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We present the latest release of PANNA 2.0 (Properties from Artificial NeuralNetwork Architectures), a code for the generation of neural network interatomicpotentials based on local atomic descriptors and multilayer perceptrons. Builton a new back end, this new release of PANNA features improved tools forcustomizing and monitoring network training, better GPU support including afast descriptor calculator, new plugins for external codes and a newarchitecture for the inclusion of long-range electrostatic interactions througha variational charge equilibration scheme. We present an overview of the mainfeatures of the new code, and several benchmarks comparing the accuracy ofPANNA models to the state of the art, on commonly used benchmarks as well asricher datasets.

PANNA 2.0: Efficient neural network interatomic potentials and new architectures