{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a GAP model\n",
    "\n",
    "## steps\n",
    "    1. generate a small dataset of water structures \n",
    "        - use CP2K if you havea access to it\n",
    "        - otherwise: use any simple potential implemented in ASE, just for trying this out I have used EMT here\n",
    "    1. generate e0 values\n",
    "    1. separate a training and a validation dataset\n",
    "    1. **train the model**\n",
    "    1. look at the outcome of the model\n",
    "    \n",
    "## here we will fit twice, to see the difference between a 2b-only and a 2b+3b fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:48:40.136302Z",
     "start_time": "2018-10-08T08:48:40.130282Z"
    }
   },
   "outputs": [],
   "source": [
    "# general imports \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy as cp\n",
    "\n",
    "# ase imports\n",
    "import ase.io\n",
    "from ase import Atoms, Atom\n",
    "from ase import units\n",
    "from ase.build import molecule\n",
    "# for MD\n",
    "from ase.md.langevin import Langevin\n",
    "from ase.io.trajectory import Trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:07:43.520727Z",
     "start_time": "2018-10-07T17:07:43.460810Z"
    }
   },
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def make_water(density, super_cell=[3, 3, 3]):\n",
    "    \"\"\" Geenrates a supercell of water molecules with a desired density.\n",
    "        Density in g/cm^3!!!\"\"\"\n",
    "    h2o = molecule('H2O')\n",
    "    a = np.cbrt((sum(h2o.get_masses()) * units.m ** 3 * 1E-6 ) / (density * units.mol))\n",
    "    h2o.set_cell((a, a, a))\n",
    "    h2o.set_pbc((True, True, True))\n",
    "    #return cp(h2o.repeat(super_cell))\n",
    "    return h2o.repeat(super_cell)\n",
    "\n",
    "def rms_dict(x_ref, x_pred):\n",
    "    \"\"\" Takes two datasets of the same shape and returns a dictionary containing RMS error data\"\"\"\n",
    "\n",
    "    x_ref = np.array(x_ref)\n",
    "    x_pred = np.array(x_pred)\n",
    "\n",
    "    if np.shape(x_pred) != np.shape(x_ref):\n",
    "        raise ValueError('WARNING: not matching shapes in rms')\n",
    "\n",
    "    error_2 = (x_ref - x_pred) ** 2\n",
    "\n",
    "    average = np.sqrt(np.average(error_2))\n",
    "    std_ = np.sqrt(np.var(error_2))\n",
    "\n",
    "    return {'rmse': average, 'std': std_}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## generating data only with ASE, using the EMT calculator\n",
    "\n",
    "This is only for the demonstration of how to do it, this run is will be done very fast. There is no practical use of the data beyond lerning the use teach_sparse, quip, etc. with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:50.964994Z",
     "start_time": "2018-10-07T17:07:43.523997Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy per atom: Epot = 0.885eV  Ekin = 0.000eV (T=  0K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.885eV  Ekin = 0.000eV (T=  0K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.820eV  Ekin = 0.053eV (T=413K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.660eV  Ekin = 0.208eV (T=1610K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.632eV  Ekin = 0.224eV (T=1733K)  Etot = 0.856eV\n",
      "Energy per atom: Epot = 0.869eV  Ekin = 0.005eV (T= 39K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.781eV  Ekin = 0.088eV (T=683K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.756eV  Ekin = 0.117eV (T=904K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.706eV  Ekin = 0.165eV (T=1274K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.703eV  Ekin = 0.159eV (T=1227K)  Etot = 0.861eV\n",
      "Energy per atom: Epot = 0.869eV  Ekin = 0.005eV (T= 40K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.693eV  Ekin = 0.171eV (T=1323K)  Etot = 0.864eV\n",
