diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..cad7657 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "cmake.configureOnOpen": false +} \ No newline at end of file diff --git a/AbinitioDMFT/data/mlwf/lco_centres.xyz b/AbinitioDMFT/data/mlwf/lco_centres.xyz new file mode 100644 index 0000000..cdd243e --- /dev/null +++ b/AbinitioDMFT/data/mlwf/lco_centres.xyz @@ -0,0 +1,10 @@ + 8 + Wannier centres, written by Wannier90 on24Jul2023 at 10:48:54 +X -0.00000000 -0.00000000 0.00000000 +La -0.00000000 -0.00000000 4.77028391 +La 1.90914470 1.90914470 1.83281415 +Cu -0.00000000 -0.00000000 -0.00000000 +O -0.00000000 1.90914470 0.00000000 +O 1.90914470 -0.00000000 0.00000000 +O -0.00000000 -0.00000000 2.45222396 +O 1.90914470 1.90914470 4.15087411 diff --git a/Advanced/TB_to_sympy.ipynb b/Advanced/TB_to_sympy.ipynb new file mode 100644 index 0000000..85a01ed --- /dev/null +++ b/Advanced/TB_to_sympy.ipynb @@ -0,0 +1,416 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ```TB_to_sympy```: Simplifying Hamiltonian Expressions for Condensed Matter Systems Within a Tight-Binding Model Framework." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction. ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you model electrons as tightly bound to atoms, you get the TB model which assumes that main electronic behavior arises from interactions between neighboring atoms, where electrons hop between atomic orbitals through tunneling or wavefunction overlap.\n", + "The TB model is a minimal model that serves as a foundation for studying electron behavior in metals, including the origins of magnetism and superconductivity:\n", + "- For theorists, it reduces the number of parameters, from which they can study the effects of the parameters separately.\n", + "- For experimentalists, it’s useful for a one-body description of the electronic structure. Therefore, if they can understand their data by using a simple representation like a TB model, their data can be understood simply. An analytical TB model with a restricted number of parameters can help experimentalists fit their data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ```TB_to_sympy``` function takes hopping amplitudes to generate a TB model. The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In second quantization, the TB Hamiltonian is given by:\n", + "\n", + "$$\n", + "H = -\\sum_{ l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", + "$$\n", + "\n", + "where:\n", + "- $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$.\n", + "- $\\sigma$ is the spin and $c^{\\dagger}$\n", + "- $c$ are creation and annihilation operators.\n", + "\n", + "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(k)$, which is achieved using the ```TB_to_sympy``` function.\n", + "\n", + "$$\n", + "H(k) = \\frac{1}{(2\\pi)^d} \\sum_{R} e^{i k \\cdot R} H_{R}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function Definition and its Parameters. ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```TB_to_sympy``` is defined as:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " returns the analytical form of the momentum space hamiltonian of the tight-binding model \n", + " from a tight-binding lattice object by utilizing Fourier series\n", + " \n", + " Parameters\n", + " ----------\n", + " TBL: triqs TBLattice object\n", + " triqs tight binding object\n", + " analytical: boolean, default = True\n", + " whether to return the Hamiltonian in analytical (true) or numerical (false) form.\n", + " precision: integer, default = 6\n", + " specifies the number of digits in the floating point amplitudes. The default value is 6 but the user\n", + " can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings\n", + " across the crystal lattice\n", + " \n", + " Returns\n", + " -------\n", + " Hk: NumPy array\n", + " the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical\n", + " form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output\n", + " is the reduced analytical form. The numerical form depends solely on the k-space vector components\n", + " while the analytical form takes into account both the k-space vector components and the lattice\n", + " vectors\n", + "\n", + " \n" + ] + } + ], + "source": [ + "from triqs.lattice.utils import TB_to_sympy\n", + "\n", + "print(TB_to_sympy.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only required parameter for our function is ```TBL```, a TRIQS TBLattice Object (Tight-Binding Lattice Object). To obtain ```TBL```, you can use TRIQS-provided functions that allow conversion of your model to a TRIQS TBLattice object. These functions are:\n", + "1. ```TB_from_pythTB``` that converts your model from a pythTB model to a TRIQS TBLattice Object.\n", + "2. ```TB_from_wannier90``` which converts your model from a Wannier90 model to a TRIQS TBLattice Object. It reads wannier90 output and converts it to a TBLattice object.