论文标题
大型累积特征值作为激子冷凝的签名
Large Cumulant Eigenvalue as a Signature of Exciton Condensation
论文作者
论文摘要
激子将激子缩合到单个量子态被称为激子凝结。激子凝结可能支持无摩擦能量流，最近在石墨烯双层和范德华异质结构中实现了。在这里，我们表明激子冷凝物可以通过降低的密度矩阵理论和累积理论的结合来预测。我们表明，当且仅当存在粒子孔累积部分中的较大特征值时，就会发生激子凝结。在热力学限制中，我们表明，大型特征值在上面的激子数量上界定。与粒子孔基质的特征值相反，累积基质的大特征值的优势是提供凝结程度的大小扩展度量。在这里，我们应用此特征来预测苯Lipkin模型和分子堆栈中的激子冷凝。计算签名在分子和材料中都应用了激子冷凝的预测。
The Bose-Einstein condensation of excitons into a single quantum state is known as exciton condensation. Exciton condensation, which potentially supports the frictionless flow of energy, has recently been realized in graphene bilayers and van der Waals heterostructures. Here we show that exciton condensates can be predicted from a combination of reduced density matrix theory and cumulant theory. We show that exciton condensation occurs if and only if there exists a large eigenvalue in the cumulant part of the particle-hole reduced density matrix. In the thermodynamic limit we show that the large eigenvalue is bounded from above by the number of excitons. In contrast to the eigenvalues of the particle-hole matrix, the large eigenvalue of the cumulant matrix has the advantage of providing a size-extensive measure of the extent of condensation. Here we apply this signature to predict exciton condensation in both the Lipkin model and molecular stacks of benzene. The computational signature has applications to the prediction of exciton condensation in both molecules and materials.
