pymoldis ✨

┏┓  ┳┳┓  ┓┳┓• 
┃┃┓┏┃┃┃┏┓┃┃┃┓┏
┣┛┗┫┛ ┗┗┛┗┻┛┗┛
   ┛                                                                                                         

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

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 [email protected]:moldis-group/pymoldis.git
    pip3 install -e pymoldis

• Install from PyPI

   pip3 install pymoldis

Tutorial

Data-mining S1-T1 energies of bigQM7w dataset

If you want to try a simple query, try the following

   import pymoldis

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

which will return some statistics for the S1/T1 energies and the f01 oscillator strength calculated with TDDFT and ADC(2) methods.

To learn about more advanced queries, please go through the SI of our paper https://arxiv.org/abs/2402.13801. The corresponding tutorial Jupyter notebooks are here: tutorial_ipynb_bigqm7w_S1T1

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
Phys. Chem. Chem. Phys. 26 (2024) 14505-14513.

---

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/

---

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}
}

Revision Notes

• 27 April 2024: We have updated the values of S1/T1 energies and the f01 oscillator strength at the SCS-PBE-QIDH/def2-TZVP levels for a few molecules. This revision was made because Orca 5.0.4 does not always print the excitation energies (S1 and T1) in ascending order. This change has a negligible effect on the overall statistics of the TDDFT results. We calculated TDDFT spectra with twelve eigenvalues and sorted six singlet and six triplet energies separately to extract S1 (lowest excited singlet) and T1 (lowest excited triplet) TD-DFT excitation energies. All other data, such as structures and ADC(2) results, remain the same as in our first database release on 15 February 2024. We have also updated the tutorial notebooks accordingly.