Simulation data for: "Two parameter scaling in the crossover from symmetry class BDI to AI"

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2020-06-12
2022-01-20

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2022-01-20

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Simulation data for: "Two parameter scaling in the crossover from symmetry class BDI to AI"

Published Date

2022-08-01

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Author Contact

Kasturirangan, Saumitran
kastu007@umn.edu

Type

Dataset
Simulation Data

Abstract

The transport statistics at finite energies near a quantum critical point in the presence of disorder were not well understood analytically. This was approached by performing extensive simulations of transport using the package KWANT for python for disordered 1D quantum chains and metallic arm-chair graphene nanoribbons. This dataset contains the resulting data for several system sizes, strengths, and energies. This was used to establish two-parameter scaling and characterize the transport statistics.

Description

Transport data for 1D chain and metallic arm-chair graphene ribbons with hopping disorder obtained using KWANT. There are two files, both pandas dataframes in pickle format. One file contains the transmission probabilities for every disorder configuration simulated. The other contains the aggregated data for the average and variance of log conductance as functions of the scaling parameters s and r

Referenced by

https://doi.org/10.1103/PhysRevB.105.174204

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DMR-2011401
DMR-1928166
DMR-2037654
Carnegie corporation of New York

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Kasturirangan, Saumitran; Kamenev, Alex; Burnell, Fiona J. (2022). Simulation data for: "Two parameter scaling in the crossover from symmetry class BDI to AI". Retrieved from the Data Repository for the University of Minnesota (DRUM), https://hdl.handle.net/11299/229873.

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