{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Performing a convergence study\n",
    "\n",
    "This example shows how to perform a convergence study to find an appropriate\n",
    "discretisation parameters for the Brillouin zone (`kgrid`) and kinetic energy\n",
    "cutoff (`Ecut`), such that the simulation results are converged to a desired\n",
    "accuracy tolerance.\n",
    "\n",
    "Such a convergence study is generally performed by starting with a\n",
    "reasonable base line value for `kgrid` and `Ecut` and then increasing these\n",
    "parameters (i.e. using finer discretisations) until a desired property (such\n",
    "as the energy) changes less than the tolerance.\n",
    "\n",
    "This procedure must be performed for each discretisation parameter. Beyond\n",
    "the `Ecut` and the `kgrid` also convergence in the smearing temperature or\n",
    "other numerical parameters should be checked. For simplicity we will neglect\n",
    "this aspect in this example and concentrate on `Ecut` and `kgrid`. Moreover\n",
    "we will restrict ourselves to using the same number of $k$-points in each\n",
    "dimension of the Brillouin zone.\n",
    "\n",
    "As the objective of this study we consider bulk platinum. For running the SCF\n",
    "conveniently we define a function:"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using DFTK\n",
    "using LinearAlgebra\n",
    "using Statistics\n",
    "\n",
    "function run_scf(; a=5.0, Ecut, nkpt, tol)\n",
    "    atoms    = [ElementPsp(:Pt, psp = load_psp(\"hgh/lda/Pt-q10\"))]\n",
    "    position = [zeros(3)]\n",
    "    lattice  = a * Matrix(I, 3, 3)\n",
    "\n",
    "    model  = model_LDA(lattice, atoms, position; temperature=1e-2)\n",
    "    basis  = PlaneWaveBasis(model; Ecut, kgrid=(nkpt, nkpt, nkpt))\n",
    "    println(\"nkpt = $nkpt Ecut = $Ecut\")\n",
    "    self_consistent_field(basis; is_converged=DFTK.ScfConvergenceEnergy(tol))\n",
    "end;"
   ],
   "metadata": {},
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": [
    "Moreover we define some parameters. To make the calculations run fast for the\n",
    "automatic generation of this documentation we target only a convergence to\n",
    "1e-2. In practice smaller tolerances (and thus larger upper bounds for\n",
    "`nkpts` and `Ecuts` are likely needed."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "tol   = 1e-2      # Tolerance to which we target to converge\n",
    "nkpts = 1:7       # K-point range checked for convergence\n",
    "Ecuts = 10:2:24;  # Energy cutoff range checked for convergence"
   ],
   "metadata": {},
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "source": [
    "As the first step we converge in the number of $k$-points employed in each\n",
    "dimension of the Brillouin zone …"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkpt = 1 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -26.49707213503                   -0.22    8.0\n",
      "  2   -26.58885940341       -1.04       -0.62    1.0\n",
      "  3   -26.61293325597       -1.62       -1.41    2.0\n",
      "  4   -26.61325390199       -3.49       -2.06    2.0\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.79177006861                   -0.09    7.0\n",
      "  2   -26.23209937729       -0.36       -0.70    2.0\n",
      "  3   -26.23818463139       -2.22       -1.32    2.0\n",
      "  4   -26.23847860610       -3.53       -2.33    1.0\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78573965275                   -0.09    5.2\n",
      "  2   -26.23823108072       -0.34       -0.78    2.0\n",
      "  3   -26.25073683514       -1.90       -1.63    2.0\n",
      "  4   -26.25103744161       -3.52       -2.16    1.2\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.91129517408                   -0.11    5.3\n",
      "  2   -26.29254228862       -0.42       -0.76    1.7\n",
      "  3   -26.30833779731       -1.80       -1.73    2.3\n",
      "  4   -26.30842572439       -4.06       -2.69    1.1\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90223608917                   -0.11    3.8\n",
      "  2   -26.26453671399       -0.44       -0.71    1.9\n",
      "  3   -26.28541311018       -1.68       -1.61    2.1\n",
      "  4   -26.28570505619       -3.53       -2.23    1.8\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.87446019292                   -0.10    5.1\n",
      "  2   -26.27177097099       -0.40       -0.76    1.9\n",
      "  3   -26.28806902290       -1.79       -1.70    2.2\n",
      "  4   -26.28818661989       -3.93       -2.59    1.1\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89690098511                   -0.11    3.4\n",
      "  2   -26.27706164391       -0.42       -0.74    1.9\n",
      "  3   -26.29414419593       -1.77       -1.74    2.0\n",
      "  4   -26.29420692311       -4.20       -2.69    1.1\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "5"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "cell_type": "code",
   "source": [
    "function converge_kgrid(nkpts; Ecut, tol)\n",
    "    energies = [run_scf(; nkpt, tol=tol/10, Ecut).energies.total for nkpt in nkpts]\n",
    "    errors = abs.(energies[1:end-1] .- energies[end])\n",
    "    iconv = findfirst(errors .< tol)\n",
    "    (; nkpts=nkpts[1:end-1], errors, nkpt_conv=nkpts[iconv])\n",
    "end\n",
    "result = converge_kgrid(nkpts; Ecut=mean(Ecuts), tol)\n",
    "nkpt_conv = result.nkpt_conv"
   ],
   "metadata": {},
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "source": [
    "… and plot the obtained convergence:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "cell_type": "code",
   "source": [
    "using Plots\n",
    "plot(result.nkpts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,\n",
    "     xlabel=\"k-grid\", ylabel=\"energy absolute error\")"
   ],
   "metadata": {},
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "We continue to do the convergence in Ecut using the suggested $k$-point grid."
