# Polarizability using automatic differentiation¶

Simple example for computing properties using (forward-mode) automatic differentation. For a more classical approach and more details about computing polarizabilities, see Polarizability by linear response.

In :
using DFTK
using LinearAlgebra
using ForwardDiff

# Construct PlaneWaveBasis given a particular electric field strength
# Again we take the example of a Helium atom.
function make_basis(ε::T; a=10., Ecut=30) where T
lattice=T(a) * I(3)  # lattice is a cube of a Bohrs
# Helium at the center of the box
positions = [[1/2, 1/2, 1/2]]

model = model_DFT(lattice, atoms, positions, [:lda_x, :lda_c_vwn];
extra_terms=[ExternalFromReal(r -> -ε * (r - a/2))],
symmetries=false)
PlaneWaveBasis(model; Ecut, kgrid=[1, 1, 1])  # No k-point sampling on isolated system
end

# dipole moment of a given density (assuming the current geometry)
function dipole(basis, ρ)
@assert isdiag(basis.model.lattice)
a  = basis.model.lattice[1, 1]
rr = [a * (r - 1/2) for r in r_vectors(basis)]
sum(rr .* ρ) * basis.dvol
end

# Function to compute the dipole for a given field strength
function compute_dipole(ε; tol=1e-8, kwargs...)
scfres = self_consistent_field(make_basis(ε; kwargs...), tol=tol)
dipole(scfres.basis, scfres.ρ)
end;


With this in place we can compute the polarizability from finite differences (just like in the previous example):

In :
polarizability_fd = let
ε = 0.01
(compute_dipole(ε) - compute_dipole(0.0)) / ε
end

n     Energy            log10(ΔE)   log10(Δρ)   Diag
---   ---------------   ---------   ---------   ----
1   -2.770907117738                   -0.52    9.0
2   -2.772149038433       -2.91       -1.32    1.0
3   -2.772170030240       -4.68       -2.39    2.0
4   -2.772170722605       -6.16       -4.07    2.0
┌ Warning: Negative ρ detected
│   min_ρ = -1.237664896679902e-18
└ @ DFTK /home/runner/work/DFTK.jl/DFTK.jl/src/densities.jl:7
5   -2.772170723005       -9.40       -5.00    2.0
n     Energy            log10(ΔE)   log10(Δρ)   Diag
---   ---------------   ---------   ---------   ----
1   -2.770681324509                   -0.53    8.0
2   -2.772036828676       -2.87       -1.30    1.0
3   -2.772083327254       -4.33       -2.78    2.0
4   -2.772083416680       -7.05       -3.80    2.0
5   -2.772083417796       -8.95       -4.60    2.0

Out:
1.77364460650267

We do the same thing using automatic differentiation. Under the hood this uses custom rules to implicitly differentiate through the self-consistent field fixed-point problem.

In :
polarizability = ForwardDiff.derivative(compute_dipole, 0.0)
println()
println("Polarizability via ForwardDiff:       $polarizability") println("Polarizability via finite difference:$polarizability_fd")

n     Energy            log10(ΔE)   log10(Δρ)   Diag
---   ---------------   ---------   ---------   ----
1   -2.770741463218                   -0.53    8.0
2   -2.772049843356       -2.88       -1.30    1.0
3   -2.772082720625       -4.48       -2.69    1.0
4   -2.772083413349       -6.16       -3.77    2.0
5   -2.772083417381       -8.39       -4.22    2.0

Polarizability via ForwardDiff:       1.7724740060017201
Polarizability via finite difference: 1.77364460650267