\n", + "\n", + "Currently, it is advisable to utilize ```TB_from_pythTB``` since it enables straightforward initiation of cutoffs to your pythTB model before converting it to a TBLattice object, especially for complex systems, granting greater control over your model and allows examination of the output's behavior at different cutoffs. The possible cutoff parameters in pythTB include:\n", + "1. ```zero_energy```: This parameter establishes the energy zero point in the band structure, typically aligned with the Fermi level.\n", + "2. ```min_hopping_norm```: The hopping terms obtained from Wannier90 are filtered based on their hopping amplitudes (measured in electron volts). Terms with amplitudes less than ```min_hopping_norm``` are excluded from the calculations.\n", + "3. ```max_distance```: Hopping distances exceeding ```max_distance``` are disregarded during the calculations.\n", + "\n", + "The ```analytical``` flag ensures that the default Hamiltonian returned by ```sympyfy``` is in an analytical form. If the user sets the ```analytical``` flag to ```False```, the resulting Hamiltonian will be in a \"numerical\" form. Although termed \"numerical,\" this expression still depends on the parameters of the k-space vectors (```kx```, ```ky```, and ```kz```) as the lattice constants and vectors are expressed numerically. For simplicity, we will continue to refer to it as numerical in this tutorial, assuming the user can provide numerical values for ```kx```, ```ky```, and ```kz```.\n", + "\n", + "Lastly, the ```precision``` parameter allows the user to control the number of digits in the hopping amplitudes and lattice parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling the Function ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first import the necessary dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: could not identify MPI environment!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting serial run at: 2023-10-03 19:38:51.236899\n" + ] + } + ], + "source": [ + "# importing the dependencies\n", + "from itertools import product as itp\n", + "from pythtb import *\n", + "from triqs.lattice.tight_binding import TBLattice\n", + "import sympy as sp\n", + "import warnings\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from triqs.lattice.utils import TB_from_pythTB\n", + "# accessing the necessary Wannier90 output files\n", + "w90_input = w90('../AbinitioDMFT/data/mlwf/', 'lco')\n", + "fermi_ev = 12.7367\n", + "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", + "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here are two cases:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical expression \n", + " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The analytical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = True, precision = 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The numerical expression \n", + " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The numerical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = False, precision = 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main distinction between the analytical and numerical expressions is that the numerical expression depends on ```kx```, ```ky```, and ```kz```, while the analytical expression additionally depends on the lattice vectors, ```a1```, ```a2```, and ```a3```. The lattice vectors define the orientation and shape of the crystal unit cell and can be replaced with numerical values using the ```units``` function from the TRIQS TB lattice object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Minimal Models ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a figure to compare three minimal models, each with different `min_hopping_norm` cutoffs, against the full model from Wannier90. In the snippet below we compare 3 minimal models. You are free to choose the cutoffs you wish." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let us create a function that deals with making the minimal models:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def minimal_model(w90_input, fermi_ev, cutoff):\n", + " # get band structure from Wannier90 in the form of\n", + " # list of k-points in reduced coordinates\n", + " # energies interpolated by Wannier90\n", + " (w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "\n", + " # obtain simplified model based on various cutoffs\n", + " w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff, max_distance = None)\n", + "\n", + " # solve simplified model on the same k-path as in Wannier90\n", + " return w90_model.solve_all(w90_kpt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let us set up the comparison plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setting up the plots\n", + "fig, ax = plt.subplots(dpi = 200)\n", + "\n", + "# plotting the full Wannier90 