QML
Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy
| Coordinator | University of Basel |
| Grant period | 2018-06-01 - 2024-05-31 |
| Funding body | European Union |
| Call number | ERC-2017-COG |
| Grant number | 772834 |
| Identifier | G:(EU-Grant)772834 |
Note: Large and diverse property data sets of relaxed molecules and crystals, resulting from computationally demanding quantum calculations, have recently been used to train machine learning models of various energetic and electronic properties. We propose to advance these techniques to a level where they can also describe reaction profiles, i.e. reactive non-equilibrium processes which traditionally would require quantum chemistry treatment. The resulting quantum machine learning (QML) models will provide reaction profiles for new reactants in real-time and with quantum accuracy. The overall goal is to develop a predictive computational tool which allows chemists to easily optimize reaction conditions, develop new catalysts, or even plan new synthetic pathways.
Recent Publications
All known publications ...
Download: BibTeX | EndNote XML, Text | RIS |
http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
Preprint
Kilaj, A. ; Käser, S. ; Wang, J.CFEL*Extern*DESY* ; Straňák, P. ; Schwilk, M. ; Xu, L. ; von Lilienfeld, O. A. ; Küpper, J. (Corresponding author)CFEL*XFEL.EU*DESY* ; Meuwly, M. (Corresponding author) ; Willitsch, S. (Corresponding author)
Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions
[10.3204/PUBDB-2024-01157]
Files Fulltext by arXiv.org
BibTeX |
EndNote:
XML,
Text |
RIS
All known publications ...
Download: BibTeX | EndNote XML, Text | RIS |
guest ::
