pbc.dft

The ciderpress.pyscf.pbc.dft module serves the same purpose as ciderpress.pyscf.dft but for periodic systems. This module’s version of make_cider_calc() modifies a pbc Kohn Sham DFT object from PySCF to evaluate a CIDER functional. Currently only semilocal and SDMX features are supported.

NOTE: This module is particularly experimental. It is provided for the purpose of reproducing previous work that used this module (i.e. Bystrom et al.[1]) and is not as close to production readiness as other parts of the code.

ciderpress.pyscf.pbc.dft.make_cider_calc(ks, mlfunc, xmix=1.0, xc=None, xkernel=None, ckernel=None, mlfunc_format=None, nlc_coeff=None, nldf_init=None, sdmx_init=None, dense_mesh=None, rhocut=None)

Same as ciderpress.pyscf.dft.make_cider_calc(), but for periodic systems. Note that only semilocal and SDMX features are supported. The ks object must use a uniform XC integration grid and pseudopotentials.

[1]

Kyle Bystrom, Stefano Falletta, and Boris Kozinsky. Training machine-learned density functionals on band gaps. Journal of Chemical Theory and Computation, 20(17):7516–7532, 2024. PMID: 39178337. URL: https://doi.org/10.1021/acs.jctc.4c00999, arXiv:https://doi.org/10.1021/acs.jctc.4c00999, doi:10.1021/acs.jctc.4c00999.