Welcome to SpaiNN’s documentation!
spaiNN is a Python package that provides a flexible and efficient interface to the SchNetPack 2.0 package 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. spaiNN allows users to predict energies, forces, dipoles, and non-adiabatic couplings for multiple electronic states, and additionally provides an interface to the SHARC (Surface Hopping including Arbitrary Couplings) software for running excited-state dynamics simulations. spaiNN is an extension to the SchNarc [1] software, i.e., a python software that combines SchNetPack 1.0 [2-4] and SHARC.
It offers a simple and intuitive python and command line API.
Features
Predict potential energy surfaces of multiple electronic states (SchNet [1-4])
Predict vector-properties of multiple electronic states, such as non-adiabatic couplings or dipole moments (SchNet [1-4], PaiNN [5])
Interface to the SHARC software for running excited state dynamics simulations
Flexible implementation in Python
Check out the usage section for further information, including how to Installation the project.
Note
This project is under active development.
Contents
Get Started
Tutorials
References
[1] J. Westermayr, M. Gastegger, P. Marquetand, Phys. Chem. Lett. 2020, 11, 10, 3828–3834, 10.1021/acs.jpclett.0c00527
[2] K.T. Schütt. F. Arbabzadah. S. Chmiela, K.-R. Müller, A. Tkatchenko, Nat. Comm. 2017, 8, 13890, 10.1038/ncomms13890
[3] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller, Advances in Neural Information Processing Systems 2017, 30, 992-1002, Paper
[4] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller, J. Chem. Phys. 2018, 148, 24, 241722, 10.1063/1.5019779
[5] K. T. Schütt, O. T. Unke, M. Gastegger, Proceedings of the 38th International Conference on Machine Learning 2021, PMLR 139:9377-9388, Paper