First draft of readme

README.md

-# Project Name
+# pymf
 
-## Research Goal
+`pymf` is a Python package for numerical self-consistent mean-field calculations in tight-binding systems using Hartree-Fock approximation. Its interface is designed to allow users to control cost functions and numerical solvers. Additionally, it has an interface with [`Kwant`](https://kwant-project.org/) for simple construction of tight-binding models.
 
-## Research Plan
+## Installation
 
-## Working on this project
-Configure the project by running
+```
+pip install pymf
+```
 
-    ./setup
+## Usage
+
+The algorithm consists of a few simple steps. We exemplify with the one-dimensional Hubbard model with two atoms per unit cell.
+
+1. Define the Hamiltonian
+
+```python
+# Hopping matrix
+hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))
+# Non-interacting Hamiltonian
+h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}
+# Interacting Hamiltonian
+U=2
+s_x = np.array([[0, 1], [1, 0]])
+h_int = {(0,): U * np.kron(np.eye(2), s_x),}
+```
+
+2. Combine non-interacting and interacting Hamiltonians into a model.
+
+```python
+# Number of electrons per unit cell
+filling = 2
+# Define model
+model = pymf.Model(h_0, h_int, filling)
+```
+
+3. Compute the groundstate Hamiltonian.
+
+```python
+# Generate a random guess
+guess = pymf.generate_guess(frozenset(h_int), ndof=4)
+# Compute groundstate Hamiltonian
+gs_hamiltonian = pymf.solver(model, guess, nk=nk)
+```
+
+# Citing `pymf`
+
+We provide `pymf` as a free software under BSD license. If you have used `pymf` for work that has lead to a scientific publication, please mention the fact that you used it explicitly in the text body. For example, you may add
+
+> the numerical calculations were performed using the pymf code
+
+to the description of your numerical calculations. In addition, we ask you to cite the Zenodo repository:
+
+```
+zenodo red here
+```