SchNetPack: A Deep Learning Toolbox For Atomistic Systems

TL;DR

SchNetPack is a comprehensive deep learning toolbox designed for predicting quantum-chemical properties of molecules and materials, facilitating easy model development, training, and application on large datasets with GPU support.

Contribution

It introduces a flexible, PyTorch-based toolkit with built-in atomistic neural network components, benchmark datasets, and integration with existing simulation environments.

Findings

Enables efficient training on large datasets with millions of calculations.

Provides ready-to-use implementations of atom-centered symmetry functions and SchNet.

Facilitates easy application of neural networks to quantum-chemical property prediction.

Abstract

SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atomcentered symmetry functions and the deep tensor neural network SchNet as well as ready-to-use scripts that allow to train these models on molecule and material datasets. Based upon the PyTorch deep learning framework, SchNetPack allows to efficiently apply the neural networks to large datasets with millions of reference calculations as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.