Self-consistent mean-field problems

Here we show how to solve interacting-electron problems in Quantica, approximated at the mean field level. A mean field is a collection of onsite and hopping terms that are added to a given h::AbstractHamiltonian, that depend on the density matrix ρ. Since ρ itself depends on h, this defines a self-consistent problem.

If the mean field solution is dubbed Φ, the problem consists in finding a fixed point solution to the function Φ = M(Φ) for a certain function M that takes Φ, computes h with the added mean field onsite and hopping terms, computes the density matrix, and from that computes the new mean field Φ. To attack this problem we will employ non-spatial models and a new meanfield constructor.

Schematically the process is as follows:

• We start from an AbstractHamiltonian that includes a non-interacting model model_0 and non-spatial model model_1 + model_2 with a mean field parameter, e.g. Φ,

julia> model_0 = hopping(1); # a possible non-interacting model

julia> model_1 = @onsite((i; Φ = zerofield) --> Φ[i]);       # Onsite Hartree-Fock

julia> model_2 = @hopping((i, j; Φ = zerofield) -> Φ[i, j]); # Non-local Fock

julia> h = lat |> hamiltonian(model_0 + model_1 + model_2)

Here model_1 corresponds to Hartree and onsite-Fock mean field terms, while model_2 corresponds to inter-site Fock terms. The default value Φ = zerofield is an singleton object that represents no interactions, so model_1 and model_2 vanish by default.

• We build the GreenFunction of h with g = greenfunction(h, solver; kw...) using the GreenSolver of choice

• We construct a M::MeanField object using M = meanfield(g; potential = pot, other_options...)

  Here pot(r) is the charge-charge interaction potential between electrons. We can also specify hopselector directives to define which sites interacts, adding e.g. selector = (; range = 2) to other_options, to make sites at distance 2 interacting. See meanfield docstring for further details.

• We evaluate this M with Φ0 = M(µ, kBT; params...).

  This computes the density matrix at specific chemical potential µ and temperature kBT, and for specific parameters of h (possibly including Φ). Then it computes the appropriate Hartree and Fock terms, and stores them in the returned Φ0::OrbitalSliceMatrix, where Φ0ᵢⱼ = δᵢⱼ hartreeᵢ + fockᵢⱼ. In normal systems, these terms read

  $\text{hartree}_i = Q \sum_k v_H(r_i-r_k) \text{tr}(\rho_{kk}Q)$

  $\text{fock}_{ij} = -v_F(r_i-r_j) Q \rho_{ij} Q$

  where v_H and v_F are Hartree and Fock charge-charge interaction potentials (by default equal to pot), and the charge operator is Q (equal to the identity by default, but can be changed to implement e.g. spin-spin interactions).

  When computing Φ0 we don't specify Φ in params, so that Φ0 is evaluated using the non-interacting model, hence its name.

• The self-consistent condition can be tackled naively by iteration-until-convergence,

Φ0 = M(µ, kBT; params...)
Φ1 = M(µ, KBT; Φ = Φ0, params...)
Φ2 = M(µ, KBT; Φ = Φ1, params...)
...

A converged solution Φ, if found, should satisfy the fixed-point condition

Φ_sol ≈ M(µ, KBT; Φ = Φ_sol, params...)

Then, the self-consistent Hamiltonian is given by h(; Φ = Φ_sol, params...).

The key problem is to actually find the fixed point of the M function. The naive iteration above is not optimal, and often does not converge. To do a better job we should use a dedicated fixed-point solver.

Using an external fixed-point solver

We now show how to approach such a fixed-point problem. We will employ an external library to solve generic fixed-point problems, and show how to make it work with Quantica MeanField objects efficiently. Many generic fixed-point solver backends exist. Here we use the SIAMFANLEquations.jl package. It provides a simple utility aasol(f, x0) that computes the solution of f(x) = x with initial condition x0 using Anderson acceleration. This is an example of how it works to compute the fixed point of function f(x) = 1 + atan(x)

julia> using SIAMFANLEquations

julia> f!(x, x0) = (x .= 1 .+ atan.(x0))

julia> m = 3; x0 =  rand(3); vstore = rand(3, 3m+3);  # order m, initial condition x0, and preallocated space vstore

julia> aasol(f!, x0, m, vstore).solution
3-element Vector{Float64}:
 2.132267725272934
 2.132267725272908
 2.132267725284556

The package requires as input an in-place version f! of the function f, and the preallocation of some storage space vstore (see the aasol documentation). The package, as a few others, also requires the variable x and the initial condition x0 to be an AbstractArray (or a scalar, but we need the former for our use case), hence the broadcast dots above. In our case we will therefore need to translate back and forth from an Φ::OrbitalSliceMatrix to a real vector x to pass it to aasol. This translation is achieved using Quantica's serialize/deserialize funcionality.

