{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to multivariable Green's functions\n", "\n", "This notebook demonstrates how to create and manipulate multivariable Green's functions.\n", "As an example, we consider the Green's function on a square lattice with nearest-neighbour hopping $t$,\n", "\n", "\\begin{equation}\n", "G(\\mathbf{k},i\\omega_n)=\\frac{1}{i\\omega_n + \\mu - \\epsilon(\\mathbf{k})}\n", "\\end{equation}\n", "\n", "with dispersion $\\epsilon(\\mathbf{k})=-2t(\\cos{k_x}+\\cos{k_y})$. Here $\\mathbf{k}$ is a vector in the Brillouin zone (in units where the lattice spacing is unity $a=1$), $\\mu$ is the chemical potential and $i\\omega_n$ is a Matsubara frequency." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and parameters\n", "\n", "Below we import modules that will be useful in the following. We also set the\n", "parameters of the problem." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Relevant Imports \n", "from triqs.lattice import BravaisLattice, BrillouinZone\n", "from triqs.gfs import Gf, MeshProduct, MeshBrZone, MeshImFreq\n", "import numpy as np\n", "from math import cos, pi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Physical parameters\n", "beta = 2 # Inverse temperature\n", "t = 1.0 # Hopping (unit of energy)\n", "mu = 0 # Chemical potential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constructing and Initializing a Lattice Green's function\n", "\n", "We first define a simple Bravais lattice (`BravaisLattice`) in 2 dimensions with basis vectors $\\hat{e}_x = (1, 0, 0)$ and ${\\hat e}_y=(0, 1, 0)$. Given this bravais lattice we construct the reciprocal (momentum) space Brillouin zone (`BrillouinZone`), on which we can then construct a momentum mesh (`MeshBrZone`)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "BL = BravaisLattice([(1,0,0), (0,1,0)]) # Two unit vectors in R3\n", "BZ = BrillouinZone(BL) \n", "\n", "# n_k denotes the number of k-points for each dimension\n", "n_k = 128\n", "k_mesh = MeshBrZone(bz=BZ, n_k=n_k)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Lattice Green's function is defined on a mesh that is the cartesian product of this momentum mesh and a Matsubara mesh.\n", "\n", "$$\n", "G: (\\mathbf{k}, i\\omega_n) \\rightarrow {\\mathcal{C}}\n", "$$\n", "\n", "To construct this mesh we use the `MeshProduct` provided by TRIQS:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "iw_mesh = MeshImFreq(beta=beta, statistic='Fermion', n_iw=128)\n", "k_iw_mesh = MeshProduct(k_mesh, iw_mesh)\n", "\n", "# Recall that for an empty target_shape G0 has values that are scalars instead of matrices.\n", "G = Gf(mesh=k_iw_mesh, target_shape=[])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To fill the Green's function we construct a function for the dispersion $\\epsilon(\\mathbf{k})$ and set each element of $G$ by looping over the momentum and frequency meshes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#%%timeit\n", "def eps(k):\n", " return -2*t * (cos(k[0]) + cos(k[1]))\n", "\n", "# Loop initialization. Slow..\n", "for k, iw in G.mesh:\n", " G[k, iw] = 1/(iw + mu - eps(k))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NumPy Broadcasting\n", "\n", "Instead of writing a loop we can use the [broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html) features of the NumPy package to assign directly into the data-array of the Green's function object. This approach is a lot faster than writing a loop" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "iw_arr = np.array(list(iw_mesh.values()))\n", "k_arr = np.array(list(k_mesh.values()))\n", "np_eps = np.vectorize(eps, signature='(d)->()')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#%%timeit\n", "# Vectorized function evaluation\n", "eps_arr = np_eps(k_arr)\n", "\n", "# Numpy Broadcasting\n", "G.data[:] = 1.0 / (iw_arr[None,::] + mu - eps_arr[::,None])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We provide through the [TRIQS/tprf](https://triqs.github.io/tprf) application more efficient parallelized routines for initializing lattice Green functions. Those will be introduced in the **Two Particle Reponse** Notebooks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluate the Green's function\n", "\n", "The Green's function object $G(k,i\\omega_n)$ can be evaluated like an ordinary Python function\n", "at a given reciprocal vector and Matsubara frequency:\n", "\n", "- The reciprocal vector $k$ is a tuple/list/numpy.array of double \n", "- The Matsubara frequency is an integer $n$, the $n$ in $i\\omega_n$\n", "\n", "The result will be a linear interpolation on the Brillouin zone \n", " with the points on the grid of $G$ around $k$.\n", "\n", "Therefore, one can use $g_0$ as any python function of $k$ and $i\\omega_n$, \n", "and forget its precise representation in memory (what is the grid, etc...).\n", "We will use that in the plot functions below.\n", "\n", "Example:\n", "Let's evaluate the above Green's function at $\\mathbf{k} = (\\pi,\\pi,0)$ and $i\\omega_2$. As $\\epsilon((\\pi,\\pi,0)) = 4t = 4$ and $i\\omega_2 = i\\frac{(2*2 + 1)\\pi}{\\beta}$, we check:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0j\n" ] } ], "source": [ "G_eval = G((pi,pi,0), 2)\n", "G_exact = 1.0/(1j * (2*2+1)*pi/beta - 4)\n", "print(G_eval - G_exact) # Check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Partial evaluation\n", "\n", "Given a function $G(k,i\\omega_n)$ you can obtain the function $i\\omega_n \\rightarrow G(k_0, i\\omega_n)$ for a fixed $k_0$:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Green's Function with mesh Imaginary frequency mesh with beta = 2, statistics = Fermion, N_iw = 128, positive_only = false and target_shape (): \n", "\n" ] } ], "source": [ "k0 = (0.02,0.01,0) # a k-point as a tuple of 3 floats\n", "Giw = G(k0, all) # We use the \"built-in\" function all here as equivalent of :, \n", " # which Python does not allow in ()\n", " \n", "# Giw is a Green's function of the Matsubara frequency only\n", "# It is calculated by k-interpolation of G\n", "print(Giw)\n", "\n", "# Giw uses the original Matsubara mesh\n", "assert Giw.mesh == G.mesh[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here `Giw` is obtained through linearly of `G` for the point $k_0$ on the original Brillouin zone grid.\n", "\n", "It is simply a Matsubara Green's function, which means that you can use all the common methods, such as `density()` or Fourier transforms:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n_k = 0.9996637574643696\n" ] } ], "source": [ "# This is the density n_k at k=(0.02, 0.01)\n", "print(\"n_k =\", Giw.density().real)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining a Tight-Binding Hamiltonian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practice we often know the tight-binding Hamiltonian on our Bravais lattice rather than the analytic dispersion relation.\n", "TRIQS provides the `TightBinding` class for this case:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python Library Documentation: class TightBinding in module triqs.lattice.lattice_tools\n", "\n", "class TightBinding(builtins.object)\n", " | Tight-binding Hamiltonian on a Bravais lattice with fully localised orbitals.