Monitoring self-consistent field calculations

The self_consistent_field function takes as the callback keyword argument one function to be called after each iteration. This function gets passed the complete internal state of the SCF solver and can thus be used both to monitor and debug the iterations as well as to quickly patch it with additional functionality.

This example discusses a few aspects of the callback function taking again our favourite silicon example.

We setup silicon in an LDA model using the ASE interface to build the silicon lattice, see Creating slabs with ASE for more details.

In [1]:
using DFTK
using PyCall

silicon = pyimport("ase.build").bulk("Si")
atoms = load_atoms(silicon)
atoms = [ElementPsp(el.symbol, psp=load_psp(el.symbol, functional="lda")) => position
         for (el, position) in atoms]
lattice = load_lattice(silicon);

model = model_LDA(lattice, atoms)
kgrid = [3, 3, 3]  # k-point grid
Ecut = 5           # kinetic energy cutoff in Hartree
basis = PlaneWaveBasis(model, Ecut; kgrid=kgrid);

DFTK already defines a few callback functions for standard tasks. One example is the usual convergence table, which is defined in the callback ScfDefaultCallback. Another example is ScfPlotTrace, which records the total energy at each iteration and uses it to plot the convergence of the SCF graphically once it is converged. For details and other callbacks see src/scf/scf_callbacks.jl.

!!! note "Callbacks are not exported" Callbacks are not exported from the DFTK namespace as of now, so you will need to use them, e.g., as DFTK.ScfDefaultCallback and DFTK.ScfPlotTrace.

In this example we define a custom callback, which plots the change in density at each SCF iteration after the SCF has finished. For this we first define the empty plot canvas and an empty container for all the density differences:

In [2]:
using Plots
p = plot(yaxis=:log)
density_differences = Float64[];

The callback function itself gets passed a named tuple similar to the one returned by self_consistent_field, which contains the input and output density of the SCF step as ρin and ρout. Since the callback gets called both during the SCF iterations as well as after convergence just before self_consistent_field finishes we can both collect the data and initiate the plotting in one function.

In [3]:
using LinearAlgebra

function plot_callback(info)
    if info.stage == :finalize
        plot!(p, density_differences, label="|ρout - ρin|", markershape=:x)
    else
        push!(density_differences, norm(info.ρout.real - info.ρin.real))
    end
    info
end
callback = DFTK.ScfDefaultCallback()  plot_callback;

Notice that for constructing the callback function we chained the plot_callback (which does the plotting) with the ScfDefaultCallback, such that when using the plot_callback function with self_consistent_field we still get the usual convergence table printed. We run the SCF with this callback ...

In [4]:
scfres = self_consistent_field(basis, tol=1e-8, callback=callback);
n     Energy            Eₙ-Eₙ₋₁     ρout-ρin   Diag
---   ---------------   ---------   --------   ----
  1   -7.844864752901         NaN   1.97e-01    4.3 
  2   -7.850324551881   -5.46e-03   2.93e-02    1.0 
  3   -7.850613027425   -2.88e-04   2.77e-03    1.3 
  4   -7.850646744680   -3.37e-05   1.40e-03    2.5 
  5   -7.850647256498   -5.12e-07   8.16e-04    1.0 
  6   -7.850647505115   -2.49e-07   3.94e-05    1.0 
  7   -7.850647511478   -6.36e-09   7.94e-06    2.5 

... and show the plot

In [5]:
p
Out[5]:

The info object passed to the callback contains not just the densities but also the complete Bloch wave (in ψ), the occupation, band eigenvalues and so on. See src/scf/self_consistent_field.jl for all currently available keys.

!!! tip "Debugging with callbacks" Very handy for debugging SCF algorithms is to employ callbacks with an @infiltrate from Infiltrator.jl to interactively monitor what is happening each SCF step.