qmlspectrum

qmlspectrum is a small test-suite that uses qml package for modeling spectra as continuous functions. In this repository, we also distribute suitable datasets suitable for spectral modeling. Example input scripts collected in example_scripts show how to use the qmlspectrum test-suite.

Status

We are developing new content through collaborative efforts which will soon be collected here.

Installation

qmlspectrum can be installed using the Python package manager pip3

pip3 install qmlspectrum --user

Dependencies

matplitlib, pandas, scipy, numpy, os, qml all of which can be installed using the Python package manager pip3

Contributors

Prakriti Kayastha
Arpan Chaudury
Sabysachi Chakraborty
Debashree Ghosh
Raghunathan Ramakrishnan

Development

This test-suite is developed by Raghunathan Ramakrishnan and maintained at https://github.com/raghurama123/qmlspectrum/ and https://pypi.org/project/qmlspectrum/

Citation

If you are using the program and the bigQM7ω dataset distributed here, please consider citing the following article and the QML code.

bigQM7ω dataset and full-spectrum modeling

Quantum Machine Learning Transition Probabilities in Electronic Excitation Spectra across Chemical Space: The Resolution-vs.-Accuracy Dilemma
Prakriti Kayastha, Sabyasachi Chakraborty, Raghunathan Ramakrishnan (2022)

@article{kayastha2022quantum,
  title={Quantum Machine Learning Transition Probabilities in Electronic Excitation 
  Spectra across Chemical Space: The Resolution-vs.-Accuracy Dilemma},
  author={Kayastha, Prakriti and Chakraborty, Sabyasachi and Ramakrishnan, Raghunathan},
  journal={arXiv preprint arXiv:2110.11798},
  url={https://doi.org/10.48550/arXiv.2110.11798},
  year={2022}
}

QML, A Python Toolkit for Quantum Machine Learning

AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, KR Muller, OA von Lilienfeld (2017) "QML: A Python Toolkit for Quantum Machine Learning, https://github.com/qmlcode/qml"

@misc{christensenqml,
  title={QML: A Python Toolkit for Quantum Machine Learning, 2019},
  author={Christensen, Anders S and Bratholm, Lars A and Amabilino, Silvia and Kromann, Jimmy C 
  and Faber, Felix A and Huang, Bing and Tkatchenko, A and von Lilienfeld, OA}
  url={https://www.qmlcode.org/}
}

scchem2021/qmlspectrum