model\n", + "(w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "for i in range(w90_evals.shape[0]):\n", + " x_axis = list(range(w90_evals.shape[1]))\n", + " y_axis = w90_evals[i] - fermi_ev\n", + " ax.plot(x_axis, y_axis, \"k-\", zorder = -100, label = \"Full model\")\n", + "\n", + "# plotting the minimal models\n", + "for cutoff in [0.038, 0.05, 0.45]:\n", + " int_evals = minimal_model(w90_input, fermi_ev, cutoff)\n", + " for i in range(int_evals.shape[0]):\n", + " ax.plot(list(range(int_evals.shape[1])), int_evals[i], zorder = -50, label = \"Cutoff at %.4f eV\" % cutoff)\n", + " \n", + "# plotting the horizontal line that passes through the y-axis\n", + "ax.axhline(y = 0, color = 'm', linestyle = '--')\n", + "ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", + "\n", + "# increasing the width of the curves\n", + "lines = ax.lines\n", + "for line in lines:\n", + " line.set_linewidth(2.5)\n", + "\n", + "# coordinates of the high symmetry points\n", + "G = np.array([0.00, 0.00, 0.00])\n", + "N = np.array([0.25, -0.25, 0.25])\n", + "X = np.array([0.00, 0.00, 0.50])\n", + "M = np.array([0.25, 0.25, -0.25])\n", + "\n", + "# x-axis labels of the high-symmetry points\n", + "kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", + "idx_kpt = {}\n", + "\n", + "# checking k-point coordinates equal to the high-symmetry point coordinates\n", + "for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", + " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", + " \n", + "# setting the x-axis ticks\n", + "kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", + "kpath_xticks.append(idx_kpt['G'][1])\n", + "kpath_xticks = sorted(kpath_xticks)\n", + "ax.set_xticks(kpath_xticks)\n", + "ax.set_xticklabels(kpath_labels)\n", + "\n", + "# setting the vertical lines at the high-symmetry points\n", + "for n in range(len(kpath_xticks)):\n", + " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", + "\n", + "# plot properties\n", + "ax.set_xlim(0, int_evals.shape[1] - 1)\n", + "ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", + "ax.set_title(\"Comparing Minimal Models\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this.\n", + "\n", + "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "triqs-dev", + "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", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/SymPyfy Tutorial.ipynb b/SymPyfy Tutorial.ipynb new file mode 100644 index 0000000..72863e0 --- /dev/null +++ b/SymPyfy Tutorial.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ```TB_to_sympy```: Simplifying Hamiltonian Expressions for Condensed Matter Systems Within a Tight-Binding Model Framework." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction. ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tight-binding (TB) model is used in condensed matter physics to model the properties of conductors, semiconductors, and insulators. Experimental physicists often interact with the TB model during electronic band structure measurements, like angle-resolved photoemission spectroscopy (ARPES). They must verify that their experimental data aligns with theoretical predictions. The TB model's tractability offers a clear analytical solution, which proves efficient for experimentalists to quickly validate their data. Thus, there's a need for a function like ```TB_to_sympy```, which provides a reduced analytical Hamiltonian expression for the analyzed element." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you model electrons as tightly bound to atoms, you get the TB model which assumes that main electronic behavior arises from interactions between neighboring atoms, where electrons hop between atomic orbitals through tunneling or wavefunction overlap.\n", + "The TB model is a minimal model that serves as a foundation for studying electron behavior in metals, including the origins of magnetism and superconductivity:\n", + "- For theorists, it reduces the number of parameters, from which they can study the effects of the parameters separately.\n", + "- For experimentalists, it’s useful for a one-body description of the electronic structure. Therefore, if they can understand their data by using a simple representation like a TB model, their data can be understood simply. An analytical TB model with a restricted number of parameters can help experimentalists fit their data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ```TB_to_sympy``` function is designed to process a pre-constructed Tight-Binding (TB) model, transforming it into an analytic expression. This function does not generate a TB model on its own. Instead, it takes an existing TB model, which could either be derived from a Wannier90 calculation or represent a model Hamiltonian. The primary output of this function is the analytic representation of the input TB model, effectively translating its hopping amplitudes and