A DMRG study of transport properties and correlations of quantum dots

ORAL

Abstract

We study transport through quantum dots using the time-dependent density matrix renormalization group method (tDMRG), recently proposed as a powerful computational tool to investigate transport through interacting nanostructures [1]. Since this technique relies on the numerical solution of finite clusters, we analyze the finite-size dependence of both static properties such as spin and charge fluctuations, spin-spin correlations and the conductance in detail, focusing on the example of one quantum dot. Our study reveals a crucial influence of global quantum numbers of finite clusters such as total spin on the results of tDMRG simulations, reflected in even-odd effects. We further establish a connection between the size of charge fluctuations on the quantum dot and the convergence of tDMRG with system size. Similar substantial even-odd effects exist within the framework of another technique, the embedded cluster approximation method (ECA). For the example of three quantum dots, we show that such even-odd effects strongly affect the spin fluctuations, leading to qualitatively different results for the conductance within ECA. [1] Al-Hassanieh et al., Phys. Rev. B 73, 195304 (2006)

Authors

  • Fabian Heidrich-Meisner

    The University of Tennessee at Knoxville and ORNL

  • K. A. Al-Hassanieh

    The University of Tennessee at Knoxville and ORNL, Oak Ridge National Laboratory, Oak Ridge TN, and University of Tennessee, Knoxville TN 37831, USA

  • Elbio Dagotto

    Oak Ridge National Lab, Oak Ridge, TN and University of Tennessee, Knoxville, TN, The University of Tennessee at Knoxville and ORNL, Oak Ridge National Laboratory, Materials Science and Technology Division and University of Tennessee, Knoxville, University of Tennessee and Oak Ridge National Laboratory

  • George Martins

    Oakland University, Michigan, Physics Department, Oakland University, Rochester, MI

  • Adrian Feiguin

    Microsoft Research, Station Q, Microsoft Q, The University of California at Santa Barbara