      "Energy per atom: Epot = 0.670eV  Ekin = 0.198eV (T=1529K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.821eV  Ekin = 0.052eV (T=402K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.775eV  Ekin = 0.093eV (T=721K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.804eV  Ekin = 0.068eV (T=524K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.745eV  Ekin = 0.126eV (T=975K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.740eV  Ekin = 0.129eV (T=996K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.779eV  Ekin = 0.093eV (T=721K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.780eV  Ekin = 0.094eV (T=728K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.746eV  Ekin = 0.125eV (T=967K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.743eV  Ekin = 0.127eV (T=980K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.720eV  Ekin = 0.149eV (T=1153K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.745eV  Ekin = 0.126eV (T=975K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.759eV  Ekin = 0.113eV (T=875K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.726eV  Ekin = 0.144eV (T=1114K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.722eV  Ekin = 0.148eV (T=1142K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.742eV  Ekin = 0.128eV (T=993K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.752eV  Ekin = 0.120eV (T=931K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.725eV  Ekin = 0.146eV (T=1129K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.718eV  Ekin = 0.153eV (T=1184K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.686eV  Ekin = 0.184eV (T=1423K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.662eV  Ekin = 0.207eV (T=1605K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.636eV  Ekin = 0.233eV (T=1801K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.619eV  Ekin = 0.251eV (T=1941K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.583eV  Ekin = 0.286eV (T=2215K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.606eV  Ekin = 0.265eV (T=2048K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.557eV  Ekin = 0.312eV (T=2415K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.557eV  Ekin = 0.311eV (T=2405K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.581eV  Ekin = 0.288eV (T=2228K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.629eV  Ekin = 0.241eV (T=1868K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.595eV  Ekin = 0.276eV (T=2134K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.580eV  Ekin = 0.290eV (T=2247K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.608eV  Ekin = 0.262eV (T=2029K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.542eV  Ekin = 0.326eV (T=2525K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.519eV  Ekin = 0.350eV (T=2706K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.553eV  Ekin = 0.318eV (T=2457K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.553eV  Ekin = 0.317eV (T=2455K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.563eV  Ekin = 0.307eV (T=2377K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.555eV  Ekin = 0.315eV (T=2438K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.535eV  Ekin = 0.336eV (T=2599K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.522eV  Ekin = 0.348eV (T=2693K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.544eV  Ekin = 0.326eV (T=2524K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.480eV  Ekin = 0.391eV (T=3022K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.487eV  Ekin = 0.385eV (T=2979K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.486eV  Ekin = 0.384eV (T=2971K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.498eV  Ekin = 0.371eV (T=2871K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.483eV  Ekin = 0.388eV (T=2998K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.487eV  Ekin = 0.382eV (T=2957K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.493eV  Ekin = 0.377eV (T=2916K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.482eV  Ekin = 0.388eV (T=3005K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.461eV  Ekin = 0.410eV (T=3172K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.480eV  Ekin = 0.390eV (T=3021K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.449eV  Ekin = 0.423eV (T=3275K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.465eV  Ekin = 0.406eV (T=3140K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.431eV  Ekin = 0.440eV (T=3406K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.393eV  Ekin = 0.478eV (T=3695K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.424eV  Ekin = 0.448eV (T=3464K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.396eV  Ekin = 0.475eV (T=3678K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.389eV  Ekin = 0.482eV (T=3728K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.383eV  Ekin = 0.488eV (T=3773K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.440eV  Ekin = 0.432eV (T=3343K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.416eV  Ekin = 0.455eV (T=3517K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.449eV  Ekin = 0.424eV (T=3283K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.461eV  Ekin = 0.411eV (T=3182K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.499eV  Ekin = 0.374eV (T=2895K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.441eV  Ekin = 0.430eV (T=3326K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.425eV  Ekin = 0.444eV (T=3434K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.423eV  Ekin = 0.448eV (T=3464K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.436eV  Ekin = 0.435eV (T=3367K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.396eV  Ekin = 0.473eV (T=3662K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.395eV  Ekin = 0.473eV (T=3657K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.375eV  Ekin = 0.493eV (T=3812K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.348eV  Ekin = 0.518eV (T=4009K)  Etot = 0.867eV\n",
      "Energy per atom: Epot = 0.361eV  Ekin = 0.507eV (T=3922K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.389eV  Ekin = 0.479eV (T=3709K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.369eV  Ekin = 0.491eV (T=3795K)  Etot = 0.859eV\n",
      "Energy per atom: Epot = 0.357eV  Ekin = 0.512eV (T=3957K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.365eV  Ekin = 0.504eV (T=3903K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.392eV  Ekin = 0.495eV (T=3832K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.390eV  Ekin = 0.493eV (T=3817K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.439eV  Ekin = 0.487eV (T=3771K)  Etot = 0.927eV\n",
      "Energy per atom: Epot = 0.406eV  Ekin = 0.520eV (T=4020K)  Etot = 0.926eV\n",
      "Energy per atom: Epot = 0.378eV  Ekin = 0.547eV (T=4231K)  Etot = 0.925eV\n",
      "Energy per atom: Epot = 0.359eV  Ekin = 0.566eV (T=4380K)  Etot = 0.925eV\n",
      "Energy per atom: Epot = 0.411eV  Ekin = 0.515eV (T=3985K)  Etot = 0.926eV\n",
      "Energy per atom: Epot = 0.361eV  Ekin = 0.572eV (T=4426K)  Etot = 0.934eV\n",
      "Energy per atom: Epot = 0.365eV  Ekin = 0.566eV (T=4379K)  Etot = 0.931eV\n",
      "Energy per atom: Epot = 0.414eV  Ekin = 0.522eV (T=4042K)  Etot = 0.937eV\n",
      "Energy per atom: Epot = 0.417eV  Ekin = 0.518eV (T=4010K)  Etot = 0.935eV\n",
      "Energy per atom: Epot = 0.354eV  Ekin = 0.574eV (T=4443K)  Etot = 0.928eV\n",
      "Energy per atom: Epot = 0.366eV  Ekin = 0.564eV (T=4361K)  Etot = 0.930eV\n",
      "Energy per atom: Epot = 0.394eV  Ekin = 0.536eV (T=4145K)  Etot = 0.930eV\n",
      "Energy per atom: Epot = 0.393eV  Ekin = 0.541eV (T=4183K)  Etot = 0.933eV\n",
      "Energy per atom: Epot = 0.383eV  Ekin = 0.541eV (T=4189K)  Etot = 0.924eV\n",
      "Energy per atom: Epot = 0.426eV  Ekin = 0.504eV (T=3901K)  Etot = 0.930eV\n",
      "Energy per atom: Epot = 0.374eV  Ekin = 0.555eV (T=4294K)  Etot = 0.929eV\n",
      "Energy per atom: Epot = 0.393eV  Ekin = 0.536eV (T=4144K)  Etot = 0.928eV\n",
      "Energy per atom: Epot = 0.390eV  Ekin = 0.539eV (T=4173K)  Etot = 0.929eV\n",
      "Energy per atom: Epot = 0.374eV  Ekin = 0.554eV (T=4285K)  Etot = 0.928eV\n",
      "Energy per atom: Epot = 0.349eV  Ekin = 0.578eV (T=4470K)  Etot = 0.927eV\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy per atom: Epot = 0.409eV  Ekin = 0.521eV (T=4034K)  Etot = 0.931eV\n",
