  A new, open source, parallel, stand-alone software package (Fortnet) has been
developed, which implements Behler-Parrinello neural networks. It covers the
entire workflow from feature generation to the evaluation of generated
potentials, coupled with higher-level analysis such as the analytic calculation
of atomic forces. The functionality of the software package is demonstrated by
driving the training for the fitted correction functions of the density
functional tight binding (DFTB) method, which are commonly used to compensate
the inaccuracies resulting from the DFTB approximations to the Kohn-Sham
Hamiltonian. The usual two-body form of those correction functions limits the
transferability of the parameterizations between very different structural
environments. The recently introduced DFTB+ANN approach strives to lift these
limitations by combining DFTB with a near-sighted artificial neural network
(ANN). After investigating various approaches, we have found the combination of
DFTB with an ANN acting on-top of some baseline correction functions (delta
learning) the most promising one. It allowed to introduce many-body corrections
on top of two-body parametrizations, while excellent transferability to
chemical environments with deviating energetics could be demonstrated.
