We offer pre-trained models that can be used to replicate our experiments in the original GDML 1 and sGDML 2 publications. Pre-trained models for the recently released MD22 3 benchmark dataset are also available.
They can be downloaded directly from the command line, using:
$ sgdml-get model
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R. (2017). Machine Learning of Accurate Energy-conserving Molecular Force Fields. Sci. Adv., 3(5), e1603015.
Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A. (2018). Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields. Nat. Commun., 9(1), 3887.
Chmiela, S., Vassilev-Galindo, V., Unke, O. T., Kabylda, A., Sauceda, H. E., Tkatchenko, A., Müller, K.-R. (2023). Accurate Global Machine Learning Force Fields for Molecules With Hundreds of Atoms. Sci. Adv., 9, eadf0873.