\n", " |\n", " | The Hamiltonian is parametrised by a set of lattice displacements :math:`\\{ \\mathbf{R}_j \\}` (given in\n", " | units of the lattice basis vectors) and the associated overlap (hopping) matrices :math:`\\{ t_{\\mathbf{R}_j} \\}`\n", " | between orbitals in the unit cell. The Bloch Hamiltonian in reciprocal space is obtained by the discrete Fourier\n", " | transform\n", " |\n", " | .. math::\n", " |\n", " | h_{\\mathbf{k}} = \\sum_j t_{\\mathbf{R}_j} \\, e^{2 \\pi i \\, \\mathbf{k} \\cdot \\mathbf{R}_j} \\; ,\n", " |\n", " | where the momentum :math:`\\mathbf{k}` is expressed in units of the reciprocal lattice basis vectors.\n", " |\n", " | The orbital overlap within a unit cell (the on-site block at :math:`\\mathbf{R} = 0`) is the identity matrix unless\n", " | explicitly overridden by the user-provided hoppings.\n", " |\n", " | ----------\n", " |\n", " | Dispatched C++ constructor(s).\n", " |\n", " | ::\n", " |\n", " | [1] (bl: BravaisLattice, displ_vec: [ndarray[int, 1]], overlap_mat_vec: [ndarray[complex, 2]])\n", " |\n", " | [2] (bl: BravaisLattice, hoppings: dict[tuple[int,...], ndarray])\n", " |\n", " |\n", " | [1] Construct a tight-binding Hamiltonian on a given Bravais lattice from explicit displacement and overlap\n", " | lists.\n", " |\n", " | The matrix structure of each overlap matrix is with respect to the orbitals in the unit cell. The\n", " | displacement and overlap lists must have the same length, and every overlap matrix must be square with size\n", " | equal to the number of orbitals in the unit cell.\n", " |\n", " | ------\n", " |\n", " | [2] Construct a tight-binding Hamiltonian on a given Bravais lattice from a hopping dictionary.\n", " |\n", " | ------\n", " |\n", " | Parameters\n", " | ----------\n", " | bl : BravaisLattice\n", " | Underlying Bravais lattice.\n", " | displ_vec : [ndarray[int, 1]]\n", " | List of displacement vectors, in units of the lattice basis vectors.\n", " | overlap_mat_vec : [ndarray[complex, 2]]\n", " | List of overlap (hopping) matrices, one per displacement.\n", " | hoppings : dict[tuple[int,...], ndarray]\n", " | Hopping dictionary mapping displacement vectors to their overlap matrices.\n", " |\n", " | Methods defined here:\n", " |\n", " | __eq__(self, value, /)\n", " | Return self==value.\n", " |\n", " | __ge__(self, value, /)\n", " | Return self>=value.\n", " |\n", " | __getstate__(...)\n", " | Helper for pickle.\n", " |\n", " | __gt__(self, value, /)\n", " | Return self>value.\n", " |\n", " | __init__(self, /, *args, **kwargs)\n", " | Initialize self. See help(type(self)) for accurate signature.\n", " |\n", " | __le__(self, value, /)\n", " | Return self<=value.\n", " |\n", " | __lt__(self, value, /)\n", " | Return self ndarray[float, 1]\n", " |\n", " | [2] (k: ndarray[float, 2])\n", " | -> ndarray[float, 2]\n", " |\n", " | [3] (k_mesh: MeshBrZone)\n", " | -> Gf[MeshBrZone, 1]\n", " |\n", " | [4] (n_l: int)\n", " | -> Gf[MeshBrZone, 1]\n", " |\n", " |\n", " | [1, 2] Compute the dispersion, i.e. the eigenvalue spectrum of :math:`h_{\\mathbf{k}}`, for a given momentum vector\n", " | (or array of momentum vectors).\n", " |\n", " | ------\n", " |\n", " | [3] Compute the dispersion on a given Brillouin zone mesh.\n", " |\n", " | ------\n", " |\n", " | [4] Compute the dispersion on a regular Brillouin zone mesh with `n_l` points per dimension.\n", " |\n", " | ------\n", " |\n", " | Parameters\n", " | ----------\n", " | k : ndarray[float, 1], ndarray[float, 2]\n", " | Momentum vector (or an array of momentum vectors) in units of the reciprocal lattice basis vectors.