other parameters into a symbolic form suitable for further analysis and manipulation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hopping amplitudes are obtained from the Wannier90 package, which fits the band structure obtained from density functional theory (DFT) for a set of orbitals. The larger the hopping amplitude, the more significant it is. For instance, a hopping amplitude of 0.00005 eV does not contribute as much to the movement of an electron as a hopping amplitude of 0.05 eV. There is a need for a cutoff either in the hopping distances or the hopping amplitudes. A minimal model can neglect these smaller amplitudes. Using the PythTB package's ```model``` function, we select only the hopping amplitudes bigger than the cutoff or smaller than the cutoff distance, restricting the hopping processes to be closer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A generic form of a TB Hamiltonian reads:\n", + "\n", + "$$\n", + "H = -\\sum_{ l_1 l_2 \\sigma} t_{i j}^{l_1 l_2} [c^{\\dagger}_{i l_1 \\sigma} c_{j l_2 \\sigma} + c^{\\dagger}_{j l_2 \\sigma} c_{i l_1 \\sigma}]\n", + "$$\n", + "\n", + "where:\n", + "- $t_{i j}^{l_1 l_2}$ is the hopping amplitude between orbital $l_1$ on site $i$ and orbital $l_2$ on site $j$.\n", + "- $\\sigma$ is the spin and $c^{\\dagger}$\n", + "- $c$ are creation and annihilation operators.\n", + "\n", + "In this tutorial, we examine a system with one orbital and one site per unit cell. By performing a Fourier transform of the Hamiltonian from real space to momentum space, we obtain $H(k)$.\n", + "\n", + "$$\n", + "H(k) = \\frac{1}{(2\\pi)^d} \\sum_{R} e^{i k \\cdot R} H_{R}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function Definition and its Parameters. ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first import the necessary dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# importing the dependencies\n", + "from itertools import product as itp\n", + "from pythtb import *\n", + "from triqs.lattice.tight_binding import TBLattice\n", + "import sympy as sp\n", + "import warnings\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# import all files from lattice in TRIQS\n", + "\n", + "# # importing it from TRIQS repo\n", + "# from triqs.lattice.utils import TB_to_sympy\n", + "\n", + "# importing it from this locala tutorials repo\n", + "from TB_to_sympy import TB_to_sympy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To view the documentation for the `TB_to_sympy` function, run the following cell. The documentation will appear in a pop-up window or an inline frame, showing the detailed description, parameters, and return values of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function TB_to_sympy in module TB_to_sympy:\n", + "\n", + "TB_to_sympy(TBL, analytical=True, precision=6)\n", + " returns the analytical form of the momentum space hamiltonian of the tight-binding model \n", + " from a tight-binding lattice object by utilizing Fourier series\n", + " \n", + " Parameters\n", + " ----------\n", + " TBL: triqs TBLattice object\n", + " triqs tight binding object\n", + " analytical: boolean, default = True\n", + " whether to return the Hamiltonian in analytical (true) or numerical (false) form.\n", + " precision: integer, default = 6\n", + " specifies the number of digits in the floating point amplitudes. The default value is 6 but the user\n", + " can decrease it to help recognize similar hopping amplitudes, particularly for symmetrical hoppings\n", + " across the crystal lattice\n", + " \n", + " Returns\n", + " -------\n", + " Hk: NumPy array\n", + " the Hamiltonian of the tight-binding model in momentum space. It can be output in either numerical\n", + " form (Hk_numerical) or reduced analytical form (Hk) based on the user's choice. The default output\n", + " is the reduced analytical form. The numerical form depends solely on the k-space vector components\n", + " while the analytical form takes into account both the k-space vector components and the lattice\n", + " vectors\n", + "\n" + ] + } + ], + "source": [ + "help(TB_to_sympy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further explanation of the parameters:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary parameter required for our function is ```w90_triqs```, which is a TRIQS TBLattice Object (Tight-Binding Lattice Object). This object can be obtained in multiple ways. While it can be generated through conversion functions like ```TB_from_pythTB``` (converting a pythTB model to TRIQS TBLattice) or ```TB_from_wannier90``` (converting a Wannier90 model to TRIQS TBLattice), it's important to note that ```w90_triqs``` can also be directly defined without these conversion steps. This flexibility allows for a broader range of applications, accommodating both models derived from tools like pythTB or Wannier90 and those created explicitly as TRIQS TBLattice objects.