Citing

If you use mkite in your project, please consider citing the publication describing the software:

@article{mkite2023,
  title = {mkite: A distributed computing platform for high-throughput materials simulations},
  author = {Schwalbe-Koda, Daniel},
  year = {2023},
  journal = {arXiv:2301.08841},
  doi = {10.48550/arXiv.2301.08841},
  url = {https://doi.org/10.48550/arXiv.2301.08841},
  arxiv={2301.08841},
}

Other packages

The mkite suite builds on other packages in the community. Depending on your application, consider citing other packages that make atomistic simulation more efficient:

• A. H. Larsen et al. “The Atomic Simulation Environment - A Python library for working with atoms” J. Phys.: Condens. Matter 29, 273002 (2017).

• S. P. Ong et al. “Python Materials Genomics (pymatgen): A Robust, Open-Source Python Library for Materials Analysis.” Computational Materials Science 68, 314-319 (2013).

• 7. Landrum. “RDKit: Open-source cheminformatics.” https://www.rdkit.org

The mkite package draws inspiration from other workflow tools in the community, especially:

• J. Luc Peterson et al. “Enabling machine learning-ready HPC ensembles with Merlin.” Future Generation Computer Systems 131, 255-268 (2022)

• S. P. Huber et al. “AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance.” Sci. Data 7, 300 (2020)

• A. Jain et al. “FireWorks: a dynamic workflow system designed for high-throughput applications.” Concurrency Computat.: Pract. Exper. 27, 5037–5059 (2015)

• J. Janssen et al. “pyiron: An integrated development environment for computational materials science.” Comp. Mater. Sci. 163, 24 (2019)