Since DFTK is completely generic in the floating-point type
in its routines, there is no reason to perform the computation
using double-precision arithmetic (i.e.`Float64`

).
Other floating-point types such as `Float32`

(single precision)
are readily supported as well.
On top of that we already reported^{1} calculations
in DFTK using elevated precision
from DoubleFloats.jl
or interval arithmetic
using IntervalArithmetic.jl.
In this example, however, we will concentrate on single-precision
computations with `Float32`

.
The setup of such a reduced-precision calculation is basically identical
to the regular case, since Julia automatically compiles all routines
of DFTK at the precision, which is used for the lattice vectors.
Apart from setting up the model with an explicit cast of the lattice
vectors to `Float32`

, there is thus no change in user code required:

M. F. Herbst, A. Levitt, E. Cancès.

*A posteriori error estimation for the non-self-consistent Kohn-Sham equations*ArXiv 2004.13549↩

In [1]:

```
using DFTK
# Setup silicon lattice
a = 10.263141334305942 # lattice constant in Bohr
lattice = a / 2 .* [[0 1 1.]; [1 0 1.]; [1 1 0.]]
Si = ElementPsp(:Si, psp=load_psp(:Si, functional="lda"))
atoms = [Si => [ones(3)/8, -ones(3)/8]]
# Cast to Float32, setup model and basis
model = model_DFT(Array{Float32}(lattice), atoms, [:lda_x, :lda_c_vwn])
Ecut = 7
basis = PlaneWaveBasis(model, Ecut, kgrid=[4, 4, 4])
# Run the SCF
scfres = self_consistent_field(basis, tol=1e-4);
```

To check the calculation has really run in Float32, we check the energies and density are expressed in this floating-point type:

In [2]:

```
scfres.energies
```

Out[2]:

In [3]:

```
eltype(scfres.energies.total)
```

Out[3]:

In [4]:

```
eltype(scfres.ρ.real)
```

Out[4]: