Welcome to Qstack documentation

Q-stack

About

Q-stack is a stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).

Install

The installation of the library for python use can be done executing one of the following commands:

python -m pip install git+https://github.com/lcmd-epfl/Q-stack.git
python -m pip install "qstack @ git+https://github.com/lcmd-epfl/Q-stack.git"
python -m pip install "qstack[all] @ git+https://github.com/lcmd-epfl/Q-stack.git"

The last one is recommended if you do not know which features you wish to use, since they pull the dependencies required with all ‘optional’ parts of Q-stack.

Features:

feature	capability
[pyscf]	quantum-chemical computations, wrappers, and conversion tools
[qml]	stucture-based representations such as SLATM, SLATM_d, B2R2 (no quantum chemistry)
[spahm]	SPAHM (ε-SPAHM, SPAHM(a,b)) representations
[cell2mol]	interface with cell2mol
[equio]	interface with metatensor
[regression]	tools to run kernel ridge regression (no quantum chemistry)
[wigner]	tool to compute Wigner d-matrices for real spherical harmonics
[dev]	all above + linting + documentation building

If you want to be able to edit Q-stack’s code after installing, you need to download it yourself, for instance with this series of commands

git clone https://github.com/lcmd-epfl/Q-stack
cd Q-stack

#optionally, run the following line, if you want to be extra-careful about reproducibility, by installing a well-known version of all dependencies
python -m pip install -r requirements.py3.13.txt   # (or "requirements.py3.11.txt" if you have an older version of python and that first file doesn't work)

python -m pip install -e .[all]    # note: this translates as "install as '-e'ditable, install from this directory ('.'), with optional feature 'all'"

For the optional step above, we also have an environment.yml file available, if you prefer working with conda environments.

Examples

Q-stack comes with several example codes that illustrate some of its key capabilities. Check out the examples/ and tests/ folders:

• Field decomposition:

  • examples/example_deco.py

  • tests/test_fitting.py

• Computation of Hirshfeld charges:

  • examples/example_hirsh.py

• Basis set optimization:

  • examples/example_opt.py

• Generation of the ε-SPAHM representation:

  • examples/example_SPAHM.py

  • tests/test_spahm.py

  • tests/test_spahm_grad.py

• Generation of the SPAHM(a,b) representations:

  • tests/test_spahm_a.py

  • tests/test_spahm_b.py

  • tests/test_spahm_b_selected.py

• Reaction representations ($B^2R^2$ and $\mathrm{SLATM}_d$):

  • tests/test_rxn-repr.py

• Computation of DORI (density overlap regions indicator):

  • tests/test_dori.py

• Interface with cell2mol:

  • tests/test_c2mio.py

• Parsing ORCA binary output:

  • examples/example_orcaio.py

  • tests/test_orca.py

Sources

Relevant packages incorporated (among others):

• https://github.com/lcmd-epfl/azo-xcite-tools

• https://github.com/lcmd-epfl/SPAHM

• https://github.com/lcmd-epfl/SPAHM-RHO

• https://github.com/lcmd-epfl/RHO-Decomposition

• https://github.com/lcmd-epfl/OTPD-basis

• https://github.com/lcmd-epfl/b2r2-reaction-rep

• https://github.com/lcmd-epfl/ml-density

References

• A. Fabrizio, A. Grisafi, B. Meyer, M. Ceriotti, and C. Corminboeuf, “Electron density learning of non-covalent systems”, Chem. Sci. 10, 9492 (2019)

• A. Fabrizio, K. R. Briling, D. D. Girardier, and C. Corminboeuf, “Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation”, J. Chem. Phys. 153, 204111 (2020)

• S. Vela, A. Fabrizio, K. R. Briling, and C. Corminboeuf, “Machine-learning the transition density of the productive excited states of azo-dyes” J. Phys. Chem. Lett. 12, 5957–5962 (2021)

• K. R. Briling, A. Fabrizio, and C. Corminboeuf, “Impact of quantum-chemical metrics on the machine learning prediction of electron density”, J. Chem. Phys. 155, 024107 (2021)

• A. Fabrizio, K. R. Briling, and C. Corminboeuf, “SPAHM: the Spectrum of Approximated Hamiltonian Matrices representations”, Digital Discovery 1, 286–294 (2022)

• K. R. Briling, Y. Calvino Alonso, A. Fabrizio, and C. Corminboeuf, “SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations”, J. Chem. Theory Comput. 20, 1108–1117 (2024)

• 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 numbers 182892 and 205602),

• the NCCR “Sustainable chemical process through catalysis (Catalysis)” of the SNSF (grant numbers 180544 and 225147),

• the European Research Council (ERC, grant number 817977).