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arXiv:2212.05517 (physics)
[Submitted on 11 Dec 2022 ]

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

Title: SchNetPack 2.0:原子机器学习的神经网络工具包

Authors:Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
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.
Abstract: SchNetPack 是一个通用的神经网络工具箱,既满足了原子机器学习的方法开发需求,也满足了其应用需求。 版本 2.0 配备了改进的数据处理流程、等变神经网络模块以及分子动力学的 PyTorch 实现。 与 PyTorch Lightning 和 Hydra 配置框架的可选集成增强了灵活的命令行界面。 这使得 SchNetPack 2.0 可轻松通过自定义代码进行扩展,并准备好执行复杂的训练任务,例如生成 3D 分子结构。
Subjects: Chemical Physics (physics.chem-ph) ; Machine Learning (stat.ML)
Cite as: arXiv:2212.05517 [physics.chem-ph]
  (or arXiv:2212.05517v1 [physics.chem-ph] for this version)
  https://doi.org/10.48550/arXiv.2212.05517
arXiv-issued DOI via DataCite
Related DOI: https://doi.org/10.1063/5.0138367
DOI(s) linking to related resources

Submission history

From: Kristof Schütt [view email]
[v1] Sun, 11 Dec 2022 14:44:56 UTC (1,050 KB)
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