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Machine Learning for Catalytic Carbon Dioxide Activation

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Publications

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Author(s): Gebauer, Niklas W. A.; Gastegger, Michael; Schütt, Kristof T.
Published in: Advances in Neural Information Processing Systems, Issue 32, 2019, Page(s) 7566-7578

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Author(s): K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, R. J. Maurer
Published in: Nature Communications, Issue 10/1, 2019, Page(s) 5024, ISSN 2041-1723
DOI: 10.1038/s41467-019-12875-2

Machine learning enables long time scale molecular photodynamics simulations

Author(s): Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand
Published in: Chemical Science, Issue 10/35, 2019, Page(s) 8100-8107, ISSN 2041-6520
DOI: 10.1039/c9sc01742a

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Author(s): Julia Westermayr, Michael Gastegger, Philipp Marquetand
Published in: The Journal of Physical Chemistry Letters, Issue 11/10, 2020, Page(s) 3828-3834, ISSN 1948-7185
DOI: 10.1021/acs.jpclett.0c00527

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Author(s): K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, K.-R. Müller
Published in: Journal of Chemical Theory and Computation, Issue 15/1, 2018, Page(s) 448-455, ISSN 1549-9618
DOI: 10.1021/acs.jctc.8b00908

Molecular Dynamics with Neural Network Potentials

Author(s): Michael Gastegger, Philipp Marquetand
Published in: Machine Learning Meets Quantum Physics, Issue 968, 2020, Page(s) 233-252
DOI: 10.1007/978-3-030-40245-7_12

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Author(s): Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
Published in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Issue 11700, 2019, Page(s) 311-330
DOI: 10.1007/978-3-030-28954-6_17