Machine Learning for Catalytic Carbon Dioxide Activation

Project Information

MachineCat

Grant agreement ID: 792572

DOI

10.3030/792572

Project closed

EC signature date 4 April 2018

Start date 1 June 2018

End date 31 May 2020

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 159 460,80

EU contribution

€ 159 460,80

Coordinated by

TECHNISCHE UNIVERSITAT BERLIN
Germany

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Conference proceedings (1)

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
Publisher: Curran Associates, Inc.

Peer reviewed articles (4)

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
Publisher: Nature Publishing Group
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
Publisher: Royal Society of Chemistry
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
Publisher: American Chemical Society
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
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.8b00908

Book chapters (2)

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, ISBN 978-3-030-40244-0
Publisher: Springer International Publishing
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, ISBN 978-3-030-28954-6
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-28954-6_17