# SchNetPack 2.0: A neural network toolbox for atomistic machine learning

Source: [SchNetPack 2.0: A neural network toolbox for atomistic machine learning](<https://www.sota2.com/research/paper/68fa44151e209131ce90e8c4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T04:36:55.870Z

Authors: Kristof T. Sch\\"utt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger

Published: 2022-12-11T14:44:56.000Z

## Abstract

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Potential energy prediction on Paracetamol](<https://www.sota2.com/research/sota/potential-energy-prediction-on-paracetamol.md>): Potential energy prediction; Paracetamol; MAE: 1.4.

- [Potential Energy Prediction on Aspirin](<https://www.sota2.com/research/sota/potential-energy-prediction-on-aspirin.md>): Potential energy prediction; Aspirin; MAE: 2.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.05517>)

[PDF](<https://arxiv.org/pdf/2212.05517.pdf>)