  Machine-learning force fields enable an accurate and universal description of
the potential energy surface of molecules and materials on the basis of a
training set of ab initio data. However, large-scale applications of these
methods rest on the possibility to train accurate machine learning models with
a small number of ab initio data. In this respect, active-learning strategies,
where the training set is self-generated by the model itself, combined with
linear machine-learning models are particularly promising. In this work, we
explore an active-learning strategy based on linear regression and able to
predict the model's uncertainty on predictions for molecular configurations not
sampled by the training set, thus providing a straightforward recipe for the
extension of the latter. We apply this strategy to the spectral neighbor
analysis potential and show that only tens of ab initio simulations of atomic
forces are required to generate stable force fields for room-temperature
molecular dynamics at or close to chemical accuracy. Moreover, the method does
not necessitate any conformational pre-sampling, thus requiring minimal user
intervention and parametrization.
