{
 "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   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -26.49634914704                   -0.22    8.0         \n",
      "  2   -26.58841313409       -1.04       -0.62    1.0   55.2ms\n",
      "  3   -26.61295542499       -1.61       -1.41    2.0   26.8ms\n",
      "  4   -26.61323707557       -3.55       -2.04    1.0   23.6ms\n",
      "nkpt = 2 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.79162959067                   -0.09    7.2         \n",
      "  2   -26.23211854084       -0.36       -0.70    2.0   84.9ms\n",
      "  3   -26.23818207792       -2.22       -1.31    2.0    101ms\n",
      "  4   -26.23847850046       -3.53       -2.33    1.0   70.3ms\n",
      "nkpt = 3 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78541142788                   -0.09    4.8         \n",
      "  2   -26.23814848735       -0.34       -0.78    2.0   84.6ms\n",
      "  3   -26.25073318183       -1.90       -1.63    2.0   88.7ms\n",
      "  4   -26.25103724968       -3.52       -2.16    1.2   84.3ms\n",
      "nkpt = 4 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.91148069698                   -0.11    5.2         \n",
      "  2   -26.29265049767       -0.42       -0.76    1.8    156ms\n",
      "  3   -26.30834304052       -1.80       -1.74    2.3    183ms\n",
      "  4   -26.30842616173       -4.08       -2.71    1.1    132ms\n",
      "nkpt = 5 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90223254558                   -0.11    3.7         \n",
      "  2   -26.26470422876       -0.44       -0.71    2.0    164ms\n",
      "  3   -26.28542111944       -1.68       -1.61    2.1    174ms\n",
      "  4   -26.28570524902       -3.55       -2.23    1.8    159ms\n",
      "nkpt = 6 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.87443250946                   -0.10    5.2         \n",
      "  2   -26.27177313140       -0.40       -0.76    1.8    282ms\n",
      "  3   -26.28806918113       -1.79       -1.70    2.2    330ms\n",
      "  4   -26.28818662917       -3.93       -2.59    1.1    233ms\n",
      "nkpt = 7 Ecut = 17.0\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89695161429                   -0.11    3.5         \n",
      "  2   -26.27709396454       -0.42       -0.74    1.9    297ms\n",
      "  3   -26.29414573998       -1.77       -1.74    2.0    324ms\n",
      "  4   -26.29420698985       -4.21       -2.68    1.0    228ms\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   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.57894309932                   -0.16    3.4         \n",
      "  2   -25.77640882211       -0.70       -0.76    1.3   72.6ms\n",
      "  3   -25.78625548682       -2.01       -1.85    2.0   90.4ms\n",
      "  4   -25.78631741267       -4.21       -2.86    1.0   83.4ms\n",
      "nkpt = 5 Ecut = 12\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.78674225052                   -0.12    3.5         \n",
      "  2   -26.07606991000       -0.54       -0.71    1.7   80.0ms\n",
      "  3   -26.09343233084       -1.76       -1.69    2.0    108ms\n",
      "  4   -26.09374011825       -3.51       -2.32    1.6   78.3ms\n",
      "  5   -26.09375405180       -4.86       -2.72    1.9   87.5ms\n",
      "nkpt = 5 Ecut = 14\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.86788758953                   -0.11    3.5         \n",
      "  2   -26.20691097778       -0.47       -0.71    1.9   67.4ms\n",
      "  3   -26.22668804064       -1.70       -1.63    2.0   85.6ms\n",
      "  4   -26.22700144167       -3.50       -2.24    1.7   65.5ms\n",
      "  5   -26.22702625723       -4.61       -2.72    1.9   73.3ms\n",
      "nkpt = 5 Ecut = 16\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.89672650519                   -0.11    3.7         \n",
      "  2   -26.25497718897       -0.45       -0.71    1.9    161ms\n",
      "  3   -26.27557447357       -1.69       -1.62    2.1    175ms\n",
      "  4   -26.27586792463       -3.53       -2.23    2.0    168ms\n",
      "  5   -26.27589367768       -4.59       -2.78    1.9    164ms\n",
      "nkpt = 5 Ecut = 18\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90549425228                   -0.11    3.7         \n",
      "  2   -26.27006094425       -0.44       -0.71    1.9    162ms\n",
      "  3   -26.29098801544       -1.68       -1.61    2.2    177ms\n",
      "  4   -26.29127679721       -3.54       -2.22    1.7    157ms\n",
      "  5   -26.29130427038       -4.56       -2.76    2.0    173ms\n",
      "nkpt = 5 Ecut = 20\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90782358244                   -0.11    3.8         \n",
      "  2   -26.27443305385       -0.44       -0.71    2.0    163ms\n",
      "  3   -26.29529615014       -1.68       -1.61    2.2    186ms\n",
      "  4   -26.29557873635       -3.55       -2.22    1.8    157ms\n",
      "  5   -26.29560588099       -4.57       -2.76    1.9    171ms\n",
      "nkpt = 5 Ecut = 22\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90844850868                   -0.11    3.9         \n",
      "  2   -26.27545120948       -0.44       -0.71    2.0    172ms\n",
      "  3   -26.29614219593       -1.68       -1.62    2.1    183ms\n",
      "  4   -26.29641218463       -3.57       -2.23    2.0    174ms\n",
      "  5   -26.29643560942       -4.63       -2.75    2.0    176ms\n",
      "nkpt = 5 Ecut = 24\n",
      "n     Energy            log10(ΔE)   log10(Δρ)   Diag   Δtime\n",
      "---   ---------------   ---------   ---------   ----   ------\n",
      "  1   -25.90850226584                   -0.11    4.5         \n",
      "  2   -26.27583923738       -0.43       -0.71    2.0    174ms\n",
      "  3   -26.29623451977       -1.69       -1.63    2.1    181ms\n",
      "  4   -26.29649217282       -3.59       -2.26    2.0    174ms\n",
      "  5   -26.29651144072       -4.72       -2.74    2.0    180ms\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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