GreenFunctions

Up to now we have seen how to define Lattices, Models, Hamiltonians and Bandstructures. Most problems require the computation of different physical observables for these objects, e.g. the local density of states or various transport coefficients. We reduce this general problem to the computation of the retarded Green function

$G^r_{ij}(\omega) = \langle i|(\omega-H-\Sigma(\omega))^{-1}|j\rangle$

where i, j are orbitals, H is the (possibly infinite) Hamiltonian matrix, and Σ(ω) is the self-energy coming from any coupling to other systems (typically described by their own AbstractHamiltonian).

We split the problem of computing Gʳᵢⱼ(ω) of a given h::AbstractHamiltonian into four steps:

1. Attach self-energies to h using the command oh = attach(h, args...). This produces a new object oh::OpenHamiltonian with a number of Contacts, numbered 1 to N
2. Use g = greenfunction(oh, solver) to build a g::GreenFunction representing Gʳ (at arbitrary ω and i,j), where oh::OpenHamiltonian and solver::AbstractGreenSolver (see GreenSolvers below for available solvers)
3. Evaluate gω = g(ω; params...) at fixed energy ω and model parameters, which produces a gω::GreenSolution
4. Slice gω[sᵢ, sⱼ] or gω[sᵢ] == gω[sᵢ, sᵢ] to obtain Gʳᵢⱼ(ω) as a flat matrix, where sᵢ, sⱼ are either site selectors over sites spanning orbitals i,j, integers denoting contacts, 1 to N, or : denoting all contacts merged together.

A simple example

Here is a simple example of the Green function of a 1D lead with two sites per unit cell, a boundary at cell = 0, and with no attached self-energies for simplicity

julia> hlead = LP.square() |> supercell((1,0), region = r -> 0 <= r[2] < 2) |> hopping(1);

julia> glead = greenfunction(hlead, GreenSolvers.Schur(boundary = 0))
GreenFunction{Float64,2,1}: Green function of a Hamiltonian{Float64,2,1}
  Solver          : AppliedSchurGreenSolver
  Contacts        : 0
  Contact solvers : ()
  Contact sizes   : ()
  Hamiltonian{Float64,2,1}: Hamiltonian on a 1D Lattice in 2D space
    Bloch harmonics  : 3
    Harmonic size    : 2 × 2
    Orbitals         : [1]
    Element type     : scalar (ComplexF64)
    Onsites          : 0
    Hoppings         : 6
    Coordination     : 3.0

julia> gω = glead(0.2)  # we first fix energy to ω = 0.2
GreenSolution{Float64,2,1}: Green function at arbitrary positions, but at a fixed energy

julia> gω[cells = 1:2]  # we now ask for the Green function between orbitals in the first two unit cells to the righht of the boundary
4×4 Matrix{ComplexF64}:
   0.1-0.858258im    -0.5-0.0582576im  -0.48-0.113394im   -0.2+0.846606im
  -0.5-0.0582576im    0.1-0.858258im    -0.2+0.846606im  -0.48-0.113394im
 -0.48-0.113394im    -0.2+0.846606im   0.104-0.869285im   0.44+0.282715im
  -0.2+0.846606im   -0.48-0.113394im    0.44+0.282715im  0.104-0.869285im

Note that the result is a 4 x 4 matrix, because there are 2 orbitals (one per site) in each of the two unit cells. Note also that the GreenSolvers.Schur used here allows us to compute the Green function between distant cells with little overhead

julia> @time gω[cells = 1:2];
  0.000067 seconds (70 allocations: 6.844 KiB)

julia> @time gω[cells = (SA[10], SA[100000])];
  0.000098 seconds (229 allocations: 26.891 KiB)

GreenSolvers

The currently implemented GreenSolvers (abbreviated as GS) are the following

• GS.SparseLU()

  For bounded (L=0) AbstractHamiltonians. Default for L=0.

  Uses a sparse LU factorization to compute the inverse of ⟨i|ω - H - Σ(ω)|j⟩, where Σ(ω) is the self-energy from the contacts.

• GS.Spectrum(; spectrum_kw...)

  For bounded (L=0) Hamiltonians. This solver does not accept ParametricHamiltonians. Convert to Hamiltonian with h(; params...) first.

