{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stretched LDA H_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from CADMium import Psgrid\n",
    "from CADMium import Kohnsham\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Distance: 0.5 -> Energy: -0.4684324535052049\n",
      " Distance: 0.6551724137931034 -> Energy: -0.8083407156250979\n",
      " Distance: 0.8103448275862069 -> Energy: -0.97842718732902\n",
      " Distance: 0.9655172413793104 -> Energy: -1.0667404443615993\n",
      " Distance: 1.1206896551724137 -> Energy: -1.1115704089542473\n",
      " Distance: 1.2758620689655173 -> Energy: -1.1318202580552466\n",
      " Distance: 1.4310344827586208 -> Energy: -1.1376537973331131\n",
      " Distance: 1.5862068965517242 -> Energy: -1.1348780722421599\n",
      " Distance: 1.7413793103448276 -> Energy: -1.1269625395137237\n",
      " Distance: 1.896551724137931 -> Energy: -1.1160498352041444\n",
      " Distance: 2.0517241379310347 -> Energy: -1.1034954497077645\n",
      " Distance: 2.206896551724138 -> Energy: -1.090171017251243\n",
      " Distance: 2.3620689655172415 -> Energy: -1.0766420207752279\n",
      " Distance: 2.5172413793103448 -> Energy: -1.0632755819428226\n",
      " Distance: 2.6724137931034484 -> Energy: -1.0503077720307186\n",
      " Distance: 2.8275862068965516 -> Energy: -1.0378867213699405\n",
      " Distance: 2.9827586206896552 -> Energy: -1.0261008151622297\n",
      " Distance: 3.137931034482759 -> Energy: -1.0149974994056163\n",
      " Distance: 3.293103448275862 -> Energy: -1.0045960328984411\n",
      " Distance: 3.4482758620689657 -> Energy: -0.9948962656705309\n",
      " Distance: 3.603448275862069 -> Energy: -0.9858852452776176\n",
      " Distance: 3.7586206896551726 -> Energy: -0.9775397773469396\n",
      " Distance: 3.913793103448276 -> Energy: -0.9698318987930303\n",
      " Distance: 4.068965517241379 -> Energy: -0.9627296740026599\n",
      " Distance: 4.224137931034483 -> Energy: -0.9561991097679967\n",
      " Distance: 4.379310344827586 -> Energy: -0.950205315730581\n",
      " Distance: 4.5344827586206895 -> Energy: -0.9447133436002727\n",
      " Distance: 4.689655172413794 -> Energy: -0.9396887898775765\n",
      " Distance: 4.844827586206897 -> Energy: -0.9350982226710518\n",
      " Distance: 5.0 -> Energy: -0.9309094765957149\n",
      " Distance: 5.1 -> Energy: -0.9284085724229765\n",
      " Distance: 5.644444444444444 -> Energy: -0.917165411761904\n",
      " Distance: 6.188888888888888 -> Energy: -0.9091554695880649\n",
      " Distance: 6.733333333333333 -> Energy: -0.9034940552888145\n",
      " Distance: 7.277777777777778 -> Energy: -0.8995056933150725\n",
      " Distance: 7.822222222222222 -> Energy: -0.8966827070085016\n",
      " Distance: 8.366666666666667 -> Energy: -0.8946401351598964\n",
      " Distance: 8.911111111111111 -> Energy: -0.8930881023753587\n",
      " Distance: 9.455555555555556 -> Energy: -0.8917960645167057\n",
      " Distance: 10.0 -> Energy: -0.890566987826421\n"
     ]
    }
   ],
   "source": [
    "dis_eq      = np.linspace(0.5,5,30)\n",
    "dis_st      = np.linspace(5.1,10,10)\n",
    "distances   = np.concatenate((dis_eq, dis_st))\n",
    "lda_energy  = []\n",
    "\n",
    "Za, Zb = 1,1\n",
    "Nmo = [[1,1]]\n",
    "N   = [[1,1]]\n",
    "pol = 2\n",
    "NP, NM = 7, [4,4] \n",
    "optKS = {\"xc_family\"  : \"lda\",\n",
    "         \"xfunc_id\"   : 1,   \n",
    "         \"cfunc_id\"   : 12,\n",
    "         \"sym\"        : True,\n",
    "         \"fractional\" : False}\n",
    "\n",
    "\n",
    "for d in distances:\n",
    "    #Initialize Grid and Separation\n",
    "    a =  d/2\n",
    "    L = np.arccosh(10./a) \n",
    "    loc = np.array(range(-4,5))\n",
    "    grid = Psgrid(NP, NM, a, L, loc)\n",
    "    grid.initialize()\n",
    "\n",
    "    #Kohn Sham Object. SCF Calcualtion\n",
    "    KS = Kohnsham(grid, Za, Zb, pol, Nmo, N, optKS)\n",
    "    KS.scf({\"disp\" : False})\n",
    "    lda_energy.append(KS.E.E)\n",
    "    print(f\" Distance: {d} -> Energy: {KS.E.E}\")\n",
    "    \n",
    "# Store results\n",
    "energy    = np.array(lda_energy)\n",
    "np.save('h2_distance.npy', distances)\n",
    "np.save('h2_lda.npy', lda_energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcc42a164c0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_energy  = -0.5\n",
    "energy    = np.array(lda_energy)\n",
    "\n",
    "fig, ax = plt.subplots(1,1, dpi=75)\n",
    "\n",
    "ax.axhline(y=0, alpha=0.5, c=\"grey\", ls=\":\")\n",
    "ax.plot(distances, energy - 2*h_energy)\n",
    "# ax.set_ylim(-1.2,-0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}