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
break
end
x = x + mixing_factor * inc
fx = f(x)
end
(; fixpoint=x, converged=norm(fx-x) < tol)
end;
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=[], n_iter=0, converged=true, n_matvec=0)
end;
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
end
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
else
# Use the default KerkerMixing from DFTK
DFTK.mix_density(KerkerMixing(), basis, δF; kwargs...)
end
end
That's it! Now we just run the SCF with these solvers
scfres = self_consistent_field(basis;
tol=1e-4,
solver=my_fp_solver,
eigensolver=my_eig_solver,
mixing=MyMixing());
n Energy log10(ΔE) log10(Δρ) Diag Δtime --- --------------- --------- --------- ---- ------ 1 -7.090073978406 -0.39 0.0 2.08s 2 -7.226157343674 -0.87 -0.65 0.0 890ms 3 -7.249782177445 -1.63 -1.17 0.0 79.8ms 4 -7.250986294028 -2.92 -1.48 0.0 39.6ms 5 -7.251258312426 -3.57 -1.78 0.0 46.1ms 6 -7.251319808416 -4.21 -2.07 0.0 52.0ms 7 -7.251334099764 -4.84 -2.35 0.0 54.1ms 8 -7.251337569219 -5.46 -2.62 0.0 54.0ms 9 -7.251338457710 -6.05 -2.88 0.0 109ms 10 -7.251338698748 -6.62 -3.14 0.0 39.8ms 11 -7.251338767946 -7.16 -3.39 0.0 45.2ms 12 -7.251338788853 -7.68 -3.64 0.0 52.1ms 13 -7.251338795449 -8.18 -3.88 0.0 54.3ms 14 -7.251338797603 -8.67 -4.12 0.0 72.0ms
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
.