  Uses a diagonalization of H, obtained with spectrum(H; spectrum_kw...), to compute the G⁰ᵢⱼ using the Lehmann representation ∑ₖ⟨i|ϕₖ⟩⟨ϕₖ|j⟩/(ω - ϵₖ). Any eigensolver supported by spectrum can be used here. If there are contacts, it dresses G⁰ using a T-matrix approach, G = G⁰ + G⁰TG⁰.

• GS.KPM(order = 100, bandrange = missing, kernel = I)

  For bounded (L=0) Hamiltonians, and restricted to sites belonging to contacts (see the section on Contacts).

  It precomputes the Chebyshev momenta, and incorporates the contact self energy with a T-matrix approach.

• GS.Schur(boundary = Inf)

  For 1D (L=1) AbstractHamiltonians with only nearest-cell coupling. Default for L=1.

  Uses a deflating Generalized Schur (QZ) factorization of the generalized eigenvalue problem to compute the unit-cell self energies. The Dyson equation then yields the Green function between arbitrary unit cells, which is further dressed using a T-matrix approach if the lead has any attached self-energy.

• GS.Bands(bandsargs...; boundary = missing, bandskw...)

  For unbounded (L>0) Hamiltonians.

  It precomputes a bandstructure b = bands(h, bandsargs...; kw..., split = false) and then uses analytic expressions for the contribution of each subband simplex to the GreenSolution. If boundary = dir => cell_pos, it takes into account the reflections on an infinite boundary perpendicular to Bravais vector number dir, so that all sites with cell index c[dir] <= cell_pos are removed. Contacts are incorporated using a T-matrix approach.

  To retrieve the bands from a g::GreenFunction that used the GS.Bands solver, we may use bands(g).

Attaching Contacts

A self energy Σ(ω) acting of a finite set of sites of h (i.e. on a LatticeSlice of lat = lattice(h)) can be incorporated using the attach command. This defines a new Contact in h. The general syntax is oh = attach(h, args...; sites...), where the sites directives define the Contact LatticeSlice (lat[siteselector(; sites...)]), and args can take a number of forms.

The supported attach forms are the following

• Generic self-energy

  attach(h, gs::GreenSlice, coupling::AbstractModel; sites...)

  This is the generic form of attach, which couples some sites i of a g::Greenfunction (defined by the slice gs = g[i]), to sites of h using a coupling model. This results in a self-energy Σ(ω) = V´⋅gs(ω)⋅V on h sites, where V and V´ are couplings matrices given by coupling.

• Dummy self-energy

  attach(h, nothing; sites...)

  This form merely defines a new contact on the specified sites, but adds no actual self-energy to it. It is meant as a way to refer to some sites of interest using the g[i::Integer] slicing syntax for GreenFunctions, where i is the contact index.

• Model self-energy

  attach(h, model::AbstractModel; sites...)

  This form defines a self-energy Σᵢⱼ(ω) in terms of model, which must be composed purely of parametric terms (@onsite and @hopping) that have ω as first argument, as in e.g. @onsite((ω, r) -> Σᵢᵢ(ω, r)) or @hopping((ω, r, dr) -> Σᵢⱼ(ω, r, dr)). This is a modellistic approach, wherein the self-energy is not computed from the properties of another AbstractHamiltonian, but rather has an arbitrary form defined by the user.

• Matched lead self-energy

  attach(h, glead::GreenFunction; reverse = false, transform = identity, sites...)

  Here glead is a GreenFunction of a 1D lead, possibly with a boundary.

  With this syntax sites must select a number of sites in h whose position match (after applying transform to them and modulo an arbitrary displacement) the sites in the unit cell of glead. Then, the coupling between these and the first unit cell of glead on the positive side of the boundary will be the same as between glead unitcells, i.e. V = hlead[(1,)], where hlead = hamiltonian(glead).

  If reverse == true, the lead is reversed before being attached, so that h is coupled through V = hlead[(-1,)] to the first unitcell on the negative side of the boundary. If there is no boundary, the cell = 0 unitcell of the glead is used.

• Generic lead self-energy

  attach(h, glead::GreenFunction, model::AbstractModel; reverse = false, transform = identity, sites...)

