18986

PANNA: Properties from Artificial Neural Network Architectures

Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli
SISSA, Via Bonomea 265, I-34136 Trieste, Italy
arXiv:1907.03055 [physics.comp-ph], (6 Jul 2019)

@misc{lot2019panna,

   title={PANNA: Properties from Artificial Neural Network Architectures},

   author={Lot, Ruggero and Pellegrini, Franco and Shaidu, Yusuf and Kucukbenli, Emine},

   year={2019},

   eprint={1907.03055},

   archivePrefix={arXiv},

   primaryClass={physics.comp-ph}

}

Prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computational cost by leveraging existing example data. Here, we present a software package "Properties from Artificial Neural Network Architectures" (PANNA) that provides a comprehensive toolkit for creating neural network models for atomistic systems. Besides the core routines for neural network training, it includes data parser, descriptor builder and force-field generator suitable for integration within molecular dynamics packages. PANNA offers a variety of activation and cost functions, regularization methods, as well as the possibility of using fully-connected networks with custom size for each atomic species. PANNA benefits from the optimization and hardware-flexibility of the underlying TensorFlow engine which allows it to be used on multiple CPU/GPU/TPU systems, making it possible to develop and optimize neural network models based on large datasets.
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