  Over the past decade inter-atomic potentials based on machine-learning (ML)
techniques have become an indispensable tool in the atomic-scale modeling of
materials. Trained on energies and forces obtained from electronic-structure
calculations, they inherit their predictive accuracy, and extend greatly the
length and time scales that are accessible to explicit atomistic simulations.
Inexpensive predictions of the energetics of individual configurations have
facilitated greatly the calculation of the thermodynamics of materials,
including finite-temperature effects and disorder. More recently,
machine-learning models have been closing the gap with first-principles
calculations in another area: the prediction of arbitrarily complicated
functional properties, from vibrational and optical spectroscopies to
electronic excitations. The implementation of integrated machine-learning
models, that combine energetic and functional predictions with statistical and
dynamical sampling of atomic-scale properties is bringing the promise of
predictive, uncompromising simulations of existing and novel materials closer
to its full realisation.
