  Artificial neural networks (NNs) are one of the most frequently used machine
learning approaches to construct interatomic potentials and enable efficient
large-scale atomistic simulations with almost ab initio accuracy. However, the
simultaneous training of NNs on energies and forces, which are a prerequisite
for, e.g., molecular dynamics simulations, can be demanding. In this work, we
present an improved NN architecture based on the previous GM-NN model [V.
Zaverkin and J. K\"astner, J. Chem. Theory Comput. 16, 5410-5421 (2020)], which
shows an improved prediction accuracy and considerably reduced training times.
Moreover, we extend the applicability of Gaussian moment-based interatomic
potentials to periodic systems and demonstrate the overall excellent
transferability and robustness of the respective models. The fast training by
the improved methodology is a pre-requisite for training-heavy workflows such
as active learning or learning-on-the-fly.