Using Serializers with fixed-point solvers

With the serializer functionality we can build a version of the fixed-point function f that operates on real vectors. Let's take a 1D Hamiltonian with a sawtooth potential, and build a Hartree mean field (note the fock = nothing keyword)

julia> using SIAMFANLEquations

julia> h = LP.linear() |> supercell(4) |> onsite(r->r[1]) - hopping(1) + @onsite((i; phi = zerofield) --> phi[i]);

julia> M = meanfield(greenfunction(h); onsite = 1, selector = (; range = 0), fock = nothing)
MeanField{ComplexF64} : builder of Hartree-Fock-Bogoliubov mean fields
  Charge type      : scalar (ComplexF64)
  Hartree pairs    : 4
  Mean field pairs : 4
  Nambu            : false

julia> Φ0 = M(0.0, 0.0);

julia> function f!(x, x0, (M, Φ0))
        Φ = M(0.0, 0.0; phi = deserialize(Φ0, x0))
        copy!(x, serialize(Φ))
        return x
    end;

Then we can proceed as in the f(x) = 1 + atan(x) example

julia> m = 2; x0 = serialize(Φ0); vstore = rand(length(x0), 3m+3);  # order m, initial condition x0, and preallocated space vstore

julia> x = aasol(f!, x0, m, vstore; pdata = (M, Φ0)).solution
4-element Vector{ComplexF64}:
  0.5658185030962436 + 0.0im
   0.306216109313951 + 0.0im
 0.06696362342872919 + 0.0im
 0.06100176416107613 + 0.0im

julia> h´ = h(; phi = deserialize(Φ0, x))
Hamiltonian{Float64,1,1}: Hamiltonian on a 1D Lattice in 1D space
  Bloch harmonics  : 3
  Harmonic size    : 4 × 4
  Orbitals         : [1]
  Element type     : scalar (ComplexF64)
  Onsites          : 4
  Hoppings         : 8
  Coordination     : 2.0

julia> h´[()]
4×4 SparseArrays.SparseMatrixCSC{ComplexF64, Int64} with 10 stored entries:
 0.565819+0.0im     -1.0+0.0im          ⋅            ⋅
     -1.0+0.0im  1.30622+0.0im     -1.0+0.0im        ⋅
          ⋅         -1.0+0.0im  2.06696+0.0im   -1.0+0.0im
          ⋅              ⋅         -1.0+0.0im  3.061+0.0im

Note that the content of pdata is passed by aasol as a third argument to f!. We use this to pass the serializer s and U parameter to use.

GreenSolvers without support for ParametricHamiltonians

Some GreenSolver's, like GS.KPM, do not support ParametricHamiltonians. In such cases, the approach above will fail, since it will not be possible to build g before knowing phi. In such cases one would need to rebuild the meanfield object at each step of the fixed-point solver. This is one way to do it.

julia> using SIAMFANLEquations

julia> h = LP.linear() |> supercell(4) |> supercell |> onsite(1) - hopping(1) + @onsite((i; phi) --> phi[i]);

julia> M´(phi = zerofield) = meanfield(greenfunction(h(; phi), GS.Spectrum()); onsite = 1, selector = (; range = 0), fock = nothing)
M´ (generic function with 3 methods)

julia> Φ0 = M´()(0.0, 0.0);

julia> function f!(x, x0, (M´, Φ0))
        Φ = M´(deserialize(Φ0, x0))(0.0, 0.0)
        copy!(x, serialize(Φ))
        return x
           end;

julia> m = 2; x0 = serialize(Φ0); vstore = rand(length(x0), 3m+3);  # order m, initial condition x0, and preallocated space vstore

julia> x = aasol(f!, x0, m, vstore; pdata = (M´, Φ0)).solution
4-element Vector{ComplexF64}:
 0.15596283661234628 + 0.0im
 0.34403716338765444 + 0.0im
 0.34403716338765344 + 0.0im
 0.15596283661234572 + 0.0im