  The same as above, but without any restriction on sites. The coupling between these and the first unit cell of glead (transformed by transform) is constructed using model::TightbindingModel. The "first unit cell" is defined as above.

A more advanced example

Let us define the classical example of a multiterminal mesoscopic junction. We choose a square lattice, and a circular central region of radius 10, with four leads of width 5 coupled to it at right angles.

We first define a single lead Greenfunction and the central Hamiltonian

julia> glead = LP.square() |> onsite(4) - hopping(1) |> supercell((1, 0), region = r -> abs(r[2]) <= 5/2) |> greenfunction(GS.Schur(boundary = 0));

julia> hcentral = LP.square() |> onsite(4) - hopping(1) |> supercell(region = RP.circle(10) | RP.rectangle((22, 5)) | RP.rectangle((5, 22)));

The two rectangles overlayed on the circle above create the stubs where the leads will be attached:

We now attach glead four times using the Matched lead syntax

julia> Rot = r -> SA[0 -1; 1 0] * r;  # 90º rotation function

julia> g = hcentral |>
    attach(glead, region = r -> r[1] ==  11) |>
    attach(glead, region = r -> r[1] == -11, reverse = true) |>
    attach(glead, region = r -> r[2] ==  11, transform = Rot) |>
    attach(glead, region = r -> r[2] == -11, reverse = true, transform = Rot) |>
    greenfunction
GreenFunction{Float64,2,0}: Green function of a Hamiltonian{Float64,2,0}
  Solver          : AppliedSparseLUGreenSolver
  Contacts        : 4
  Contact solvers : (SelfEnergySchurSolver, SelfEnergySchurSolver, SelfEnergySchurSolver, SelfEnergySchurSolver)
  Contact sizes   : (5, 5, 5, 5)
  Hamiltonian{Float64,2,0}: Hamiltonian on a 0D Lattice in 2D space
    Bloch harmonics  : 1
    Harmonic size    : 353 × 353
    Orbitals         : [1]
    Element type     : scalar (ComplexF64)
    Onsites          : 0
    Hoppings         : 1320
    Coordination     : 3.73938

julia> qplot(g, children = (; selector = siteselector(; cells = 1:5), sitecolor = :blue))

Note that since we did not specify the solver in greenfunction, the L=0 default GS.SparseLU() was taken.

We can also visualize glead, which is defined on a 1D lattice with a boundary. Boundary cells are shown by default in red

Slicing and evaluation

As explained above, a g::GreenFunction represents a Green function of an OpenHamiltonian (i.e. AbstractHamiltonian with zero or more self-energies), but it does so for any energy ω or lattice sites.

- To specify `ω` (plus any parameters `params` in the underlying `AbstractHamiltonian`) we use the syntax `g(ω; params...)`, which yields an `gω::GreenSolution`
- To specify source (`sⱼ`) and drain (`sᵢ`) sites we use the syntax `g[sᵢ, sⱼ]` or `g[sᵢ] == g[sᵢ,sᵢ]`, which yields a `gs::GreenSlice`. `sᵢ` and `sⱼ` can be `SiteSelectors(; sites...)`, or an integer denoting a specific contact (i.e. sites with an attached self-energy) or `:` denoting all contacts merged together.
- If we specify both of the above we get the Green function between the orbitals of the specified sites at the specified energy, in the form of an `OrbitalSliceMatrix`, which is a special `AbstractMatrix` that knows about the orbitals in the lattice over which its matrix elements are defined.

Let us see this in action using the example from the previous section

julia> g[1, 3]
GreenSlice{Float64,2,0}: Green function at arbitrary energy, but at a fixed lattice positions

julia> g(0.2)
GreenSolution{Float64,2,0}: Green function at arbitrary positions, but at a fixed energy

