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# # Custom solvers
# In this example, we show how to define custom solvers. Our system
# will again be silicon, because we are not very imaginative
using DFTK, LinearAlgebra
a = 10.26
lattice = a / 2 * [[0 1 1.];
[1 0 1.];
[1 1 0.]]
Si = ElementPsp(:Si, psp=load_psp("hgh/lda/Si-q4"))
atoms = [Si, Si]
positions = [ones(3)/8, -ones(3)/8]
## We take very (very) crude parameters
model = model_LDA(lattice, atoms, positions)
basis = PlaneWaveBasis(model; Ecut=5, kgrid=[1, 1, 1]);
# We define our custom fix-point solver: simply a damped fixed-point
function my_fp_solver(f, x0, max_iter; tol)
mixing_factor = .7
x = x0
fx = f(x)
for n = 1:max_iter
inc = fx - x
if norm(inc) < tol
x = x + mixing_factor * inc
fx = f(x)
(fixpoint=x, converged=norm(fx-x) < tol)
# Our eigenvalue solver just forms the dense matrix and diagonalizes
# it explicitly (this only works for very small systems)
function my_eig_solver(A, X0; maxiter, tol, kwargs...)
n = size(X0, 2)
A = Array(A)
E = eigen(A)
λ = E.values[1:n]
X = E.vectors[:, 1:n]
(; λ, X, residual_norms=[], iterations=0, converged=true, n_matvec=0)
# Finally we also define our custom mixing scheme. It will be a mixture
# of simple mixing (for the first 2 steps) and than default to Kerker mixing.
# In the mixing interface `δF` is ``(ρ_\text{out} - ρ_\text{in})``, i.e.
# the difference in density between two subsequent SCF steps and the `mix`
# function returns ``δρ``, which is added to ``ρ_\text{in}`` to yield ``ρ_\text{next}``,
# the density for the next SCF step.
struct MyMixing
n_simple # Number of iterations for simple mixing
MyMixing() = MyMixing(2)
function DFTK.mix_density(mixing::MyMixing, basis, δF; n_iter, kwargs...)
if n_iter <= mixing.n_simple
return δF # Simple mixing -> Do not modify update at all
## Use the default KerkerMixing from DFTK
DFTK.mix_density(KerkerMixing(), basis, δF; kwargs...)
# That's it! Now we just run the SCF with these solvers
scfres = self_consistent_field(basis;
# Note that the default convergence criterion is the difference in
# density. When this gets below `tol`, the
# "driver" `self_consistent_field` artificially makes the fixed-point
# solver think it's converged by forcing `f(x) = x`. You can customize
# this with the `is_converged` keyword argument to
# `self_consistent_field`.