About ↑
Q-stack is a stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). It is a work in progress. Stay tuned for updates!
This sub-part of Q-stack contains a few geometry-only descriptors, which are the following ones:
-
$B^2R^2$ (https://github.com/lcmd-epfl/b2r2-reaction-rep) $\mathrm{SLATM}_d$
Install ↑
The installation of this sub-part can be done executing the following command:
python -m pip install git+https://github.com/lcmd-epfl/Q-stack.git#subdirectory=qstack/qstack-qml
Examples ↑
Q-stack comes with several example codes that illustrate some of its key capabilities. Examples for firs sub-part will follow shortly.
References ↑
- P. van Gerwen, A. Fabrizio, M. Wodrich, and C. Corminboeuf,
“Physics-based representations for machine learning properties of chemical reactions”,
Mach. Learn.: Sci. Technol. 3, 045005 (2022)
Acknowledgements ↑
The authors of Q-stack acknowledge the National Centre of Competence in Research (NCCR) "Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL)" of the Swiss National Science Foundation (SNSF, grant number 182892) and the European Research Council (ERC, grant agreement no 817977).