\n", + "\n", + "When using conversion functions, ```TB_from_pythTB``` is particularly useful for initiating cutoffs in your pythTB model before converting it to a TBLattice object. This is especially beneficial for complex systems, as it provides greater control over the model and allows for the examination of the output's behavior at different cutoffs. The available cutoff parameters in pythTB include:\n", + "\n", + "1. ```zero_energy```: Sets the energy zero point in the band structure, typically aligned with the Fermi level.\n", + "2. ```min_hopping_norm```: Filters hopping terms based on their amplitudes, measured in electron volts. Terms with amplitudes below this threshold are excluded.\n", + "3. ```max_distance```: Excludes hopping distances beyond this specified maximum.\n", + "\n", + "The ```analytical``` flag in the function determines the form of the default Hamiltonian returned by ```TB_to_sympy```. If set to ```False```, the Hamiltonian is in a 'numerical' form, which, despite the name, still depends on parameters like ```kx```, ```ky```, and ```kz```. For the purpose of this tutorial, we will refer to it as numerical, assuming the user can provide these k-space vector values.\n", + "\n", + "Lastly, the ```precision``` parameter allows users to control the number of digits in the hopping amplitudes and lattice parameters, providing further customization and control over the modeling process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling the Function ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us first load a model of $La_2CuO_4$, lanthanum copper oxide, a high-temperature superconductor:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from triqs.lattice.utils import TB_from_pythTB\n", + "# accessing the necessary Wannier90 output files\n", + "w90_input = w90('AbinitioDMFT/data/mlwf/', 'lco')\n", + "fermi_ev = 12.7367\n", + "w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = 0.05, max_distance = None)\n", + "w90_triqs_La2CuO4 = TB_from_pythTB(w90_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call ```TB_to_sympy``` for different cases depending on the values we assign to its parameters. Here is one case:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical expression \n", + " [[-0.88*cos(a1k + a3k) - 0.88*cos(a2k + a3k) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The analytical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = True, precision = 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The numerical expression \n", + " [[-0.88*cos(3.818*kx) - 0.88*cos(3.818*ky) + 0.159]]\n" + ] + } + ], + "source": [ + "print(\"The numerical expression \\n\", TB_to_sympy(w90_triqs_La2CuO4, analytical = False, precision = 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key distinction between the analytical and numerical expressions lies in their respective use of basis vectors. The numerical expression utilizes Euclidean unit vectors and depends on ```kx```, ```ky```, and ```kz```. These unit vectors are standard Cartesian coordinates that represent points in the Euclidean space.\n", + "\n", + "On the other hand, the analytical expression employs reciprocal basis vectors ```a1```, ```a2```, and ```a3```. These vectors define the orientation and shape of the crystal's unit cell in reciprocal space. The reciprocal lattice vectors are crucial in representing periodic structures and are used to express properties and behaviors of the crystal in terms of wave vectors.\n", + "\n", + "It's important to understand this distinction because it reflects the different frameworks within which each expression operates. While the numerical expression is grounded in the familiar Euclidean space, the analytical expression is more closely aligned with the intrinsic properties of the crystal structure as represented in reciprocal space. This difference is not just mathematical but also conceptual, offering varied perspectives and tools for analyzing the behavior of the crystal.\n", + "\n", + "Users can replace these lattice vectors with numerical values using the ```units``` function from the TRIQS TB lattice object. This conversion is particularly useful when translating the abstract concepts of lattice vectors into concrete numerical values for practical computations and analyses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Minimal Models ##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create a figure to compare three minimal models, each with different `min_hopping_norm` cutoffs, against the full model from Wannier90. In the snippet below we compare 3 minimal models. You are free to choose the cutoffs you wish." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let us create a function that deals with making the minimal models:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def minimal_model(w90_input, fermi_ev, cutoff):\n", + " \"\"\"\n", + " Constructs a minimal tight-binding model based on the input TRIQS TBLattice object.