PANNA: Properties from Artificial Neural Network Architectures

Abstract

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.

arXiv commentSource code: https://gitlab.com/PANNAdevs/panna

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• Data-driven simulation and characterisation of gold nanoparticle melting
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
• Efficient implementation of atom-density representations
• Developments and Further Applications of Ephemeral Data Derived Potentials
• KLIFF: A framework to develop physics-based and machine learning interatomic potentials
• A collinear-spin machine learned interatomic potential for Fe${}_7$Cr${}_2$Ni alloy
• Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water
• Fortnet, a software package for training Behler-Parrinello neural networks
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