Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose, and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water, and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs. It provides analytical stress tensor calculations and interfaces to both the atomic simulation environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized, which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials.

中文翻译：

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PiNN：一个用于构建分子和材料的原子神经网络的Python库。

原子神经网络（ANN）构成了一类机器学习方法，用于预测分子和材料的势能面以及理化性质。尽管取得了许多成功，但是开发可解释的ANN架构并有效地实现现有的ANN架构仍然具有挑战性。这要求可靠，通用和开源代码。在这里，我们提出了一个名为PiNN的python库，作为实现此目标的解决方案。在PiNN中，我们设计了一个新的可解释和高性能的图卷积神经网络变体PiNet，并实现了已建立的Behler-Parrinello神经网络。使用隔离的小分子，结晶物质，液态水和碱性水溶液的数据集测试了这些实现。PiNN带有一个名为PiNNBoard的可视化工具，用于提取ANN“学习”的化学见解。它提供了分析应力张量计算，并与原子模拟环境和Amsterdam Modeling Suite的开发版本建立了接口。而且，PiNN是高度模块化的，这使其不仅可以作为独立程序包使用，还可以作为开发和实施新颖ANN的工具链使用。该代码在许可的BSD许可下分发，可在https://github.com/Teoroo-CMC/PiNN/上免费获得，并提供完整的文档和教程。这不仅使它作为一个独立的程序包，而且作为开发和实现新颖的人工神经网络的工具链有用。该代码在许可的BSD许可下分发，可在https://github.com/Teoroo-CMC/PiNN/上免费获得，并提供完整的文档和教程。这不仅使它作为一个独立的程序包，而且作为开发和实现新颖的人工神经网络的工具链有用。该代码在许可的BSD许可下分发，可在https://github.com/Teoroo-CMC/PiNN/上免费获得，并提供完整的文档和教程。