  In recent years the machine learning techniques have shown a great potential
in various problems from a multitude of disciplines, including materials design
and drug discovery. The high computational speed on the one hand and the
accuracy comparable to that of DFT on another hand make machine learning
algorithms efficient for high-throughput screening through chemical and
configurational space. However, the machine learning algorithms available in
the literature require large training datasets to reach the chemical accuracy
and also show large errors for the so-called outliers - the out-of-sample
molecules, not well-represented in the training set. In the present paper we
propose a new machine learning algorithm for predicting molecular properties
that addresses these two issues: it is based on a local model of interatomic
interactions providing high accuracy when trained on relatively small training
sets and an active learning algorithm of optimally choosing the training set
that significantly reduces the errors for the outliers. We compare our model to
the other state-of-the-art algorithms from the literature on the widely used
benchmark tests.
