  Large-scale atomistic computer simulations of materials rely on interatomic
potentials providing computationally efficient predictions of energy and
Newtonian forces. Traditional potentials have served in this capacity for over
three decades. Recently, a new class of potentials has emerged, which is based
on a radically different philosophy. The new potentials are constructed using
machine-learning (ML) methods and a massive reference database generated by
quantum-mechanical calculations. While the traditional potentials are derived
from physical insights into the nature of chemical bonding, the ML potentials
utilize a high-dimensional mathematical regression to interpolate between the
reference energies. We review the current status of the interatomic potential
field, comparing the strengths and weaknesses of the traditional and ML
potentials. A third class of potentials is introduced, in which an ML model is
coupled with a physics-based potential to improve the transferability to
unknown atomic environments. The discussion is focused on potentials intended
for materials science applications. Possible future directions in this field
are outlined.