      "Energy per atom: Epot = 0.370eV  Ekin = 0.560eV (T=4335K)  Etot = 0.930eV\n",
      "Energy per atom: Epot = 0.432eV  Ekin = 0.517eV (T=4003K)  Etot = 0.949eV\n",
      "Energy per atom: Epot = 0.435eV  Ekin = 0.522eV (T=4038K)  Etot = 0.957eV\n",
      "Energy per atom: Epot = 0.428eV  Ekin = 0.534eV (T=4133K)  Etot = 0.962eV\n",
      "Energy per atom: Epot = 0.378eV  Ekin = 0.587eV (T=4541K)  Etot = 0.965eV\n",
      "Energy per atom: Epot = 0.397eV  Ekin = 0.566eV (T=4381K)  Etot = 0.963eV\n",
      "Energy per atom: Epot = 0.379eV  Ekin = 0.581eV (T=4496K)  Etot = 0.960eV\n",
      "Energy per atom: Epot = 0.353eV  Ekin = 0.602eV (T=4659K)  Etot = 0.955eV\n",
      "Energy per atom: Epot = 0.425eV  Ekin = 0.533eV (T=4124K)  Etot = 0.958eV\n",
      "Energy per atom: Epot = 0.402eV  Ekin = 0.571eV (T=4417K)  Etot = 0.973eV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running MD with ASE's EMT\n",
    "\n",
    "from ase.calculators.emt import EMT\n",
    "calc = EMT()\n",
    "\n",
    "T = 150  # Kelvin\n",
    "\n",
    "# Set up a grid of water\n",
    "water = make_water(1.0, [3, 3, 3])\n",
    "water.set_calculator(calc)\n",
    "\n",
    "# We want to run MD using the Langevin algorithm\n",
    "# with a time step of 1 fs, the temperature T and the friction\n",
    "# coefficient to 0.002 atomic units.\n",
    "dyn = Langevin(water, 1 * units.fs, T * units.kB, 0.0002)\n",
    "\n",
    "def printenergy(a=water):  # store a reference to atoms in the definition.\n",
    "    \"\"\"Function to print the potential, kinetic and total energy.\"\"\"\n",
    "    epot = a.get_potential_energy() / len(a)\n",
    "    ekin = a.get_kinetic_energy() / len(a)\n",
    "    print('Energy per atom: Epot = %.3feV  Ekin = %.3feV (T=%3.0fK)  '\n",
    "          'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))\n",
    "\n",
    "dyn.attach(printenergy, interval=5)\n",
    "\n",
    "# We also want to save the positions of all atoms after every 5th time step.\n",
    "traj = Trajectory('dyn_emt.traj', 'w', water)\n",
    "dyn.attach(traj.write, interval=5)\n",
    "\n",
    "# Now run the dynamics\n",
    "printenergy(water)\n",
    "dyn.run(600)   # CHANGE THIS IF YOU WANT LONGER/SHORTER RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.483185Z",
     "start_time": "2018-10-07T17:10:50.968420Z"
    }
   },
   "outputs": [],
   "source": [
    "# wrap and save traj in .xyz --- the .traj is a non human readable database file, xyz is much better\n",
    "out_traj = ase.io.read('dyn_emt.traj', ':')\n",
    "for at in out_traj:\n",
    "    at.wrap()\n",
    "    if 'momenta' in at.arrays: del at.arrays['momenta']\n",
    "ase.io.write('dyn_emt.xyz', out_traj, 'xyz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get e0 for H and O - energies of the isolated atoms\n",
    "\n",
    "This is the energy of the isolated atom, will be in the teach_sparse string in the following format: `e0={H:energy:O:energy}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.495057Z",
     "start_time": "2018-10-07T17:10:51.486315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e0_H: 3.21\n",
      "e0_O: 4.6\n"
     ]
    }
   ],
   "source": [
    "isolated_H = Atoms('H', calculator=EMT(), cell=[20, 20, 20], pbc=True)\n",
    "isolated_O = Atoms('O', calculator=EMT(), cell=[20, 20, 20], pbc=True)\n",
    "\n",
    "print('e0_H:',isolated_H.get_potential_energy())\n",
    "print('e0_O:',isolated_O.get_potential_energy())\n",
    "\n",
    "# this made the e0 string be the following: e0={H:3.21:O:4.6}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T13:10:08.501028Z",
     "start_time": "2018-10-07T13:10:08.496414Z"
    }
   },
   "source": [
    "## separate the dataset into a training and a validation set\n",
    "\n",
    "As we have 120 frames from the 600fs MD, I will do it 60,60 with taking even and odd frames for the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.802364Z",
     "start_time": "2018-10-07T17:10:51.497953Z"
    }
   },
   "outputs": [],
   "source": [
