{ "cells": [ { "cell_type": "markdown", "id": "f8a89516-a98c-4586-a33c-28fa63e399f7", "metadata": {}, "source": [ "\n", "# Wannier localization" ] }, { "cell_type": "code", "execution_count": 1, "id": "3cd6e4e0", "metadata": {}, "outputs": [], "source": [ "import pathlib\n", "\n", "import numpy as np\n", "\n", "from eminus import Atoms, read, SCF\n", "from eminus.extras import view\n", "from eminus.localizer import get_wannier, wannier_cost\n", "from eminus.orbitals import FLO, SCDM, WO\n", "from eminus.tools import orbital_center" ] }, { "cell_type": "code", "execution_count": 2, "id": "56855ec6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XYZ file comment: \"Experimental geometry from CCCBDB: https://cccbdb.nist.gov/exp2x.asp?casno=74828&charge=0\"\n", "Start auto minimization...\n", "Method Iteration Etot [Eh] dEtot [Eh] |Gradient| \n", "pccg 1 +18.061427 \n", "pccg 2 +7.881450 -1.0180e+01 [+1.05e+04] \n", "pccg 3 +0.099586 -7.7819e+00 [+1.87e+03] \n", "pccg 4 -4.096798 -4.1964e+00 [+3.10e+02] \n", "pccg 5 -6.145812 -2.0490e+00 [+5.75e+01] \n", "pccg 6 -6.874551 -7.2874e-01 [+1.02e+01] \n", "pccg 7 -7.285784 -4.1123e-01 [+2.95e+00] \n", "pccg 8 -7.577908 -2.9212e-01 [+3.02e+00] \n", "pccg 9 -7.787117 -2.0921e-01 [+1.35e+00] \n", "pccg 10 -7.857628 -7.0510e-02 [+2.89e-01] \n", "pccg 11 -7.875693 -1.8065e-02 [+6.94e-02] \n", "pccg 12 -7.879606 -3.9137e-03 [+1.64e-02] \n", "pccg 13 -7.880591 -9.8423e-04 [+4.26e-03] \n", "pccg 14 -7.880813 -2.2256e-04 [+9.53e-04] \n", "pccg 15 -7.880863 -4.9429e-05 [+2.08e-04] \n", "pccg 16 -7.880875 -1.2336e-05 [+5.59e-05] \n", "pccg 17 -7.880877 -2.2595e-06 [+1.01e-05] \n", "pccg 18 -7.880878 -5.3147e-07 [+2.19e-06] \n", "pccg 19 -7.880878 -1.4534e-07 [+6.02e-07] \n", "pccg 20 -7.880878 -3.6440e-08 [+1.55e-07] \n", "SCF converged after 20 iterations.\n", "Total SCF time: 5.91427 s\n", "Etot = -7.880877999 Eh\n" ] } ], "source": [ "# Run an initial calculation for methane\n", "atoms = Atoms(*read(\"CH4.xyz\"), ecut=15, center=True)\n", "scf = SCF(atoms)\n", "scf.run();" ] }, { "cell_type": "code", "execution_count": 3, "id": "020cf835", "metadata": {}, "outputs": [], "source": [ "# Calculate the SCDMs to have pre-localized orbitals\n", "scdm = SCDM(scf)" ] }, { "cell_type": "code", "execution_count": 4, "id": "47504e58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wannier localizer converged after 15 iterations.\n" ] } ], "source": [ "# Do the Wannier localization\n", "# The resulting orbitals are equivalent to Foster-Boys orbitals, but with periodic boundary conditions\n", "wannier = get_wannier(atoms, scdm)" ] }, { "cell_type": "code", "execution_count": 5, "id": "8f27bb24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Costs:\n", "[2.60730048 2.66311286 2.71520219 2.76391029]\n", "\n", "SCDM spreads = [[2.60730048 2.66311286 2.71520219 2.76391029]]\n", "SCDM spread = 10.74952581703485\n" ] } ], "source": [ "# Compare the initial SCDM spreads to the Wannier spreads\n", "scdm_spreads = wannier_cost(atoms, scdm)\n", "print(f\"\\nSCDM spreads = {scdm_spreads}\")\n", "print(f\"SCDM spread = {np.sum(scdm_spreads)}\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "a0632df3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Costs:\n", "[2.6820577 2.68206874 2.68206274 2.68211308]\n", "Wannier spreads = [[2.6820577 2.68206874 2.68206274 2.68211308]]\n", "Wannier spread = 10.728302260338978\n" ] } ], "source": [ "# The Wannier orbitals are a bit more localized, and all orbitals are evenly localized\n", "wannier_spreads = wannier_cost(atoms, wannier)\n", "print(f\"Wannier spreads = {wannier_spreads}\")\n", "print(f\"Wannier spread = {np.sum(wannier_spreads)}\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "58a92a77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wannier localizer converged after 15 iterations.