In this paper, we present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT). PyCDFT provides an object-oriented, customizable implementation of CDFT, and allows for both single-point self-consistent-field calculations and geometry optimizations. PyCDFT is designed to interface with existing density functional theory (DFT) codes to perform CDFT calculations where constraint potentials are added to the Kohn–Sham Hamiltonian. Here, we demonstrate the use of PyCDFT by performing calculations with a massively parallel first-principles molecular dynamics code, Qbox, and we benchmark its accuracy by computing the electronic coupling between diabatic states for a set of organic molecules. We show that PyCDFT yields results in agreement with existing implementations and is a robust and flexible package for performing CDFT calculations. The program is available at https://dx.doi.org/10.5281/zenodo.3821097.
Ma, He, et al. "PyCDFT: A Python package for constrained density functional theory." Journal of Computational Chemistry, vol. 41, no. 20, Jun. 2020. https://doi.org/10.1002/jcc.26354
Ma, He, Wang, Wennie, Kim, Siyoung, et al., "PyCDFT: A Python package for constrained density functional theory," Journal of Computational Chemistry 41, no. 20 (2020), https://doi.org/10.1002/jcc.26354
@article{osti_1658833,
author = {Ma, He and Wang, Wennie and Kim, Siyoung and Cheng, Man‐Hin and Govoni, Marco and Galli, Giulia},
title = {PyCDFT: A Python package for constrained density functional theory},
annote = {In this paper, we present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT). PyCDFT provides an object-oriented, customizable implementation of CDFT, and allows for both single-point self-consistent-field calculations and geometry optimizations. PyCDFT is designed to interface with existing density functional theory (DFT) codes to perform CDFT calculations where constraint potentials are added to the Kohn–Sham Hamiltonian. Here, we demonstrate the use of PyCDFT by performing calculations with a massively parallel first-principles molecular dynamics code, Qbox, and we benchmark its accuracy by computing the electronic coupling between diabatic states for a set of organic molecules. We show that PyCDFT yields results in agreement with existing implementations and is a robust and flexible package for performing CDFT calculations. The program is available at https://dx.doi.org/10.5281/zenodo.3821097.},
doi = {10.1002/jcc.26354},
url = {https://www.osti.gov/biblio/1658833},
journal = {Journal of Computational Chemistry},
issn = {ISSN 0192-8651},
number = {20},
volume = {41},
place = {United States},
publisher = {Wiley},
year = {2020},
month = {06}}