PANNA 2.0: Efficient neural network interatomic potentials and new architectures
Franco Pellegrini, Ruggero Lot, Yusuf Shaidu, Emine Küçükbenli
Abstract
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 graphics processing unit 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.