    "ase.io.write('train.xyz', out_traj[0::2] + [isolated_H] + [isolated_O])  \n",
    "ase.io.write('validate.xyz', out_traj[1::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.813416Z",
     "start_time": "2018-10-07T17:10:51.805556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['numbers', 'positions'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_traj[0].arrays.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:48:02.357449Z",
     "start_time": "2018-10-07T12:48:02.347940Z"
    }
   },
   "source": [
    "## train our GAP model from the command line\n",
    "\n",
    "Will use a fit of 2b only, using the desciptor distance_2b.\n",
    "\n",
    "Let's understand how this works. The bash command takes named arguments separated by spaces.\n",
    "\n",
    "- `teach_sparse` the command which actually does the fit  \n",
    "- `e0={H:3.21:O:4.6}` the energies of the isolated atoms\n",
    "- `energy_parameter_name=energy force_parameter_name=forces` names of the parameters\n",
    "- `do_copy_at_file=F sparse_separate_file=T` just needed, don't want to copy the training data and using separate files for the xml makes it faster\n",
    "- `gp_file=GAP.xml` filename of the potential parameters, I have always used this name, because I had separate directories for the different trainings potentials\n",
    "- `at_file=train.xyz` training file\n",
    "- `default_sigma={0.008 0.04 0 0}` sigma values to be used for energies, forces, stresses, hessians in order; this represents the accuracy of the data and the relative weight of them in the fit (more accurate --> more significant in the fit)\n",
    "- `gap={...}` the potential to be fit, separated by ':'\n",
    "\n",
    "**distance_2b**\n",
    "- `cutoff=4.0` radial, practically the highest distance the descriptor takes into account \n",
    "- `covariance_type=ard_se` use gausses in the fit\n",
    "- `delta=0.5` what relative portion of the things shall be determined by this potential\n",
    "- `theta_uniform=1.0` width of the gaussians\n",
    "- `sparse_method=uniform` use uniform bins to choose the sparse points\n",
    "- `add_species=T ` take the species into account, so it will generate more GAPs automatically (see the output)\n",
    "- `n_sparse=10` number of sparse points\n",
    "\n",
    "\n",
    "## notice, that the script is running in parallel, using all 8 cores of the current machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:55.183809Z",
     "start_time": "2018-10-07T17:10:51.816438Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libAtoms::Hello World: 07/10/2019   14:37:12\n",
      "libAtoms::Hello World: git version  git@github.com:libAtoms/QUIP.git,f538cd9fe-dirty\n",
      "libAtoms::Hello World: QUIP_ARCH    linux_x86_64_gfortran_openmp\n",
      "libAtoms::Hello World: compiled on  Oct  1 2019 at 21:54:22\n",
      "libAtoms::Hello World: OpenMP parallelisation with 4 threads\n",
      "WARNING: libAtoms::Hello World: environment variable OMP_STACKSIZE not set explicitly. The default value - system and compiler dependent - may be too small for some applications.\n",
      "libAtoms::Hello World: Random Seed = 52632114\n",
      "libAtoms::Hello World: global verbosity = 0\n",
      "\n",
      "Calls to system_timer will do nothing by default\n",
      "\n",
      "\n",
      "================================ Input parameters ==============================\n",
      "\n",
      "at_file = train.xyz\n",
      "gap = \"distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10\"\n",
      "e0 = 0.0\n",
      "local_property0 = 0.0\n",
      "e0_offset = 0.0\n",
      "e0_method = isolated\n",
      "default_sigma = \"0.008 0.04 0 0\"\n",
      "sparse_jitter = 1.0e-10\n",
      "hessian_delta = 1.0e-2\n",
      "core_param_file = quip_params.xml\n",
      "core_ip_args =\n",
      "energy_parameter_name = energy\n",
      "local_property_parameter_name = local_property\n",
      "force_parameter_name = forces\n",
      "virial_parameter_name = virial\n",
      "hessian_parameter_name = hessian\n",
