, offer a promising new path for low-power high-performance spintronics.展开更多
针对逐步Ⅱ型删失数据下Burr Type X分布的参数估计问题,提出模型参数的一种新的贝叶斯估计及相应的最大后验密度(HPD)置信区间.假设伽玛分布为待估参数的先验分布,考虑待估参数的条件后验分布未知、单峰且近似对称,选取以正态分布为提...针对逐步Ⅱ型删失数据下Burr Type X分布的参数估计问题,提出模型参数的一种新的贝叶斯估计及相应的最大后验密度(HPD)置信区间.假设伽玛分布为待估参数的先验分布,考虑待估参数的条件后验分布未知、单峰且近似对称,选取以正态分布为提议分布的Metropolis-Hastings(MH)算法生成后验样本,基于后验样本在平方误差损失函数下得到待估参数的贝叶斯估计和HPD置信区间.将基于MH算法得到的贝叶斯估计和HPD置信区间与基于EM算法得到的极大似然估计和置信区间在均方误差准则和精度意义下进行比较.Monte-Carlo模拟结果表明,基于MH算法得到的估计在均方误差准则下优于基于EM算法得到的极大似然估计,基于MH算法得到的HPD置信区间长度小于基于EM算法得到的置信区间长度.展开更多
基金supported by the National Key R&D Program of China (Grant No.2022YFA1403203)the National Natural Science Funds for Distinguished Young Scholar (Grant No.52325105)+2 种基金the National Natural Science Foundation of China (Grant Nos.12274411,12241405,52250418,and12404185)the Basic Research Program of the Chinese Academy of Sciences (CAS) Based on Major Scientific Infrastructures (Grant No.JZHKYPT-2021-08)the CAS Project for Young Scientists in Basic Research (Grant No.YSBR-084)。
文摘Magnon spin currents in insulating magnets are useful for low-power spintronics. However, in magnets stacked by antiferromagnetic(AFM) exchange coupling, which have recently aroused significant interest for potential applications in spintronics, Bose–Einstein distribution populates magnon states across all energies from opposite eigenmodes, and hence the magnon spin current is largely compensated. Contrary to this common observation,here, we show that magnets with X-type AFM stacking, where opposite magnetic sublattices form orthogonal intersecting chains, support giant magnon spin currents with minimal compensation. Our model Hamiltonian calculations predict magnetic chain locking of magnon spin currents in these X-type magnets, significantly reducing their compensation ratio. In addition, the one-dimensional nature of the chain-like magnetic sublattices enhances magnon spin conductivities surpassing those of two-dimensional ferromagnets and canonical altermagnets. Notably, uncompensated X-type magnets, such as odd-layer antiferromagnets and ferrimagnets, can exhibit magnon spin currents polarized opposite to those expected by their net magnetization. These unprecedented properties of X-type magnets, combined with their inherent advantages resulting from AFM coupling, offer a promising new path for low-power high-performance spintronics.
文摘针对逐步Ⅱ型删失数据下Burr Type X分布的参数估计问题,提出模型参数的一种新的贝叶斯估计及相应的最大后验密度(HPD)置信区间.假设伽玛分布为待估参数的先验分布,考虑待估参数的条件后验分布未知、单峰且近似对称,选取以正态分布为提议分布的Metropolis-Hastings(MH)算法生成后验样本,基于后验样本在平方误差损失函数下得到待估参数的贝叶斯估计和HPD置信区间.将基于MH算法得到的贝叶斯估计和HPD置信区间与基于EM算法得到的极大似然估计和置信区间在均方误差准则和精度意义下进行比较.Monte-Carlo模拟结果表明,基于MH算法得到的估计在均方误差准则下优于基于EM算法得到的极大似然估计,基于MH算法得到的HPD置信区间长度小于基于EM算法得到的置信区间长度.
