{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Intermidiate Tutorial\n", "\n", "In this tutorial you will learn how to define the non-analytical constants for the Data types presented in the [previous tutorial](./basic.ipynb).\n", "\n", "## Polar insulators\n", "\n", "The phonon band structure of bulk polar insulators, which we define to be insulators with different atomic species (e.g. AlAs, BaTiO{sub}`3`),\n", "needs extra quantities to be interpolated correctly around the gamma point. These quantities are the so called __non-analytical constants__. They are composed by two tensors:\n", "\n", "1. __Dielectric tensor__: this is the high-frequency dielectric tensor of the material, $\\epsilon^{\\infty}_{ij}$.\n", "2. __Born effective charges tensor__: these are effective charges of the nuclei, usually expressed in unit of charge $e$, $Z^{*}_{I,ij}$\n", "\n", "The indices $i$ and $j$ refer to the three Cartesian axes, while $I$ is the atomic index.\n", "\n", "```{important}\n", "The tensors $Z^{*}_{I,ij}$ are not symmetric with the exchange of $i$ and $j$!\n", "```\n", "\n", "```{admonition} Convention\n", ":class: important \n", "In `Phonopy` and `aiida-phonopy`, the convention for $Z^{*}_{I,ij}$ indices is as follow:\n", "\n", "1. Atomic index.\n", "2. Polarization index.\n", "3. Atomic displacement index.\n", "```\n", "\n", "Let's show this with _AlAs_. On the same lines of the [basic tutorial](./basic.ipynb), we define \n", "{py:class}`~aiida_phonopy.data.preprocess.PreProcessData`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\n", "# ================= IMPORTS ======================== #\n", "from local_module import load_temp_profile\n", "from aiida.plugins import DataFactory, CalculationFactory\n", "from aiida.orm import load_code, Dict\n", "# If you download this file, you can run it with your own profile.\n", "# Put these lines instead:\n", "# from aiida import load_profile\n", "# load_profile()\n", "load_temp_profile(\n", " name=\"intermidiate-tutorial\",\n", " add_computer=True,\n", " add_phonopy_code=True,\n", ")\n", "\n", "StructureData = DataFactory(\"core.structure\")\n", "PreProcessData = DataFactory(\"phonopy.preprocess\")\n", "PhonopyData = DataFactory(\"phonopy.phonopy\")\n", "PhonopyCalculation = CalculationFactory(\"phonopy.phonopy\")\n", "# ============== STRUCTURE DATA ==================== #\n", "a = 2.839\n", "cell = [[0,a,a],[a,0,a],[a,a,0]]\n", "structure = StructureData(cell=cell)\n", "structure.append_atom(position=(0,0,0), symbols=\"Al\")\n", "structure.append_atom(position=(0.5*a,0.5*a,0.5*a), symbols=\"As\")\n", "# ============= PREPROCESS DATA ==================== #\n", "supercell_matrix = [[-1,1,1],[1,-1,1],[1,1,-1]] # standard cell 8 atoms\n", "preprocess_data = PreProcessData(structure=structure, supercell_matrix=supercell_matrix)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We could go through and compute the forces for the displaced structure. The result would be:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dict_of_forces = {\n", " \"forces_1\": \n", " [\n", " [-0.0988347466, 0.0000000000, 0.0000000000],\n", " [-0.0200237511, 0.0000000000, 0.0000000000],\n", " [ 0.0135502277, 0.0000000000, 0.0000000000],\n", " [ 0.0135502277, 0.0000000000, 0.0000000000],\n", " [ 0.0230530248, 0.0183687320, 0.0183687320],\n", " [ 0.0230530248, -0.0183687320, -0.0183687320],\n", " [ 0.0228262535, 0.0179280450, -0.0179280450],\n", " [ 0.0228262535, -0.0179280450, 0.0179280450],\n", " ],\n", " \"forces_2\": \n", " [\n", " [ 0.0228136551, 0.0179285592, 0.0179285592],\n", " [ 0.0228136551, -0.0179285592, -0.0179285592],\n", " [ 0.0230645948, -0.0183682178, 0.0183682178],\n", " [ 0.0230645948, 0.0183682178, -0.0183682178],\n", " [-0.1025024252, 0.0000000000, 0.0000000000],\n", " [-0.0044662632, 0.0000000000, 0.0000000000],\n", " [ 0.0076060944, 0.0000000000, 0.0000000000],\n", " [ 0.0076060944, 0.0000000000, 0.0000000000],\n", " ]\n", "}\n", "\n", "phonopy_data = PhonopyData(preprocess_data=preprocess_data)\n", "phonopy_data.set_forces(dict_of_forces=dict_of_forces)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We may want to try to compute the band structure of the phonons directly." