Package to perform self-consistent mean-field calculations on tight-binding systems
Project description
MeanFi
What is MeanFi
?
MeanFi
is a Python package that performs self-consistent Hartree-Fock calculations on tight-binding models.
It aims to find the groundstate of a Hamiltonian with density-density interactions
$$ \hat{H} = \hat{H_0} + \hat{V} = \sum_{ij} h_{ij} c^\dagger_{i} c_{j} + \frac{1}{2} \sum_{ij} v_{ij} \hat{n}_i \hat{n}_j, $$
and computes the mean-field correction $\hat{V}_{\text{MF}}$ which approximates the interaction term:
$$ \hat{V} \approx \hat{V}{\text{MF}} = \sum{ij} \tilde{v}{ij} c^\dagger{i} c_{j}. $$
For more details, refer to the theory overview and algorithm description.
How to use MeanFi
?
The calculation of a mean-field Hamiltonian is a simple 3-step process:
-
Define
To specify the interacting problem, use a
Model
object which collects:- Non-interacting Hamiltonian as a tight-binding dictionary.
- Interaction Hamiltonian as a tight-binding dictionary.
- Particle filling number in the unit cell.
-
Guess
Construct a starting guess for the mean-field correction.
-
Solve
Solve for the mean-field correction using the
solver
function and add it to the non-interacting part to obtain the total mean-field Hamiltonian.
import meanfi
#Define
h_0 = {(0,) : onsite, (1,) : hopping, (-1,) : hopping.T.conj()}
h_int = {(0,) : onsite_interaction}
model = meanfi.Model(h_0, h_int, filling=2)
#Guess
guess = meanfi.guess_tb(guess_hopping_keys, ndof)
#Solve
mf_correction = meanfi.solver(model, guess)
h_mf = meanfi.add_tb(h_0, mf_correction)
For more details and examples on how to use the package, we refer to the tutorials.
Why MeanFi
?
Here is why you should use MeanFi
:
-
Simple
The workflow is straightforward. Interface with
Kwant
allows easy creation of complicated tight-binding systems and interactions. -
Extensible
MeanFi
's code is structured to be easy to understand, modify and extend. -
Optimized numerical workflow
Introduces minimal overhead to the calculation of the mean-field Hamiltonian.
What MeanFi
doesn't do (yet)
Here are some features that are not yet implemented but are planned for future releases:
- Superconductive order parameters. Mean-field Hamiltonians do not include pairing terms.
- General interactions. We allow only density-density interactions (e.g. Coulomb) which can be described by a second-order tensor.
- Temperature effects. Density matrix calculations are done at zero temperature.
Installation
pip install meanfi
Citing MeanFi
If you have used MeanFi
for work that has led to a scientific publication, please cite us as:
@misc{meanfi,
author = {Vilkelis, Kostas and Zijderveld, R. Johanna and Akhmerov, Anton R. and Manesco, Antonio L.R.},
doi = {10.5281/zenodo.11149850},
month = {5},
title = {MeanFi},
year = {2024}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file meanfi-1.0.0.tar.gz
.
File metadata
- Download URL: meanfi-1.0.0.tar.gz
- Upload date:
- Size: 178.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b0d9459f218bc502b33017a642059babb3541b59243bf09afd282050f773ee64 |
|
MD5 | eb009582656b732063ebd1e705e1597b |
|
BLAKE2b-256 | c4276b641f19d6580c5956f56b6b100527314bc74278a9c0c0abef73133ee2d8 |
File details
Details for the file meanfi-1.0.0-py3-none-any.whl
.
File metadata
- Download URL: meanfi-1.0.0-py3-none-any.whl
- Upload date:
- Size: 20.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.0
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 45f7e87709588fbf7d0ee469bd072fabdb210bf0c0515a8209bc36abd75fcc95 |
|
MD5 | a17f34d7498635569fbcba6b95cbc357 |
|
BLAKE2b-256 | 7b6205da787de9017c297a18470a366beec47e7a99c4b6e073a0993dc6b64e41 |