\n", "Write CH4_WO_k0_0.cube...\n", "Write CH4_WO_k0_1.cube...\n", "Write CH4_WO_k0_2.cube...\n", "Write CH4_WO_k0_3.cube...\n" ] } ], "source": [ "# All of the above can be done with one function call, also save the orbitals\n", "WO(scf, write_cubes=True);" ] }, { "cell_type": "code", "execution_count": 8, "id": "f36fbd6b", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d15beb1ab59c417aa0a9c47d84b66722", "version_major": 2, "version_minor": 0 }, "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "83d91141c949428aa360b1f8ec3187ec", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='filename', options=('CH4_WO_k0_2.cube', 'CH4_WO_k0_1.cube', 'CH4_W…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the orbitals from the cube files\n", "view(pathlib.Path().glob(\"*.cube\"));" ] }, { "cell_type": "code", "execution_count": 9, "id": "e9bb8078", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "black", "size": 0.1 }, "name": "Unit cell", "showlegend": false, "type": "scatter3d", "x": { "bdata": "AAAAAAAAAAAAAAAAAAAAAA==", "dtype": "f8" }, "y": { "bdata": "AAAAAAAAAAAAAAAAAAAAAA==", "dtype": "f8" }, "z": { "bdata": "AAAAAAAAAAAAAAAAAAA0QA==", "dtype": "f8" } }, { "marker": { "color": "black", "size": 0.1 }, "name": "Unit cell", "showlegend": false, "type": "scatter3d", "x": { "bdata": "AAAAAAAAAAAAAAAAAAAAAA==", "dtype": "f8" }, "y": { "bdata": "AAAAAAAAAAAAAAAAAAA0QA==", "dtype": "f8" }, "z": { "bdata": "AAAAAAAAAAAAAAAAAAAAAA==", "dtype": "f8" } }, { "marker": { "color": "black", "size": 0.1 }, "name": "Unit cell", "showlegend": false, "type": "scatter3d", "x": { "bdata": 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"constant", "title": { "text": "Selection" } }, "scene": { "aspectmode": "data", "xaxis": { "range": [ 0, 20 ], "title": { "text": "x [a0]" } }, "yaxis": { "range": [ 0, 20 ], "title": { "text": "y [a0]" } }, "zaxis": { "range": [ 0, 20 ], "title": { "text": "z [a0]" } } }, "template": { "data": { "scatter": [ { "type": "scatter" } ] } }, "width": 600 } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# One could calculate the center of masses from the Wannier orbitals...\n", "coms = orbital_center(atoms, wannier[0])\n", "atoms.view(coms)" ] }, { "cell_type": "code", "execution_count": 10, "id": "ba268f74", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Costs:\n", "[3.51558107 3.64121787 3.85319419 4.52338782]\n", "FLO spreads = [[3.51558107 3.64121787 3.85319419 4.52338782]]\n", "FLO spread = 15.533380945809824\n" ] } ], "source": [ "# ...and use them as an initial guess to create a set of FLOs\n", "flo = FLO(scf, fods=coms)\n", "flo_spreads = wannier_cost(atoms, flo)\n", "print(f\"FLO spreads = {flo_spreads}\")\n", "print(f\"FLO spread = {np.sum(flo_spreads)}\")" ] } ], "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", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "128a6250e40b481cabf0c6583e169152": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ButtonModel", "state": { "icon": "compress", "layout": "IPY_MODEL_2dab4c1803474f679f88b1a5121ab8d6", "style": "IPY_MODEL_ab259944960a4155bde43326f4fb956d", "tooltip": null } }, "2dab4c1803474f679f88b1a5121ab8d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", 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