pedersor / cond_prob_dft

conditional probability density functional theory with examples

Conditional Probability Density Functional Theory (CP-DFT)

Overview

CP-DFT is a DFT method that determines the ground-state energy of a system by finding the conditional probability (CP) density from a series of independent Kohn-Sham (KS) DFT calculations. By directly calculating CP densities, and thus the exchange-correlation (XC) hole, we bypass the need for an approximate XC energy functional.

This library contains code to reproduce the 1-dimensional (1D) examples presented in the following work:

Ryan Pederson, Jielun Chen, Steven R. White, and Kieron Burke. "Conditional probability density functional theory." Phys. Rev. B 105, 245138 (2022) arXiv:2203.09647

Installation (Linux)

• First install the DFT_1D module and dependencies:

$ git clone https://github.com/pedersor/DFT_1d.git
$ cd DFT_1d
$ pip install .

• Next, in a different directory, install the conditional probability DFT module

$ git clone https://github.com/pedersor/cond_prob_dft_1d.git
$ cd cond_prob_dft_1d
$ pip install .

Examples

The example figures in the paper can be generated by the following code in 1d_examples/, e.g., the 1D Be atom figures are obtained by executing

$ cd 1d_examples/1d_be_fig/
$ python3 fig.py

Other examples are generated similarly. The file exact_pair_density.dat is obtained from DMRG results using an identical 1D grid as the CP-DFT calculations.

Disclaimer

This library is in alpha.