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nkpt = 5 Ecut = 10\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.57897815158                   -0.16    3.5\n",
      "  2   -25.77612130572       -0.71       -0.76    1.2\n",
      "  3   -25.78624648048       -1.99       -1.85    2.0\n",
      "  4   -25.78631709645       -4.15       -2.86    1.0\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78656968436                   -0.12    3.5\n",
      "  2   -26.07635904597       -0.54       -0.71    1.9\n",
      "  3   -26.09345433953       -1.77       -1.70    2.0\n",
      "  4   -26.09374188806       -3.54       -2.34    1.5\n",
      "  5   -26.09375431981       -4.91       -2.73    1.9\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.86773393476                   -0.11    3.5\n",
      "  2   -26.20702654613       -0.47       -0.71    1.8\n",
      "  3   -26.22668795804       -1.71       -1.63    2.0\n",
      "  4   -26.22700213372       -3.50       -2.24    1.7\n",
      "  5   -26.22702632183       -4.62       -2.72    1.9\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89690942917                   -0.11    3.7\n",
      "  2   -26.25485183976       -0.45       -0.71    1.9\n",
      "  3   -26.27557224223       -1.68       -1.62    2.1\n",
      "  4   -26.27586789398       -3.53       -2.23    1.9\n",
      "  5   -26.27589342788       -4.59       -2.77    1.7\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90519251700                   -0.11    3.7\n",
      "  2   -26.27015892047       -0.44       -0.71    1.9\n",
      "  3   -26.29099031392       -1.68       -1.61    2.1\n",
      "  4   -26.29127836318       -3.54       -2.23    1.9\n",
      "  5   -26.29130436189       -4.59       -2.77    1.8\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90792098987                   -0.11    4.0\n",
      "  2   -26.27465864725       -0.44       -0.71    2.0\n",
      "  3   -26.29531028730       -1.69       -1.62    2.2\n",
      "  4   -26.29558220156       -3.57       -2.23    1.9\n",
      "  5   -26.29560588172       -4.63       -2.75    1.9\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90834205773                   -0.11    3.8\n",
      "  2   -26.27547284911       -0.44       -0.71    2.0\n",
      "  3   -26.29614249136       -1.68       -1.62    2.2\n",
      "  4   -26.29641172746       -3.57       -2.23    1.8\n",
      "  5   -26.29643549595       -4.62       -2.75    1.9\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90850847778                   -0.11    4.0\n",
      "  2   -26.27570176579       -0.44       -0.71    2.0\n",
      "  3   -26.29621996750       -1.69       -1.63    2.2\n",
      "  4   -26.29648987392       -3.57       -2.24    2.0\n",
      "  5   -26.29651152802       -4.66       -2.75    1.9\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "18"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "cell_type": "code",
   "source": [
    "function converge_Ecut(Ecuts; nkpt, tol)\n",
    "    energies = [run_scf(; nkpt, tol=tol/100, Ecut).energies.total for Ecut in Ecuts]\n",
    "    errors = abs.(energies[1:end-1] .- energies[end])\n",
    "    iconv = findfirst(errors .< tol)\n",
    "    (; Ecuts=Ecuts[1:end-1], errors, Ecut_conv=Ecuts[iconv])\n",
    "end\n",
    "result = converge_Ecut(Ecuts; nkpt=nkpt_conv, tol)\n",
    "Ecut_conv = result.Ecut_conv"
   ],
   "metadata": {},
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": [
    "… and plot it:"
   ],
   "metadata": {}
  },
  {
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Plot{Plots.GRBackend() n=1}",
      "image/png": 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      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "cell_type": "code",
   "source": [
    "plot(result.Ecuts, result.errors, dpi=300, lw=3, m=:o, yaxis=:log,\n",
    "     xlabel=\"Ecut\", ylabel=\"energy absolute error\")"
   ],
   "metadata": {},
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "## A more realistic example.\n",
    "Repeating the above exercise for more realistic settings, namely …"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "tol   = 1e-4  # Tolerance to which we target to converge\n",
    "nkpts = 1:20  # K-point range checked for convergence\n",
    "Ecuts = 20:1:50;"
   ],
   "metadata": {},
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "…one obtains the following two plots for the convergence in `kpoints` and `Ecut`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img src=\"https://docs.dftk.org/stable/assets/convergence_study_kgrid.png\" width=600 height=400 />\n",
    "<img src=\"https://docs.dftk.org/stable/assets/convergence_study_ecut.png\"  width=600 height=400 />"
   ],
   "metadata": {}
  }
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