Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning
- Author(s)
- Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- Vector Institute for Artificial Intelligence, University of Toronto, Technische Universität Berlin
- Journal
- Journal of Chemical Physics
- Volume
- 157
- No. of pages
- 8
- ISSN
- 0021-9606
- DOI
- https://doi.org/10.1063/5.0112856
- Publication date
- 12-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102019 Machine learning, 103006 Chemical physics
- Keywords
- ASJC Scopus subject areas
- Physics and Astronomy(all), Physical and Theoretical Chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/transition-state-search-and-geometry-relaxation-throughout-chemical-compound-space-with-quantum-machine-learning(dee5f062-71d1-4cdb-80a3-e6f29aa0d906).html