PANNA 2.0: Efficient neural network interatomic potentials and new
  architectures
Franco Pellegrini Ruggero Lot Yusuf Shaidu Emine Küçükbenli
We present the latest release of PANNA 2.0 (Properties from Artificial Neural
Network Architectures), a code for the generation of neural network interatomic
potentials based on local atomic descriptors and multilayer perceptrons. Built
on a new back end, this new release of PANNA features improved tools for
customizing and monitoring network training, better GPU support including a
fast descriptor calculator, new plugins for external codes and a new
architecture for the inclusion of long-range electrostatic interactions through
a variational charge equilibration scheme. We present an overview of the main
features of the new code, and several benchmarks comparing the accuracy of
PANNA models to the state of the art, on commonly used benchmarks as well as
richer datasets.