{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature spacing for replica exchange molecular dynamics simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks is to generate the temperature spacing for efficeint swaping in replica exchange molecular dynamics method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replica Exchange Molecular Dynamics (REMD)\n",
    "\n",
    "1. Enhance sampling method: overcomes the energy barriers and allows the system to sample a wider range of configurations.\n",
    "2. NOT a TRUE Molecular dynamics simulation its just a “sampling method” because parallel tempering exchange is a unphysical move.\n",
    "3. Sampling can be done on any intensive variables (Temperature, density, ..)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Detailed Balanced Condition:\n",
    "Acceptance probability of a swap between two neighbouring temperatures\n",
    "\n",
    "$acc = P(T_1 \\rightarrow T_2) = min(1, \\exp((\\beta_i -\\beta_j) (U_i - U_j))$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions ?\n",
    "\n",
    "1. How many replicas do we need to perform REMD?\n",
    "2. What should be the temperature spacing between the replicas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set global parameters\n",
    "plt.rcParams['figure.figsize'] = (8, 6)  # Figure size in inches (width, height)\n",
    "# plt.rcParams['figure.dpi'] = 150  # Figure resolution in dots per inch\n",
    "plt.rcParams['font.family'] = 'sans-serif'  # Font family\n",
    "plt.rcParams['font.sans-serif'] = 'Fira Sans'\n",
    "plt.rcParams['font.size'] = 16  # Font size\n",
    "plt.rcParams['axes.labelsize'] = 10  # Font size of x and y labels\n",
    "plt.rcParams['axes.titlesize'] = 18  # Font size of title\n",
    "plt.rcParams['axes.grid'] = True  # Show grid by default\n",
    "plt.rcParams['lines.linewidth'] = 6  # Line width\n",
    "plt.rcParams['lines.markersize'] = 10  # Marker size\n",
    "plt.rcParams['legend.fontsize'] = 16  # Font size of legend\n",
    "\n",
    "import logging\n",
    "logging.getLogger('matplotlib.font_manager').setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the working directory where you have i-pi generated short remd data\n",
    "os.chdir('./short_remd_coefficient_search/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one needs to sort the output from i-pi in accordance with the temperature\n",
    "# !python3 remdsort.py input_remd.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading all the outputs of the replicas\n",
    "\n",
    "properties = {}\n",
    "for file in os.listdir():\n",
    "    if file.endswith(\"_simulation.out\") & file.startswith(\"SRT\"):\n",
    "        idx = int(file.split('_')[1])\n",
    "        data = np.loadtxt(file)\n",
    "        properties[idx] = data\n",
    "    else :\n",
    "        continue\n",
    "properties = {k: v for k, v in sorted(properties.items(), key=lambda item: item[0])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deriving the constants for temperature spacing alorigthm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "kb = 8.617333262145e-5 # Boltzmann constant in eV/K \n",
    "Natoms = 27 # 3x3 1H-TaS2 supercell\n",
    "Ndf = 3*Natoms-3 # degrees of freedom "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using last 10ps of trajectory to compute ensemble averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_potentials = np.array([np.mean(v[-10000:,6]) for k, v in properties.items()])\n",
    "ensemble_temps = np.array([np.mean(v[-10000:,9]) for k, v in properties.items()])\n",
    "observed_temps = np.array([np.mean(v[-10000:,3]) for k, v in properties.items()])\n",
    "std_potentials = np.array([np.std(v[-10000:,6]) for k, v in properties.items()])/np.sqrt(Ndf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble temperatures: [ 50.  53.  56.  59.  62.  65.  68.  71.  74.  77.  80.  83.  86.  89.\n",
      "  92.  95.  98. 101. 104. 107. 110.]\n",
      "\n",