\n", + "\n", + " This function takes a pre-constructed TBLattice object, which represents the tight-binding \n", + " model of a crystal lattice, and applies additional parameters to refine the model.\n", + "\n", + " Parameters:\n", + " w90_input (TBLattice): A TRIQS TBLattice object representing the tight-binding model. \n", + " This object contains information about the lattice structure and \n", + " hopping parameters.\n", + " fermi_ev (float): The Fermi energy level in electron volts. This parameter sets the \n", + " energy zero point in the band structure.\n", + " cutoff (float): A threshold value to determine the significance of hopping terms in the \n", + " model. Hopping terms with amplitudes below this cutoff are excluded from \n", + " the calculations.\n", + "\n", + " Returns:\n", + " A modified TBLattice object: This object represents the refined tight-binding model with \n", + " applied cutoffs and adjusted Fermi level, suitable for further analysis and computation.\n", + " \"\"\"\n", + "\n", + " # get band structure from Wannier90 in the form of\n", + " # list of k-points in reduced coordinates\n", + " # energies interpolated by Wannier90\n", + " (w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "\n", + " # obtain simplified model based on various cutoffs\n", + " w90_model = w90_input.model(zero_energy = fermi_ev, min_hopping_norm = cutoff, max_distance = None)\n", + "\n", + " # solve simplified model on the same k-path as in Wannier90\n", + " return w90_model.solve_all(w90_kpt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let us set up the plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setting up the plots\n", + "fig, ax = plt.subplots(dpi = 200)\n", + "\n", + "# plotting the full Wannier90 model\n", + "(w90_kpt, w90_evals) = w90_input.w90_bands_consistency()\n", + "for i in range(w90_evals.shape[0]):\n", + " x_axis = list(range(w90_evals.shape[1]))\n", + " y_axis = w90_evals[i] - fermi_ev\n", + " ax.plot(x_axis, y_axis, \"k-\", zorder = -100, label = \"Full model\")\n", + "\n", + "# plotting the minimal models\n", + "for cutoff in [0.038, 0.05, 0.45]:\n", + " int_evals = minimal_model(w90_input, fermi_ev, cutoff)\n", + " for i in range(int_evals.shape[0]):\n", + " ax.plot(list(range(int_evals.shape[1])), int_evals[i], zorder = -50, label = \"Cutoff at %.4f eV\" % cutoff)\n", + " \n", + "# plotting the horizontal line that passes through the y-axis\n", + "ax.axhline(y = 0, color = 'm', linestyle = '--')\n", + "ax.set_yticks(np.arange(-2, 2.1, step = 1))\n", + "\n", + "# increasing the width of the curves\n", + "lines = ax.lines\n", + "for line in lines:\n", + " line.set_linewidth(2.5)\n", + "\n", + "# coordinates of the high symmetry points\n", + "G = np.array([0.00, 0.00, 0.00])\n", + "N = np.array([0.25, -0.25, 0.25])\n", + "X = np.array([0.00, 0.00, 0.50])\n", + "M = np.array([0.25, 0.25, -0.25])\n", + "\n", + "# x-axis labels of the high-symmetry points\n", + "kpath_labels = ['G', 'N', 'X', 'G', 'M']\n", + "idx_kpt = {}\n", + "\n", + "# checking k-point coordinates equal to the high-symmetry point coordinates\n", + "for name, idx in (zip(('G', 'N', 'X', 'M'), [G, N, X, M])):\n", + " idx_kpt[name] = list(np.where((w90_kpt == idx).all(axis = 1))[0])\n", + " \n", + "# setting the x-axis ticks\n", + "kpath_xticks = list([x[0] for x in idx_kpt.values()])\n", + "kpath_xticks.append(idx_kpt['G'][1])\n", + "kpath_xticks = sorted(kpath_xticks)\n", + "ax.set_xticks(kpath_xticks)\n", + "ax.set_xticklabels(kpath_labels)\n", + "\n", + "# setting the vertical lines at the high-symmetry points\n", + "for n in range(len(kpath_xticks)):\n", + " ax.axvline(x = kpath_xticks[n], linewidth = 1, color = 'k')\n", + "\n", + "# plot properties\n", + "ax.set_xlim(0, int_evals.shape[1] - 1)\n", + "ax.set_ylabel(r\"Band Energy - $E_F$ (eV)\")\n", + "ax.set_title(\"Comparing Minimal Models\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The band structure's accuracy depends on the user's choice of ```min_hopping_norm``` and ```max_distance``` values. By adjusting these parameters, the cutoff model can closely resemble the full Wannier90 model while providing a concise analytical Hamiltonian expression. Below is a visual example illustrating this.\n", + "\n", + "With a lower cutoff more hopping amplitudes are included which makes for a band structure that closely resembles the full Wannier90 band structure (which has all the hopping amplitudes present, that is, the full Wannier90 model includes all the hopping amplitudes when calculating the band energies across multiple k-paths)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "TRIQS", + "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", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}