      "config_type_parameter_name = config_type\n",
      "sigma_parameter_name = sigma\n",
      "config_type_sigma =\n",
      "sigma_per_atom = T\n",
      "do_copy_at_file = F\n",
      "sparse_separate_file = T\n",
      "sparse_use_actual_gpcov = F\n",
      "gp_file = GAP.xml\n",
      "verbosity = NORMAL\n",
      "rnd_seed = -1\n",
      "do_ip_timing = F\n",
      "template_file = template.xyz\n",
      "sparsify_only_no_fit = F\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "\n",
      "============== Gaussian Approximation Potentials - Database fitting ============\n",
      "\n",
      "\n",
      "Initial parsing of command line arguments finished.\n",
      "Found 1 GAPs.\n",
      "Descriptors have been parsed\n",
      "XYZ file read\n",
      "Old GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1}\n",
      "Multispecies support added where requested\n",
      "\n",
      "===================== Report on number of descriptors found ====================\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8\n",
      "Number of descriptors:                        13192\n",
      "Number of partial derivatives of descriptors: 79152\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1\n",
      "Number of descriptors:                        63476\n",
      "Number of partial derivatives of descriptors: 380856\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1\n",
      "Number of descriptors:                        58986\n",
      "Number of partial derivatives of descriptors: 353916\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "E0/atom =   0.32100000000000000E+001  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.45999999999999996E+001  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000\n",
      "\n",
      "========== Report on number of target properties found in training XYZ: ========\n",
      "\n",
      "Number of target energies (property name: energy) found: 63\n",
      "Number of target local_properties (property name: local_property) found: 0\n",
      "Number of target forces (property name: forces) found: 14829\n",
      "Number of target virials (property name: virial) found: 0\n",
      "Number of target Hessian eigenvalues (property name: hessian) found: 0\n",
      "\n",
      "================================= End of report ================================\n",
      "\n",
      "\n",
      "===== Report on per-configuration/per-atom sigma (error parameter) settings ====\n",
      "\n",
      "Number of per-configuration setting of energy_sigma found:     0\n",
      "Number of per-configuration setting of force_sigma found:      0\n",
      "Number of per-configuration setting of virial_sigma found:     0\n",
      "Number of per-configuration setting of hessian_sigma found:    0\n",
      "Number of per-atom setting of force_atom_sigma found:          0\n",
      "\n",
      "================================= End of report ================================\n",
      "\n",
      "Cartesian coordinates transformed to descriptors\n",
      "Started sparse covariance matrix calculation of coordinate 1\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 1\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1_sparse  done in .12079600000000001 cpu secs, .33371098999850801E-001 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1         done in .12085800000000002 cpu secs, .33404888999939431E-001 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 2\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 2\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2_sparse  done in .51746400000000004 cpu secs, .13925121800002671 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2         done in .51753099999999996 cpu secs, .13928714700068667 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 3\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3_sparse  done in .47832100000000000 cpu secs, .12939972799995303 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3         done in .47839199999999993 cpu secs, .12943785000061325 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_LinearAlgebra       done in .58676999999999868E-001 cpu secs, .17518616999950609E-001 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_FunctionValues      done in .20999999999826713E-004 cpu secs, .21754999579570722E-004 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse                     done in 1.1780140000000001 cpu secs, .32216350199996668 wall clock secs.\n",