      "Observed temperatures: [ 50.26108535  53.20330219  56.25668929  59.20290569  62.27423155\n",
      "  65.17519428  67.91214192  71.20242199  74.01436144  76.92244628\n",
      "  79.98700373  82.81899827  85.86762277  88.79839426  91.50337063\n",
      "  94.81545514  97.99874072 100.58586705 103.75503566 106.71843229\n",
      " 109.8139592 ]\n"
     ]
    }
   ],
   "source": [
    "print(f'Ensemble temperatures: {ensemble_temps}\\n')\n",
    "print(f'Observed temperatures: {observed_temps}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing constants:\n",
    "\n",
    "$\\mu$ = $(B_{0} + B_{1}.T)N_{atoms}$ - $3.k_{B}.T$ \\\n",
    "$\\sigma$ = $(D_{0} + D_{1}.T)\\sqrt{N_{df}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope B_1: 0.0001261751193934205 eV/K, Intercept B_0: -152996.29528628185 eV, R-squared: 0.9999451545563839\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_avg_potentials = (avg_potentials + 3 * kb * ensemble_temps)/Natoms \n",
    "plt.figure()\n",
    "plt.title('Mean Potential Energy vs Ensemble Temperature')\n",
    "plt.plot(ensemble_temps, scaled_avg_potentials ,'o--', color = 'k')\n",
    "plt.xlabel('Ensemble Temperature (K)')\n",
    "plt.ylabel(r'($\\mu$+$3.k_{B}.T$) / $Natoms$ (eV)')\n",
    "slope, intercept, r_value, p_value, std_err = linregress(ensemble_temps, scaled_avg_potentials)\n",
    "B0, B1 = intercept, slope\n",
    "print(f'Slope B_1: {slope} eV/K, Intercept B_0: {intercept} eV, R-squared: {r_value**2}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope D_1: 5.800812914842667e-05 eV/K, Intercept D_0: 9.350154882901713e-05 eV, R-squared: 0.998153170144406\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Standard Deviation of Potential Energy vs Ensemble Temperature')\n",
    "plt.plot(ensemble_temps, std_potentials ,'o--', color = 'k')\n",
    "plt.xlabel('Ensemble Temperature (K)')\n",
    "plt.ylabel(r'$\\sigma$/$\\sqrt{N_{df}}$ (eV)')\n",
    "slope, intercept, r_value, p_value, std_err = linregress(ensemble_temps, std_potentials)\n",
    "D0, D1 = intercept, slope\n",
    "print(f'Slope D_1: {slope} eV/K, Intercept D_0: {intercept} eV, R-squared: {r_value**2}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acceptance probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean and std. deviation for difference in potential energy\n",
    "\n",
    "$\\mu_{12} = B_{1}(T_{2}-T_{1})N_{atoms} - 3k_{B}(T_{2}-T_{1})$ \\\n",
    "$\\sigma_{12} = D_{1}(T_{2}-T_{1})\\sqrt{N_{df}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.special import erf\n",
    "from scipy.optimize import root_scalar\n",
    "\n",
    "class ComputeTempSpacings:\n",
    "    def __init__(self, B0, B1, D0, D1, T1, Natoms, Ndf, kb):\n",
    "        self.B0 = B0\n",
    "        self.B1 = B1\n",
    "        self.D0 = D0\n",
    "        self.D1 = D1\n",
    "        self.T1 = T1\n",
    "        self.Natoms = Natoms\n",
    "        self.Ndf = Ndf\n",
    "        self.kb = kb\n",
    "\n",
    "    def mu(self, T2):\n",
    "        return self.B1 * (T2 - self.T1) * self.Natoms - 3 * self.kb * (T2 - self.T1)\n",
    "\n",
    "    def sigma(self, T2):\n",
    "        return self.D1 * np.sqrt(self.Ndf) * (T2 - self.T1)\n",
    "\n",
    "    def C(self, T2):\n",
    "        return 1/(self.kb * T2) - 1/(self.kb * self.T1)\n",
    "\n",
    "    def prob(self, T2):\n",
    "        mu_val = self.mu(T2)\n",
    "        sigma_val = self.sigma(T2)\n",
    "        C_val = self.C(T2)\n",
    "        \n",
    "        # for numerical stability we use log probabilities\n",
    "        log_one = np.log(0.5) + np.log1p(erf(-mu_val / (np.sqrt(2) * sigma_val)))\n",
    "        log_second = np.log(0.5) + C_val * mu_val + (C_val**2 * sigma_val**2) / 2\n",
    "        log_third = np.log1p(erf((mu_val + C_val * sigma_val**2) / np.sqrt(2 * sigma_val**2)))\n",
    "        \n",
    "        log_prob = np.logaddexp(log_one, log_second + log_third)\n",
    "        return np.exp(log_prob)\n",
