sisl_toolbox.btd.DeviceGreen

class sisl_toolbox.btd.DeviceGreen(H, elecs, pivot, eta=0.0)[source]

Bases: object

Block-tri-diagonal Green function calculator

This class enables the extraction and calculation of some important quantities not currently accessible in TBtrans.

For instance it may be used to calculate scattering states from the Green function. Once scattering states have been calculated one may also calculate the eigenchannels.

Both calculations are very efficient and uses very little memory compared to the full matrices normally used.

Consider a regular 2 electrode setup with transport direction along the 3rd lattice vector. Then the following example may be used to calculate the eigen-channels:

import sisl
from sisl_toolbox.btd import *
# First read in the required data
H_elec = sisl.Hamiltonian.read("ELECTRODE.nc")
H = sisl.Hamiltonian.read("DEVICE.nc")
# remove couplings along the self-energy direction
# to ensure no fake couplings.
H.set_nsc(c=1)

# Read in a single tbtrans output which contains the BTD matrices
# and instructs this class how it should pivot the matrix to obtain
# a BTD matrix.
tbt = sisl.get_sile("siesta.TBT.nc")

# Define the self-energy calculators which will downfold the
# self-energies into the device region.
# Since a downfolding will be done it requires the device Hamiltonian.
H_elec.shift(tbt.mu("Left"))
left = DownfoldSelfEnergy("Left", s.RecursiveSI(H_elec, "-C", eta=tbt.eta("Left"),
                          tbt, H)
H_elec.shift(tbt.mu("Right") - tbt.mu("Left"))
left = DownfoldSelfEnergy("Right", s.RecursiveSI(H_elec, "+C", eta=tbt.eta("Right"),
                          tbt, H)

G = DeviceGreen(H, [left, right], tbt)

# Calculate the scattering state from the left electrode
# and then the eigen channels to the right electrode
state = G.scattering_state("Left", E=0.1)
eig_channel = G.eigenchannel(state, "Right")

To make this easier there exists a short-hand version that does the above:

G = DeviceGreen.from_fdf("RUN.fdf")

which reads all variables from the FDF file and parses them accordingly. This does not take all things into consideration, but should cover most problems.

Methods

`Hk`(args, *kwargs)
`Sk`(args, *kwargs)
`eigenchannel`(state, elec_to[, ret_coeff])	Calculate the eigen channel from scattering states entering electrodes elec_to
`from_fdf`(fdf[, prefix, use_tbt_se, eta])	Return a new `DeviceGreen` using information gathered from the fdf
`green`(E[, k, format])	Calculate the Green function for a given E and k point
`reset`()	Clean any memory used by this object
`scattering_matrix`(elec_from, elec_to, E[, ...])	Calculate the scattering matrix (S-matrix) between elec_from and elec_to
`scattering_state`(elec, E[, k, cutoff, method])	Calculate the scattering states for a given E and k point from a given electrode
`spectral`(elec, E[, k, format, method, herm])	Calculate the spectral function for a given E and k point from a given electrode

Hk(*args, **kwargs)[source]

Sk(*args, **kwargs)[source]

__init__(H, elecs, pivot, eta=0.0)[source]: Create Green function with Hamiltonian and BTD matrix elements

eigenchannel(state, elec_to, ret_coeff=False)[source]

Calculate the eigen channel from scattering states entering electrodes elec_to

The energy and k-point is inferred from the state object as returned from scattering_state.

The eigenchannels are the eigen states of the transmission matrix in the DOS weighted scattering states:

\[\begin{split}\mathbf A_{\mathfrak e_{\mathrm{from}} }(E,\mathbf k) \mathbf u_i &= 2\pi a_i \mathbf u_i \\ \mathbf t_{\mathbf u} &= \sum \langle \mathbf u | \boldsymbol\Gamma_{ \mathfrak e_{\mathrm{to}} } | \mathbf u\rangle\end{split}\]

where the eigenvectors of \(\mathbf t_{\mathbf u}\) are the coefficients of the DOS weighted scattering states (\(\sqrt{2\pi a_i} u_i\)) for the individual eigen channels. The eigenvalues are the transmission eigenvalues. Further details may be found in [6].

