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