  This paper introduces an active learning approach to the fitting of machine
learning interatomic potentials. Our approach is based on the D-optimality
criterion for selecting atomic configurations on which the potential is fitted.
It is shown that the proposed active learning approach is highly efficient in
training potentials on the fly, ensuring that no extrapolation is attempted and
leading to a completely reliable atomistic simulation without any significant
decrease in accuracy. We apply our approach to molecular dynamics and structure
relaxation, and we argue that it can be applied, in principle, to any other
type of atomistic simulation. The software, test cases, and examples of usage
are published at http://gitlab.skoltech.ru/shapeev/mlip/.