    "\n",
    "    def find_T2(self, target_prob, T2_guess_low, T2_guess_high):\n",
    "        def equation(T2):\n",
    "            return self.prob(T2) - target_prob\n",
    "        \n",
    "        solution = root_scalar(equation, bracket=[T2_guess_low, T2_guess_high], method='brentq')\n",
    "        \n",
    "        if solution.converged:\n",
    "            return solution.root\n",
    "        else:\n",
    "            raise ValueError(\"Solution did not converge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of atoms: 1404\n",
      "Degrees of freedom: 4209\n",
      "Values of constants: B0=-152996.29528628185 eV, B1=0.0001261751193934205 eV/K, D0=9.350154882901713e-05 eV, D1=5.800812914842667e-05 eV/K\n",
      "Inital temperature: 50 K\tFinal temperature: 150 K\n",
      "Target acceptance probability for swapping (T1-T2): 0.25\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Natoms = 1404 # This need to be changed for required system; here (6xroot13) 1T-TaS2 supercell\n",
    "Ndf = 3*Natoms-3 # degrees of freedom \n",
    "B0, B1, D0, D1 = B0, B1, D0, D1\n",
    "T1, T_final = 50, 150 # initial and final temperatures \n",
    "target_prob = 0.25 # target acceptance probability for swapping (T1-T2)\n",
    "T2 = 0 # just a placeholder\n",
    "\n",
    "T1s, T2s, delta_Ts = [], [], []\n",
    "\n",
    "print(f\"Total number of atoms: {Natoms}\")\n",
    "print(f\"Degrees of freedom: {Ndf}\")\n",
    "print(f\"Values of constants: B0={B0} eV, B1={B1} eV/K, D0={D0} eV, D1={D1} eV/K\")\n",
    "print(f\"Inital temperature: {T1} K\\tFinal temperature: {T_final} K\")\n",
    "print(f\"Target acceptance probability for swapping (T1-T2): {target_prob}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting temperature spacing search...\n",
      "Acceptance probability: 0.25\tT1: 50.00 K\tT2: 51.32 K\tDelta T: 1.32 K\n",
      "Acceptance probability: 0.25\tT1: 51.32 K\tT2: 52.67 K\tDelta T: 1.35 K\n",
      "Acceptance probability: 0.25\tT1: 52.67 K\tT2: 54.05 K\tDelta T: 1.39 K\n",
      "Acceptance probability: 0.25\tT1: 54.05 K\tT2: 55.48 K\tDelta T: 1.42 K\n",
      "Acceptance probability: 0.25\tT1: 55.48 K\tT2: 56.94 K\tDelta T: 1.46 K\n",
      "Acceptance probability: 0.25\tT1: 56.94 K\tT2: 58.44 K\tDelta T: 1.50 K\n",
      "Acceptance probability: 0.25\tT1: 58.44 K\tT2: 59.98 K\tDelta T: 1.54 K\n",
      "Acceptance probability: 0.25\tT1: 59.98 K\tT2: 61.56 K\tDelta T: 1.58 K\n",
      "Acceptance probability: 0.25\tT1: 61.56 K\tT2: 63.18 K\tDelta T: 1.62 K\n",
      "Acceptance probability: 0.25\tT1: 63.18 K\tT2: 64.84 K\tDelta T: 1.66 K\n",
      "Acceptance probability: 0.25\tT1: 64.84 K\tT2: 66.55 K\tDelta T: 1.71 K\n",
      "Acceptance probability: 0.25\tT1: 66.55 K\tT2: 68.30 K\tDelta T: 1.75 K\n",
      "Acceptance probability: 0.25\tT1: 68.30 K\tT2: 70.10 K\tDelta T: 1.80 K\n",
      "Acceptance probability: 0.25\tT1: 70.10 K\tT2: 71.94 K\tDelta T: 1.85 K\n",
      "Acceptance probability: 0.25\tT1: 71.94 K\tT2: 73.84 K\tDelta T: 1.89 K\n",
      "Acceptance probability: 0.25\tT1: 73.84 K\tT2: 75.78 K\tDelta T: 1.94 K\n",
      "Acceptance probability: 0.25\tT1: 75.78 K\tT2: 77.78 K\tDelta T: 2.00 K\n",
      "Acceptance probability: 0.25\tT1: 77.78 K\tT2: 79.83 K\tDelta T: 2.05 K\n",
      "Acceptance probability: 0.25\tT1: 79.83 K\tT2: 81.93 K\tDelta T: 2.10 K\n",
      "Acceptance probability: 0.25\tT1: 81.93 K\tT2: 84.09 K\tDelta T: 2.16 K\n",
      "Acceptance probability: 0.25\tT1: 84.09 K\tT2: 86.30 K\tDelta T: 2.21 K\n",
      "Acceptance probability: 0.25\tT1: 86.30 K\tT2: 88.57 K\tDelta T: 2.27 K\n",
      "Acceptance probability: 0.25\tT1: 88.57 K\tT2: 90.90 K\tDelta T: 2.33 K\n",
      "Acceptance probability: 0.25\tT1: 90.90 K\tT2: 93.30 K\tDelta T: 2.39 K\n",
      "Acceptance probability: 0.25\tT1: 93.30 K\tT2: 95.75 K\tDelta T: 2.46 K\n",
      "Acceptance probability: 0.25\tT1: 95.75 K\tT2: 98.28 K\tDelta T: 2.52 K\n",
