Machine Learning for Catalytic Carbon Dioxide Activation

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

Autori: Gebauer, Niklas W. A.; Gastegger, Michael; Schütt, Kristof T.
Pubblicato in: Advances in Neural Information Processing Systems, Numero 32, 2019, Pagina/e 7566-7578
Editore: Curran Associates, Inc.

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

Autori: K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, R. J. Maurer
Pubblicato in: Nature Communications, Numero 10/1, 2019, Pagina/e 5024, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-019-12875-2

Machine learning enables long time scale molecular photodynamics simulations

Autori: Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand
Pubblicato in: Chemical Science, Numero 10/35, 2019, Pagina/e 8100-8107, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/c9sc01742a

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

Autori: Julia Westermayr, Michael Gastegger, Philipp Marquetand
Pubblicato in: The Journal of Physical Chemistry Letters, Numero 11/10, 2020, Pagina/e 3828-3834, ISSN 1948-7185
Editore: American Chemical Society
DOI: 10.1021/acs.jpclett.0c00527

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Autori: K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, K.-R. Müller
Pubblicato in: Journal of Chemical Theory and Computation, Numero 15/1, 2018, Pagina/e 448-455, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.8b00908

Molecular Dynamics with Neural Network Potentials

Autori: Michael Gastegger, Philipp Marquetand
Pubblicato in: Machine Learning Meets Quantum Physics, Numero 968, 2020, Pagina/e 233-252, ISBN 978-3-030-40244-0
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-40245-7_12

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Autori: Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
Pubblicato in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Numero 11700, 2019, Pagina/e 311-330, ISBN 978-3-030-28954-6
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-28954-6_17

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