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（first principles）的材料属性预测通常是一项计算成本高昂的任务。近年来，人工神经网络与其他机器学习方法已成功借助现有示例数据，以较低的计算开销构建出高精度模型。本文介绍一款命名为"人工神经网络架构属性预测（Properties from Artificial Neural Network Architectures，缩写PANNA）"的软件包，该工具包可为遵循贝勒-帕里内罗（Behler–Parrinello）拓扑结构的原子体系神经网络模型构建提供完整的综合性工具包。除神经网络训练的核心流程外，该工具包还涵盖数据解析器、针对贝勒-帕里内罗类对称函数的描述符构建器，以及可集成至分子动力学软件包的力场生成器。PANNA支持多种激活函数与代价函数、正则化方法，同时支持为不同原子种类自定义尺寸的全连接神经网络。该软件依托底层TensorFlow引擎的优化特性与硬件适配灵活性，可在多类CPU、GPU、TPU系统上运行，能够基于大规模数据集开发并优化神经网络模型。