  The active-space quantum chemical methods could provide very accurate
description of strongly correlated electronic systems, which is of tremendous
value for natural sciences. The proper choice of the active space is crucial,
but a non-trivial task. In this article, we present the neural network (NN)
based approach for automatic selection of active spaces, focused on transition
metal systems. The training set has been formed from artificial systems
composed from one transition metal and various ligands, on which we have
performed DMRG and calculated single-site entropy. On the selected set of
systems, ranging from small benchmark molecules up to larger challenging
systems involving two metallic centers, we demonstrate that our ML models could
correctly predict the importance of orbitals with the high accuracy. Also, the
ML models show a high degree of transferability on systems much larger than any
complex used in training procedures.