\n", " | k_mesh : MeshBrZone\n", " | Brillouin zone mesh on which to evaluate the band energies.\n", " | n_l : int\n", " | Number of grid-points along each reciprocal direction.\n", " |\n", " | Returns\n", " | -------\n", " | [1] : ndarray[float, 1]\n", " | Real-valued array of length `n_orbitals` containing the band energies at :math:`\\mathbf{k}`, or an array\n", " | of such band-energy arrays when an array of momenta is passed.\n", " |\n", " | [2] : ndarray[float, 2]\n", " | Real-valued array of length `n_orbitals` containing the band energies at :math:`\\mathbf{k}`, or an array\n", " | of such band-energy arrays when an array of momenta is passed.\n", " |\n", " | [3] : Gf[MeshBrZone, 1]\n", " | Tensor-valued Green's function defined on `k_mesh`, with its data initialised with the band energies at\n", " | every mesh point (one real value per orbital).\n", " |\n", " | [4] : Gf[MeshBrZone, 1]\n", " | Tensor-valued Green's function defined on the regular Brillouin zone mesh, with its data initialised with\n", " | the band energies at every mesh point.\n", " |\n", " | fourier(...)\n", " | Dispatched C++ function(s).\n", " |\n", " | ::\n", " |\n", " | [1] (k: ndarray[float, 1])\n", " | -> ndarray[complex, 2]\n", " |\n", " | [2] (k: ndarray[float, 2])\n", " | -> ndarray[complex, 3]\n", " |\n", " | [3] (k_mesh: MeshBrZone)\n", " | -> Gf[MeshBrZone, 2]\n", " |\n", " | [4] (n_l: int)\n", " | -> Gf[MeshBrZone, 2]\n", " |\n", " |\n", " | [1, 2] Compute the Fourier transform for a given momentum vector (or array of momentum vectors).\n", " |\n", " | The Bloch Hamiltonian is given by\n", " |\n", " | .. math::\n", " |\n", " | h_{\\mathbf{k}} = \\sum_j t_{\\mathbf{R}_j} \\, e^{2 \\pi i \\, \\mathbf{k} \\cdot \\mathbf{R}_j} \\; ,\n", " |\n", " | with lattice displacements :math:`\\{ \\mathbf{R}_j \\}` and associated overlap (hopping) matrices\n", " | :math:`\\{ t_{\\mathbf{R}_j} \\}`. The momentum :math:`\\mathbf{k}` is expressed in units of the reciprocal lattice\n", " | basis vectors.\n", " |\n", " | ------\n", " |\n", " | [3] Compute the Fourier transform on a given Brillouin zone mesh.\n", " |\n", " | ------\n", " |\n", " | [4] Compute the Fourier transform on a regular Brillouin zone mesh with `n_l` points per dimension.\n", " |\n", " | ------\n", " |\n", " | Parameters\n", " | ----------\n", " | k : ndarray[float, 1], ndarray[float, 2]\n", " | Momentum vector (or an array of momentum vectors) in units of the reciprocal lattice basis vectors.\n", " | k_mesh : MeshBrZone\n", " | Brillouin zone mesh on which to evaluate the Bloch Hamiltonian.\n", " | n_l : int\n", " | Number of grid-points along each reciprocal direction.\n", " |\n", " | Returns\n", " | -------\n", " | [1] : ndarray[complex, 2]\n", " | Complex matrix :math:`h_{\\mathbf{k}}` (or an array of such matrices, one per input momentum).\n", " |\n", " | [2] : ndarray[complex, 3]\n", " | Complex matrix :math:`h_{\\mathbf{k}}` (or an array of such matrices, one per input momentum).\n", " |\n", " | [3] : Gf[MeshBrZone, 2]\n", " | Matrix-valued Green's function defined on `k_mesh`, with its data initialised with the Fourier transform\n", " | :math:`h_{\\mathbf{k}}` at every mesh point.\n", " |\n", " | [4] : Gf[MeshBrZone, 2]\n", " | Matrix-valued Green's function defined on the regular Brillouin zone mesh, with its data initialised with\n", " | the Fourier transform :math:`h_{\\mathbf{k}}` at every mesh point.\n", " |\n", " | lattice_to_real_coordinates(...)\n", " | Dispatched C++ function(s).\n", " |\n", " | ::\n", " |\n", " | [1] (x: ndarray[float, 1])\n", " | -> ndarray[float, 1]\n", " |\n", " |\n", " | Transform a vector from the lattice basis to the standard basis.