PANNA: Properties from Artificial Neural Network Architectures

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.

基于第一性原理对材料性质的预测通常是一项计算成本高昂的任务。近年来，通过利用现有示例数据，人工神经网络及其他机器学习方法已成功应用于以低计算成本获得精确模型。在此，我们推出名为“基于人工神经网络架构的材料性质”的软件包（PANNA），它提供了一套全面的工具集，用于构建遵循Behler–Parrinello拓扑的原子系统的神经网络模型。除神经网络训练的核心程序外，还包括数据解析器、适用于Behler–Parrinello类对称函数的描述符构建器和适用于分子动力学软件集成的力场生成器。PANNA提供了多种激活函数和成本函数、正则化方法，以及使用全连接网络并针对每种原子种类定制网络大小的可能性。得益于底层TensorFlow引擎的优化和硬件灵活性，PANNA可在多个CPU/GPU/TPU系统上使用，从而使得基于大型数据集的开发和优化神经网络模型成为可能。