julia> g(0.2)[1, 3]
5×5 OrbitalSliceMatrix{Matrix{ComplexF64}}:
 -2.56906+0.000123273im  -4.28767+0.00020578im   -4.88512+0.000234514im  -4.28534+0.00020578im    -2.5664+0.000123273im
 -4.28767+0.00020578im   -7.15613+0.00034351im   -8.15346+0.000391475im  -7.15257+0.00034351im    -4.2836+0.000205781im
 -4.88512+0.000234514im  -8.15346+0.000391475im  -9.29002+0.000446138im  -8.14982+0.000391476im  -4.88095+0.000234514im
 -4.28534+0.00020578im   -7.15257+0.00034351im   -8.14982+0.000391476im  -7.14974+0.000343511im  -4.28211+0.000205781im
  -2.5664+0.000123273im   -4.2836+0.000205781im  -4.88095+0.000234514im  -4.28211+0.000205781im  -2.56469+0.000123273im

julia> g(0.2)[siteselector(region = RP.circle(1, (0.5, 0))), 3]
2×5 OrbitalSliceMatrix{Matrix{ComplexF64}}:
 0.0749214+3.15744e-8im   0.124325+5.27948e-8im   0.141366+6.01987e-8im   0.124325+5.27948e-8im  0.0749214+3.15744e-8im
 -0.374862+2.15287e-5im  -0.625946+3.5938e-5im   -0.712983+4.09561e-5im  -0.624747+3.59379e-5im   -0.37348+2.15285e-5im

Diagonal slices

There is a special form of slicing that requests just the diagonal of a given g[sᵢ] == g[sᵢ,sᵢ]. It uses the syntax g[diagonal(sᵢ)]. Let us see it in action in a multiorbital example in 2D

julia> g = HP.graphene(a0 = 1, t0 = 1, orbitals = 2) |> greenfunction
GreenFunction{Float64,2,2}: Green function of a Hamiltonian{Float64,2,2}
  Solver          : AppliedBandsGreenSolver
  Contacts        : 0
  Contact solvers : ()
  Contact sizes   : ()
  Hamiltonian{Float64,2,2}: Hamiltonian on a 2D Lattice in 2D space
    Bloch harmonics  : 5
    Harmonic size    : 2 × 2
    Orbitals         : [2, 2]
    Element type     : 2 × 2 blocks (ComplexF64)
    Onsites          : 0
    Hoppings         : 6
    Coordination     : 3.0

julia> g(0.5)[diagonal(cells = (0, 0))]
4-element OrbitalSliceVector{Vector{ComplexF64}}:
  -0.3497363468480622 - 0.3118358260294266im
  -0.3497363468271048 - 0.31183582602942655im
  -0.3497363468402952 - 0.31183582602942667im
 -0.34973634686125243 - 0.3118358260294267im

Note that we get an OrbitalSliceVector, which is equal to the diagonal diag(g(0.5)[cells = (0, 0)]). Like the g OrbitalSliceMatrix, this vector is resolved in orbitals, of which there are two per site and four per unit cell in this case. Using diagonal(sᵢ; kernel = K) we can collect all the orbitals of different sites, and compute tr(g[site, site] * K) for a given matrix K. This is useful to obtain spectral densities. In the above example, and interpreting the two orbitals per site as the electron spin, we could obtain the spin density along the x axis, say, using σx = SA[0 1; 1 0] as kernel,

julia> g(0.5)[diagonal(cells = (0, 0), kernel = SA[0 1; 1 0])]
2-element OrbitalSliceVector{Vector{ComplexF64}}:
   4.26186044627701e-12 - 2.2846013280115095e-17im
 -4.261861877528737e-12 + 1.9177925470610777e-17im

which is zero in this spin-degenerate case

Visualizing a Green function

We can use qplot to visualize a GreenSolution in space. Here we define a bounded square lattice with an interesting shape, and attach a model self-energy to the right. Then we compute the Green function from each orbital in the contact to any other site in the lattice, and compute the norm over contact sites. The resulting vector is used as a shader for the color and radius of sites when plotting the system

julia> h = LP.square() |> onsite(4) - hopping(1) |> supercell(region = r -> norm(r) < 40*(1+0.2*cos(5*atan(r[2],r[1]))));

julia> g = h |> attach(@onsite(ω -> -im), region = r -> r[1] ≈ 47) |> greenfunction;

julia> gx1 = sum(abs2, g(0.1)[siteselector(), 1], dims = 2);

julia> qplot(h, hide = :hops, sitecolor = (i, r) -> gx1[i], siteradius = (i, r) -> gx1[i], minmaxsiteradius = (0, 2), sitecolormap = :balance)