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "builder = PhonopyCalculation.get_builder()\n", "builder.code = load_code(\"phonopy@local_direct\") # WARNING! You may redefine this\n", "builder.phonopy_data = phonopy_data\n", "builder.parameters = Dict({\"band\":\"auto\"})" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Submit the calculation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "These are the final results:\n" ] }, { "data": { "text/plain": [ "{'output_parameters': ,\n", " 'phonon_bands': ,\n", " 'remote_folder': ,\n", " 'retrieved': }" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from aiida.engine import run_get_node\n", "\n", "results, node = run_get_node(builder)\n", "print(\"These are the final results:\")\n", "results" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Check the phonon dispersion." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "node.outputs.phonon_bands.show_mpl()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "It looks fine, isn't it?\n", "\n", "Actually, there is a subtle discrepancy. If one would compare this with experiments, a phonon mode seems to miss at $\\Gamma$! \n", "Try to answer the following question before continuing the tutorial.\n", "\n", "```{admonition} Question: Can you explain why? \n", ":class: tip dropdown\n", "\n", "The interpolation is missing the _long-range_ contributions. An analytical model can be devised to account for these interactions, often called **dipole-dipole** approximation. This accounts for up to the dipole expansion, meaning accounting for the dielectric and Born effective charges (the dipoles) tensors. Higher order contribution can be added, such as quadrupoles and octupoles (see e.g. [Royo et al., Phys. Rev. Lett., **125** (2020)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.217602)).\n", "```\n", "\n", "Let's now add the non-analytical contribution (NAC). Imagine we have already computed such quantities.\n", "\n", ":::{admonition} NAC calculation\n", ":class: tip dropdown\n", "\n", "The calculation of these quantities can be done in different ways. Usually DFPT implementations are used, or alternatively finite differences upon small electric fields.\n", ":::" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "phonopy_data_nac = phonopy_data.clone() # the `phonopy_data` is now immutable in AiiDA!\n", "\n", "born_charges = [\n", " [\n", " [+2.1576, 0, 0], \n", " [0, +2.1576, 0], \n", " [0, 0, +2.1576]\n", " ],\n", " [\n", " [-2.1576, 0, 0],\n", " [0, -2.1576, 0],\n", " [0, 0, -2.1576]\n", " ],\n", "]\n", "\n", "dielectric = [\n", " [9.4626, 0, 0],\n", " [0, 9.4626, 0],\n", " [0, 0, 9.4626],\n", "]\n", "\n", "phonopy_data_nac.set_born_charges(born_charges)\n", "phonopy_data_nac.set_dielectric(dielectric)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Does it have the NAC now? Let's ask!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phonopy_data_nac.has_nac_parameters()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Hurray!\n", "\n", "Now we can recompute the band structure and see if something changes." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "builder.phonopy_data = phonopy_data_nac\n", "\n", "results, node = run_get_node(builder)\n", "node.outputs.phonon_bands.show_mpl()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "A huge splitting at $\\Gamma$ appeared! This is often referred to as __LO-TO splitting__. \n", "\n", "If you want to learn more features, continue with the [next tutorial](./advanced.ipynb)." ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.8.10" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" } } }, "nbformat": 4, "nbformat_minor": 2 }