How to cite?

The PyMolDis code is made openly available at https://github.com/moldis-group/pymoldis with an MIT license. We welcome you to include it in your work. In return, we kindly ask you to cite the underlying entry in your work using PyMolDis.

Raghunathan Ramakrishnan (2024) “pymoldis: A Python suite for Molecular Discovery using Quantum Chemistry Big Data” https://moldis-group.github.io/pymoldis/

Bibtex entry

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Contributors

The program is developed by the following members of the Theory Lab at the Tata Institute of Fundamental Research Hyderabad, India

• Surajit Das
• Raghunathan Ramakrishnan

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Related publications

Resilience of Hund’s rule in the chemical space of small organic molecules. Majumdar, A., & Ramakrishnan, R. (2024). Physical Chemistry Chemical Physics, 26(20), 14505-14513.

Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules. Tripathy, S., Das, S., Jindal, S., & Ramakrishnan, R. (2024). arXiv preprint arXiv:2405.20033.