Parameters

state (sisl.physics.StateCElectron) – the scattering states as obtained from scattering_state
elec_to (str or int (list or not)) – which electrodes to consider for the transmission eigenchannel decomposition (the sum in the above equation)
ret_coeff (bool, optional) – return also the scattering state coefficients

Returns

T_eig (sisl.physics.StateCElectron) – the transmission eigenchannels, the T_eig.c contains the transmission eigenvalues.
coeff (sisl.physics.StateElectron) – coefficients of state that creates the transmission eigenchannels Only returned if ret_coeff is True. There is a one-to-one correspondance between coeff and T_eig (with a prefactor of \(\sqrt{2\pi}\)). This is equivalent to the T_eig states in the scattering state basis.

classmethod from_fdf(fdf, prefix='TBT', use_tbt_se=False, eta=None)[source]

Return a new DeviceGreen using information gathered from the fdf

Parameters

fdf (str or fdfSileSiesta) – fdf file to read the parameters from
prefix ({"TBT", "TS"}) – which prefix to use, if TBT it will prefer TBT prefix, but fall back to TS prefixes. If TS, only these prefixes will be used.
use_tbt_se (bool, optional) – whether to use the TBT.SE.nc files for self-energies or calculate them on the fly.

green(E, k=(0, 0, 0), format='array')[source]

Calculate the Green function for a given E and k point

The Green function is calculated as:

\[\mathbf G(E,\mathbf k) = \big[\mathbf S(\mathbf k) E - \mathbf H(\mathbf k) - \sum \boldsymbol \Sigma(E,\mathbf k)\big]^{-1}\]

Parameters

E (float) – the energy to calculate at, may be a complex value.
k (array_like, optional) – k-point to calculate the Green function at
format ({"array", "btd", "bm", "bd", "sparse"}) –
return the matrix in a specific format
- array: a regular numpy array (full matrix)
- btd: a block-matrix object with only the diagonals and first off-diagonals
- bm: a block-matrix object with diagonals and all off-diagonals
- bd: a block-matrix object with only diagonals (no off-diagonals)
- sparse: a sparse-csr matrix for the sparse elements as found in the Hamiltonian

reset()[source]: Clean any memory used by this object

scattering_matrix(elec_from, elec_to, E, k=(0, 0, 0), cutoff=0.0)[source]

Calculate the scattering matrix (S-matrix) between elec_from and elec_to

The scattering matrix is calculated as

\[\mathbf S_{\mathfrak e_{\mathrm{to}}\mathfrak e_{\mathrm{from}} }(E, \mathbf) = -\delta_{\alpha\beta} + i \tilde{\boldsymbol\Gamma}_{\mathfrak e_{\mathrm{to}}} \mathbf G \tilde{\boldsymbol\Gamma}_{\mathfrak e_{\mathrm{from}}}\]

Here \(\tilde{\boldsymbol\Gamma}\) is defined as:

\[\begin{split}\boldsymbol\Gamma(E,\mathbf k) \mathbf u_i &= \lambda_i \mathbf u_i \\ \tilde{\boldsymbol\Gamma}(E,\mathbf k) &= \operatorname{diag}\{ \sqrt{\boldsymbol\lambda} \} \mathbf u\end{split}\]

Once the scattering matrices have been calculated one can calculate the full transmission function

\[T_{\mathfrak e_{\mathrm{from}}\mathfrak e_{\mathrm{to}} }(E, \mathbf k) = \operatorname{Tr}\big[ \mathbf S_{\mathfrak e_{\mathrm{to}}\mathfrak e_{\mathrm{from}} }^\dagger \mathbf S_{\mathfrak e_{\mathrm{to}}\mathfrak e_{\mathrm{from}} }\big]\]

Parameters

elec_from (str or int) – the electrode where the scattering matrix originates from
elec_to (str or int or list of) – where the scattering matrix ends in.
E (float) – the energy to calculate at, may be a complex value.
k (array_like, optional) – k-point to calculate the scattering matrix at
cutoff (float, optional) – cutoff the eigen states of the broadening matrix that are below this value. I.e. only \(\lambda\) values above this value will be used. A too high value will remove too many eigen states and results will be wrong. A small value improves precision at the cost of bigger matrices.

