PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Abstract

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical 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 high-dimensional 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, 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.

References in corpus (8)

• The NumPy array: a structure for efficient numerical computation
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine Learning Unifies the Modelling of Materials and Molecules
• How van der Waals interactions determine the unique properties of water
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Coupling of surface chemistry and electric double layer at TiO${}_2$ electrochemical interfaces
• High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium

Cited by in corpus (1)

• Predicting tensorial molecular properties with equivariant machine-learning models