{
 "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; 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   -26.49772386684                   -0.22    8.0\n",
      "  2   -26.58857039266       -1.04       -0.62    1.0\n",
      "  3   -26.61293374496       -1.61       -1.41    2.0\n",
      "  4   -26.61326891107       -3.47       -2.06    2.0\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.79189789554                   -0.09    7.2\n",
      "  2   -26.23212407223       -0.36       -0.70    2.0\n",
      "  3   -26.23818615891       -2.22       -1.31    2.2\n",
      "  4   -26.23848539201       -3.52       -2.33    1.8\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78543898348                   -0.09    4.8\n",
      "  2   -26.23808615290       -0.34       -0.78    2.0\n",
      "  3   -26.25072608271       -1.90       -1.63    2.0\n",
      "  4   -26.25102642764       -3.52       -2.12    2.0\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.91133749421                   -0.11    5.3\n",
      "  2   -26.29242791504       -0.42       -0.76    1.8\n",
      "  3   -26.30833412402       -1.80       -1.73    2.2\n",
      "  4   -26.30842434089       -4.04       -2.59    2.0\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90248483086                   -0.11    3.7\n",
      "  2   -26.26447091153       -0.44       -0.71    1.8\n",
      "  3   -26.28540694015       -1.68       -1.61    2.1\n",
      "  4   -26.28569788748       -3.54       -2.19    2.0\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.87450371992                   -0.10    5.3\n",
      "  2   -26.27175325282       -0.40       -0.76    1.8\n",
      "  3   -26.28807034743       -1.79       -1.70    2.3\n",
      "  4   -26.28818519062       -3.94       -2.50    2.0\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89710572559                   -0.11    3.4\n",
      "  2   -26.27704828817       -0.42       -0.74    1.9\n",
      "  3   -26.29414270874       -1.77       -1.73    2.0\n",
      "  4   -26.29420753960       -4.19       -2.64    2.0\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   -25.57984968773                   -0.16    3.4\n",
      "  2   -25.77602420711       -0.71       -0.76    1.3\n",
      "  3   -25.78624377070       -1.99       -1.84    2.0\n",
      "  4   -25.78631891351       -4.12       -2.84    2.0\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.78666165898                   -0.12    3.5\n",
      "  2   -26.07614286497       -0.54       -0.71    1.6\n",
      "  3   -26.09344037681       -1.76       -1.69    2.0\n",
      "  4   -26.09373864723       -3.53       -2.29    2.0\n",
      "  5   -26.09375500527       -4.79       -2.79    2.2\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.86776576473                   -0.11    3.5\n",
      "  2   -26.20703223593       -0.47       -0.71    1.8\n",
      "  3   -26.22669020921       -1.71       -1.63    2.0\n",
      "  4   -26.22699757807       -3.51       -2.21    2.0\n",
      "  5   -26.22702770114       -4.52       -2.83    2.5\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.89692352050                   -0.11    3.7\n",
      "  2   -26.25482157672       -0.45       -0.71    1.9\n",
      "  3   -26.27557083068       -1.68       -1.62    2.1\n",
      "  4   -26.27586241434       -3.54       -2.20    2.0\n",
      "  5   -26.27589487364       -4.49       -2.90    2.4\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90511498279                   -0.11    3.8\n",
      "  2   -26.27016916239       -0.44       -0.71    2.0\n",
      "  3   -26.29099903411       -1.68       -1.61    2.2\n",
      "  4   -26.29127368191       -3.56       -2.20    2.0\n",
      "  5   -26.29130575979       -4.49       -2.89    2.4\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90778241670                   -0.11    3.8\n",
      "  2   -26.27465663076       -0.44       -0.71    2.0\n",
      "  3   -26.29530984919       -1.69       -1.62    2.2\n",
      "  4   -26.29557643549       -3.57       -2.20    2.0\n",
      "  5   -26.29560731679       -4.51       -2.88    2.7\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90842771068                   -0.11    3.8\n",
      "  2   -26.27547242141       -0.44       -0.71    2.0\n",
      "  3   -26.29614378633       -1.68       -1.62    2.3\n",
      "  4   -26.29640692330       -3.58       -2.20    2.2\n",
      "  5   -26.29643696000       -4.52       -2.87    2.4\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag\n",
      "---   ---------------   ---------   ---------   ----\n",
      "  1   -25.90859813004                   -0.11    3.8\n",
      "  2   -26.27590783792       -0.43       -0.71    2.0\n",
      "  3   -26.29623160886       -1.69       -1.63    2.2\n",
      "  4   -26.29648644011       -3.59       -2.22    2.4\n",
      "  5   -26.29651280192       -4.58       -2.86    2.4\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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