\n", " |\n", " | Equivalent to calling lattice_to_real_coordinates() on the underlying Bravais lattice.\n", " |\n", " | Parameters\n", " | ----------\n", " | x : ndarray[float, 1]\n", " | Vector in the lattice basis.\n", " |\n", " | Returns\n", " | -------\n", " | ndarray[float, 1]\n", " | Vector in the standard basis.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " |\n", " | __new__(*args, **kwargs)\n", " | Create and return a new object. See help(type) for accurate signature.\n", " |\n", " | h5_read_construct(...)\n", " | Dispatched C++ function(s).\n", " |\n", " | ::\n", " |\n", " | [1] (g: Group, subgroup_name: str)\n", " | -> TightBinding\n", " |\n", " |\n", " | Construct a tight-binding Hamiltonian by reading it from HDF5.\n", " |\n", " | Parameters\n", " | ----------\n", " | g : Group\n", " | `h5::group` to be read from.\n", " | subgroup_name : str\n", " | Name of the subgroup.\n", " |\n", " | Returns\n", " | -------\n", " | TightBinding\n", " | The reconstructed tight-binding Hamiltonian.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | displ_vec\n", " | Get the list of displacement vectors, in units of the lattice basis vectors.\n", " |\n", " | lattice\n", " | Get the underlying Bravais lattice.\n", " |\n", " | n_orbitals\n", " | Number of orbitals (also the size of the Bloch Hamiltonian matrix :math:`h_{\\mathbf{k}}`).\n", " |\n", " | overlap_mat_vec\n", " | Get the list of overlap (hopping) matrices, aligned with the displacement vectors.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " |\n", " | __hash__ = None\n", "\n" ] } ], "source": [ "from triqs.lattice import TightBinding\n", "?TightBinding" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Define mapping between displacement vectors and hopping amplitudes\n", "# Matrix structure of the amplitudes is w.r.t. atoms in the unit cell (here only one).\n", "hop= { (1,0) : [[ -t]], \n", " (-1,0) : [[ -t]], \n", " (0,1) : [[ -t]],\n", " (0,-1) : [[ -t]]\n", " }\n", "TB = TightBinding(bl=BL, hoppings=hop)\n", "\n", "# Green's function on the k_mesh holding the dispersion values\n", "eps_k = TB.dispersion(k_mesh)[0]\n", "\n", "# Initialize the lattice Green's function using Numpy Broadcasting\n", "Gtb = G.copy()\n", "Gtb.data[:] = 1.0 / (iw_arr[None,::] + mu - eps_k.data[::,None])\n", "\n", "# Check Equality\n", "assert np.linalg.norm((G - Gtb).data) < 1e-12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note that the object `eps_k` returned by the `dispersion` function is also a triqs `Gf` object, but one that has only a momentum mesh. This illustrates nicely that the `Gf` class in TRIQS is very flexible w.r.t. the domain of definition (mesh), and can be used to store generic functions on the domain, not just Green Functions in the sense of the many-body definition.\n", "\n", "Now let's plot the dispersion relation `eps_k` we have obtained" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.92, '$\\\\epsilon(\\\\mathbf{k})$')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Prepare the data\n", "k_grid = k_arr.reshape(n_k,n_k,3)\n", "X = k_grid[...,0]/pi\n", "Y = k_grid[...,1]/pi\n", "Z = eps_k.data.reshape(n_k,n_k)\n", "\n", "# Plot the dispersion\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "fig = plt.figure(dpi=110)\n", "ax = plt.axes(projection='3d')\n", "surf = ax.plot_surface(X, Y, Z, cmap='coolwarm')\n", "fig.colorbar(surf, shrink=0.5, aspect=10)\n", "\n", "ax.set_xlabel(r\"$k_x/\\pi$\")\n", "ax.set_ylabel(r\"$k_y/\\pi$\")\n", "ax.set_title(r\"$\\epsilon(\\mathbf{k})$\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 4 }