PyCDFT

PyCDFT performs constrained density functional theory (CDFT) calculations to compute diabatic states and electronic couplings by adding constraint potentials to the Kohn–Sham Hamiltonian.


Key Features:

  • Object-oriented framework: Provides an object-oriented and customizable Python framework for implementing CDFT workflows.
  • Constrained DFT (CDFT): Generates diabatic states using density-based constraints applied during electronic structure calculations.
  • Self-consistent-field (SCF) calculations: Supports single-point SCF calculations with applied constraints.
  • Geometry optimizations: Performs constrained geometry optimizations of electronic states.
  • Constraint incorporation: Incorporates constraint potentials directly into the Kohn–Sham Hamiltonian of underlying DFT codes.
  • First-principles MD interfacing: Interfaces with first-principles molecular dynamics codes, explicitly demonstrated with Qbox.
  • Parallel scalability: Designed for use in large-scale parallel computations with external DFT/MD codes.
  • Benchmark validation: Validated by benchmarking electronic couplings between diabatic states across various organic molecules, producing results consistent with established CDFT implementations.

Scientific Applications:

  • Diabatic state generation: Construction and analysis of diabatic states for charge-transfer and nonadiabatic studies.
  • Electronic coupling calculation: Computation of electronic couplings between diabatic states in organic molecules.
  • Constrained geometry studies: Exploration of potential energy surfaces and optimized geometries under electronic constraints.
  • First-principles molecular dynamics: Integration with MD codes such as Qbox to study dynamical behavior of constrained electronic states.
  • Quantum mechanical characterization: Investigation of quantum mechanical properties of molecular systems using CDFT.

Methodology:

PyCDFT adds constraint potentials to the Kohn–Sham Hamiltonian and performs self-consistent-field calculations and constrained geometry optimizations, interfaces with external DFT and first-principles MD codes (e.g., Qbox), and has been benchmarked via electronic coupling calculations between diabatic states in organic molecules.

Topics

Details

Programming Languages:
Python
Added:
1/18/2021
Last Updated:
1/30/2021

Operations

Publications

Ma H, Wang W, Kim S, Cheng M, Govoni M, Galli G. <scp>PyCDFT</scp>: A Python package for constrained density functional theory. Journal of Computational Chemistry. 2020;41(20):1859-1867. doi:10.1002/jcc.26354. PMID:32497321.

PMID: 32497321
Funding: - National Science Foundation: CHE‐1764399 - U.S. Department of Energy: DE‐SC0012405