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Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller, Shinichi Nakajima, Stefan Gugler, Niklas Wolf Andreas Gebauer
Molecular relaxation by reverse diffusion with time step prediction
Kim Andrea Nicoli, Christopher Anders, Lena Funcke, Karl Jansen, Shinichi Nakajima
NeuLat: a toolbox for neural samplers in lattice field theories
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, Adil Kabylda, Huziel E Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
Accurate global machine learning force fields for molecules with hundreds of atoms
Philipp Keyl, Philip Bischoff, Gabriel Dernbach, Michael Bockmayr, Rebecca Fritz, David Horst, Nils Blüthgen, Gregoire Montavon, Klaus-Robert Müller, Frederick Klauschen et al.
Single-cell gene regulatory network prediction by explainable AI
Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Lorenz Vaitl, Kim Andrea Nicoli, Shinichi Nakajima, Pan Kessel
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL divergence for Normalizing Flows
Adil Kabylda, Valentin Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, Alexandre Tkatchenko
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
Huziel E. Sauceda, Luis E. Galvez-Gonzalez, Stefan Chmiela, Lauro Oliver Paz-Borbon, Klaus-Robert Müller, Alexandre Tkatchenko
BIGDML— Towards accurate quantum machine learning force fields for materials
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko