  Atomistic simulations provide insights into structure-property relations on
an atomic size and length scale, that are complementary to the macroscopic
observables that can be obtained from experiments. Quantitative predictions,
however, are usually hindered by the need to strike a balance between the
accuracy of the calculation of the interatomic potential and the modelling of
realistic thermodynamic conditions. Machine-learning techniques make it
possible to efficiently approximate the outcome of accurate
electronic-structure calculations, that can therefore be combined with
extensive thermodynamic sampling. We take elemental nickel as a prototypical
material, whose alloys have applications from cryogenic temperatures up to
close to their melting point, and use it to demonstrate how a combination of
machine-learning models of electronic properties and statistical sampling
methods makes it possible to compute accurate finite-temperature properties at
an affordable cost. We demonstrate the calculation of a broad array of bulk,
interfacial and defect properties over a temperature range from 100 to 2500 K,
modeling also, when needed, the impact of nuclear quantum fluctuations and
electronic entropy. The framework we demonstrate here can be easily generalized
to more complex alloys and different classes of materials.