      "Acceptance probability: 0.25\tT1: 98.28 K\tT2: 100.86 K\tDelta T: 2.59 K\n",
      "Acceptance probability: 0.25\tT1: 100.86 K\tT2: 103.52 K\tDelta T: 2.66 K\n",
      "Acceptance probability: 0.25\tT1: 103.52 K\tT2: 106.25 K\tDelta T: 2.73 K\n",
      "Acceptance probability: 0.25\tT1: 106.25 K\tT2: 109.04 K\tDelta T: 2.80 K\n",
      "Acceptance probability: 0.25\tT1: 109.04 K\tT2: 111.91 K\tDelta T: 2.87 K\n",
      "Acceptance probability: 0.25\tT1: 111.91 K\tT2: 114.86 K\tDelta T: 2.95 K\n",
      "Acceptance probability: 0.25\tT1: 114.86 K\tT2: 117.89 K\tDelta T: 3.02 K\n",
      "Acceptance probability: 0.25\tT1: 117.89 K\tT2: 120.99 K\tDelta T: 3.10 K\n",
      "Acceptance probability: 0.25\tT1: 120.99 K\tT2: 124.18 K\tDelta T: 3.19 K\n",
      "Acceptance probability: 0.25\tT1: 124.18 K\tT2: 127.45 K\tDelta T: 3.27 K\n",
      "Acceptance probability: 0.25\tT1: 127.45 K\tT2: 130.80 K\tDelta T: 3.36 K\n",
      "Acceptance probability: 0.25\tT1: 130.80 K\tT2: 134.25 K\tDelta T: 3.44 K\n",
      "Acceptance probability: 0.25\tT1: 134.25 K\tT2: 137.78 K\tDelta T: 3.53 K\n",
      "Acceptance probability: 0.25\tT1: 137.78 K\tT2: 141.41 K\tDelta T: 3.63 K\n",
      "Acceptance probability: 0.25\tT1: 141.41 K\tT2: 145.13 K\tDelta T: 3.72 K\n",
      "Acceptance probability: 0.25\tT1: 145.13 K\tT2: 148.95 K\tDelta T: 3.82 K\n",
      "Acceptance probability: 0.25\tT1: 148.95 K\tT2: 152.87 K\tDelta T: 3.92 K\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15522/1430485309.py:31: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  log_one = np.log(0.5) + np.log1p(erf(-mu_val / (np.sqrt(2) * sigma_val)))\n",
      "/tmp/ipykernel_15522/1430485309.py:33: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  log_third = np.log1p(erf((mu_val + C_val * sigma_val**2) / np.sqrt(2 * sigma_val**2)))\n",
      "/tmp/ipykernel_15522/1430485309.py:35: RuntimeWarning: invalid value encountered in logaddexp\n",
      "  log_prob = np.logaddexp(log_one, log_second + log_third)\n",
      "/tmp/ipykernel_15522/1430485309.py:31: RuntimeWarning: divide by zero encountered in log1p\n",
      "  log_one = np.log(0.5) + np.log1p(erf(-mu_val / (np.sqrt(2) * sigma_val)))\n",
      "/tmp/ipykernel_15522/1430485309.py:33: RuntimeWarning: divide by zero encountered in log1p\n",
      "  log_third = np.log1p(erf((mu_val + C_val * sigma_val**2) / np.sqrt(2 * sigma_val**2)))\n"
     ]
    }
   ],
   "source": [
    "# change the directory where you want to save the temperature spacing search log\n",
    "\n",
    "os.chdir('/classical_remd/')\n",
    "print(\"Starting temperature spacing search...\")\n",
    "with open('temperature_spacing_search.log', 'w') as f:\n",
    "    f.write(f\"Total number of atoms: {Natoms}\\n\")\n",
    "    f.write(f\"Degrees of freedom: {Ndf}\\n\")\n",
    "    f.write(f\"Values of constants: B0={B0} eV, B1={B1} eV/K, D0={D0} eV, D1={D1} eV/K\\n\")\n",
    "    f.write(f\"Inital temperature: {T1} K\\tFinal temperature: {T_final} K\\n\")\n",
    "    f.write(f\"Target acceptance probability for swapping (T1-T2): {target_prob}\\n\\n\")\n",
    "    \n",
    "    while T2 < T_final:\n",
    "        model = ComputeTempSpacings(B0, B1, D0, D1, T1, Natoms, Ndf, kb)\n",
    "        try:\n",
    "            T2 = model.find_T2(target_prob, T1, 1000)\n",
    "            T1s.append(T1)\n",
    "            T2s.append(T2)\n",
    "            delta_Ts.append(T2 - T1)\n",
    "            print(f\"Acceptance probability: {model.prob(T2):.2f}\\tT1: {T1:.2f} K\\tT2: {T2:.2f} K\\tDelta T: {T2 - T1:.2f} K\")\n",
    "            f.write(f\"Acceptance probability: {model.prob(T2):.2f}\\tT1: {T1:.2f} K\\tT2: {T2:.2f} K\\tDelta T: {T2 - T1:.2f} K\\n\")\n",
    "        except ValueError as e:\n",
    "            print(e)\n",
    "        T1 = T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43,)\n"
     ]
    }
   ],
   "source": [
    "print(np.asarray(T2s).shape)"
   ]
  }
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