Returns

scat_matrix – for each elec_to a scattering matrix will be returned. Its dimensions will be depending on the cutoff value at the cost of precision.

Return type

numpy.ndarray or tuple of numpy.ndarray

scattering_state(elec, E, k=(0, 0, 0), cutoff=0.0, method='svd:gamma', *args, **kwargs)[source]

Calculate the scattering states for a given E and k point from a given electrode

The scattering states are the eigen states of the spectral function:

\[\mathbf A_{\mathfrak e}(E,\mathbf k) \mathbf u_i = 2\pi a_i \mathbf u_i\]

where \(a_i\) is the DOS carried by the \(i\)’th scattering state.

Parameters

elec (str or int) – the electrode to calculate the spectral function from
E (float) – the energy to calculate at, may be a complex value.
k (array_like, optional) – k-point to calculate the spectral function at
cutoff (float, optional) – cutoff the returned scattering states at some DOS value. Any scattering states with normalized eigenvalues lower than cutoff are discarded. The normalization is done by dividing the eigenvalue with the number of orbitals in the device region. This normalization ensures the same cutoff value has roughly the same meaning for different size devices. Values above or close to 1e-5 should be used with care.
method ({"svd:gamma", "svd:A", "full"}) – which method to use for calculating the scattering states. Use only the full method for testing purposes as it is extremely slow and requires a substantial amount of memory. The svd:gamma is the fastests while retaining complete precision. The svd:A may be even faster for very large systems with very little loss of precision, since it diagonalizes \(\mathbf A\) in the subspace of the electrode elec and reduces the propagated part of the spectral matrix.
cutoff_elec (float, optional) – Only used for method=svd:A. The initial block of the spectral function is diagonalized and only eigenvectors with eigenvalues >=cutoff_elec are retained, thus reducing the initial propagated modes. The normalization explained for cutoff also applies here.

Returns

scat – the scattering states from the spectral function. The scat.state contains the scattering state vectors (eigenvectors of the spectral function). scat.c contains the DOS of the scattering states scaled by \(1/(2\pi)\) so ensure correct density of states. One may recreate the spectral function with scat.outer(matrix=scat.c * 2 * pi).

Return type

StateCElectron

spectral(elec, E, k=(0, 0, 0), format='array', method='column', herm=True)[source]

Calculate the spectral function for a given E and k point from a given electrode

The spectral function is calculated as:

\[\mathbf A_{\mathfrak{e}}(E,\mathbf k) = \mathbf G(E,\mathbf k)\boldsymbol\Gamma_{\mathfrak{e}}(E,\mathbf k) \mathbf G^\dagger(E,\mathbf k)\]

Parameters

elec (str or int) – the electrode to calculate the spectral function from
E (float) – the energy to calculate at, may be a complex value.
k (array_like, optional) – k-point to calculate the spectral function at
format ({"array", "btd", "bm", "bd"}) –
return the matrix in a specific format
- array: a regular numpy array (full matrix)
- btd: a block-matrix object with only the diagonals and first off-diagonals
- bm: a block-matrix object with diagonals and all off-diagonals
- bd: same as btd, since they are already calculated
method ({"column", "propagate"}) – which method to use for calculating the spectral function. Depending on the size of the BTD blocks one may be faster than the other. For large systems you are recommended to time the different methods and stick with the fastest one, they are numerically identical.