PANNA: Properties from Artificial Neural Network Architectures
Description
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 following the Behler–Parrinello topology. Besides the core routines for neural network training, it includes data parser, descriptor builder for Behler–Parrinello class of symmetry functions 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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References: [1] J. Behler and M. Parrinello, Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces, Phys. Rev. Lett. 98, 146401 (2007). [2] M. Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). URL: https://www.tensorflow.org [3] S. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J. Comp. Phys. 117 1-9 (1995). URL: http://lammps.sandia.gov [4] E. B. Tadmor, R. S. Elliott, J. P. Sethna, R. E. Miller and C. A. Becker, The Potential of Atomistic Simulations and the Knowledgebase of Interatomic Models, JOM, 63, 17 (2011). URL: https://openkim.org