期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 15, 期 1, 页码 448-455出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.8b00908
关键词
-
资金
- Federal Ministry of Education and Research (BMBF) [01IS14013A]
- European Unions Horizon 2020 Research and Innovation Program, under the Marie Sklodowska-Curie Grant [792572]
- BK21 program - Korean National Research Foundation [2012-005741]
- Institute for Information & Communications Technology Promotion
- Korea government (MSIT) [2017-0-00451, 2017-0-01779]
- European Research Council (ERC-CoG grant BeStMo)
SchNetPack is a toolbox for the development and application of deep neural networks that predict 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) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据