CiderPress: Machine Learned Exchange-Correlation Functionals

CiderPress provides tools for training and evaluating CIDER functionals for use in Density Functional Theory calculations. Interfaces to the GPAW and PySCF codes are included.

Please see the CiderPress website for installation instructions and documentation.

Questions and Comments

Find a bug? Areas of code unclearly documented? Other questions? Feel free to contact Kyle Bystrom at [email protected] AND/OR create an issue on the Github page.

Citing

If you find CiderPress or CIDER functionals useful in your research, please cite the following article

@article{PhysRevB.110.075130,
  title = {Nonlocal machine-learned exchange functional for molecules and solids},
  author = {Bystrom, Kyle and Kozinsky, Boris},
  journal = {Phys. Rev. B},
  volume = {110},
  issue = {7},
  pages = {075130},
  numpages = {30},
  year = {2024},
  month = {Aug},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevB.110.075130},
  url = {https://link.aps.org/doi/10.1103/PhysRevB.110.075130}
}

The above article introduces the CIDER23X functionals and much of the algorithms in CiderPress. If you use the CIDER24X functionals, please also cite

@article{doi:10.1021/acs.jctc.4c00999,
  author = {Bystrom, Kyle and Falletta, Stefano and Kozinsky, Boris},
  title = {Training Machine-Learned Density Functionals on Band Gaps},
  journal = {Journal of Chemical Theory and Computation},
  volume = {20},
  number = {17},
  pages = {7516-7532},
  year = {2024},
  doi = {10.1021/acs.jctc.4c00999},
  note ={PMID: 39178337},
  URL = {https://doi.org/10.1021/acs.jctc.4c00999},
  eprint = {https://doi.org/10.1021/acs.jctc.4c00999}
}