论文标题
Schnetpack 2.0：用于原子机器学习的神经网络工具箱
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
论文作者
论文摘要
Schnetpack是一种多功能的神经网络工具箱，可以解决方法开发和原子机器学习应用的要求。 2.0版带有改进的数据管道，模块的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为灵活的命令行界面提供动力。这使Schnetpack 2.0轻松地使用自定义代码扩展，并准备好进行复杂的训练任务，例如生成3D分子结构。
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.
