pymoldis ✨

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A Python suite for data-mining the Quantum Chemistry Big Data developed through the MolDis project (https://moldis.tifrh.res.in/)
Support e-mail: ramakrishnan@tifrh.res.in

Install pymoldis

• Install dependencies numpy, pandas

• Additionally, if you want to convert a SMILES string to an SVG image as in query10.ipynb, install rdkit

• Download and install the package

    git clone git@github.com:moldis-group/pymoldis.git
    pip3 install -e pymoldis

• Install from PyPI

   pip3 install pymoldis

Tutorial

Data-mining S1-T1 energies of bigQM7w dataset

The tutorial Jupyter notebooks are here: tutorial_ipynb_bigqm7w_S1T1

Or, if you want to try a simple query, try the following

   import pymoldis

   df=pymoldis.get_data('bigqm7w_S1T1')
   df.describe()

Specific References

Singlet-Triplet energies of 13k molecules in the bigQM7w dataset

Resilience of Hund's rule in the Chemical Space of Small Organic Molecules
Atreyee Majumdar, Raghunathan Ramakrishnan
https://arxiv.org/abs/2402.13801 (2024)

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wB97XD/def2-TZVP dataset with 13k molecules upto 7 atoms of C/O/N/F

The Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra
Prakriti Kayastha, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
Digital Discovery, 1 (2022) 689-702.

• bigQM7w dataset with DFT/TDDFT properties: https://moldis-group.github.io/bigQM7w/

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Citing

R Ramakrishnan (2024) "pymoldis: A Python suite for Molecular Discovery with Quantum Chemistry Big Data" https://github.com/moldis-group/pymoldis

bibtex entry

@misc{ramakrishnan2024pymoldis,
  title   = {pymoldis: A Python suite for Molecular Discovery with Quantum Chemistry Big Data},
  author  = {Ramakrishnan, Raghunathan},
  url = {https://github.com/moldis-group/pymoldis},
  year    = {2024}
}