      "TIMER: GP sparsify                                        done in 1.2460960000000001 cpu secs, .35651906000020972 wall clock secs.\n",
      "\n",
      "libAtoms::Finalise: 07/10/2019   14:37:12\n",
      "libAtoms::Finalise: Bye-Bye!\n"
     ]
    }
   ],
   "source": [
    "! gap_fit energy_parameter_name=energy force_parameter_name=forces do_copy_at_file=F sparse_separate_file=T gp_file=GAP.xml at_file=train.xyz default_sigma={0.008 0.04 0 0} gap={distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:56:59.802090Z",
     "start_time": "2018-10-07T12:56:58.506422Z"
    }
   },
   "source": [
    "## use the potential with QUIP on trani.xyz and validate.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:11:02.087146Z",
     "start_time": "2018-10-07T17:10:55.187005Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate train.xyz\n",
    "\n",
    "! quip E=T F=T atoms_filename=train.xyz param_filename=GAP.xml | grep AT | sed 's/AT//' > quip_train.xyz\n",
    "! quip E=T F=T atoms_filename=validate.xyz param_filename=GAP.xml | grep AT | sed 's/AT//' > quip_validate.xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:58:09.146524Z",
     "start_time": "2018-10-07T12:58:09.136959Z"
    }
   },
   "source": [
    "## make simple plots of the energies and forces on the EMT and GAP datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:48:46.462404Z",
     "start_time": "2018-10-08T08:48:46.441786Z"
    }
   },
   "outputs": [],
   "source": [
    "def energy_plot(in_file, out_file, ax, title='Plot of energy'):\n",
    "    \"\"\" Plots the distribution of energy per atom on the output vs the input\"\"\"\n",
    "    # read files\n",
    "    in_atoms = ase.io.read(in_file, ':')\n",
    "    out_atoms = ase.io.read(out_file, ':')\n",
    "    # list energies\n",
    "    ener_in = [at.get_potential_energy() / len(at.get_chemical_symbols()) for at in in_atoms]\n",
    "    ener_out = [at.get_potential_energy() / len(at.get_chemical_symbols()) for at in out_atoms]\n",
    "    # scatter plot of the data\n",
    "    ax.scatter(ener_in, ener_out)\n",
    "    # get the appropriate limits for the plot\n",
    "    for_limits = np.array(ener_in +ener_out)   \n",
    "    elim = (for_limits.min() - 0.05, for_limits.max() + 0.05)\n",
    "    ax.set_xlim(elim)\n",
    "    ax.set_ylim(elim)\n",
    "    # add line of slope 1 for refrence\n",
    "    ax.plot(elim, elim, c='k')\n",
    "    # set labels\n",
    "    ax.set_ylabel('energy by GAP / eV')\n",
    "    ax.set_xlabel('energy by EMT / eV')\n",
    "    #set title\n",
    "    ax.set_title(title)\n",
    "    # add text about RMSE\n",
    "    _rms = rms_dict(ener_in, ener_out)\n",
    "    rmse_text = 'RMSE:\\n' + str(np.round(_rms['rmse'], 3)) + ' +- ' + str(np.round(_rms['std'], 3)) + 'eV/atom'\n",
    "    ax.text(0.9, 0.1, rmse_text, transform=ax.transAxes, fontsize='large', horizontalalignment='right', \n",
    "            verticalalignment='bottom')\n",
    "    \n",
    "def force_plot(in_file, out_file, ax, symbol='HO', title='Plot of force'):\n",
    "    \"\"\" Plots the distribution of firce components per atom on the output vs the input \n",
    "        only plots for the given atom type(s)\"\"\"\n",
    "    \n",
    "    in_atoms = ase.io.read(in_file, ':')\n",
    "    out_atoms = ase.io.read(out_file, ':')\n",
    "    \n",
    "    # extract data for only one species\n",
    "    in_force, out_force = [], []\n",
    "    for at_in, at_out in zip(in_atoms, out_atoms):\n",
    "        # get the symbols\n",
    "        sym_all = at_in.get_chemical_symbols()\n",
    "        # add force for each atom\n",
    "        for j, sym in enumerate(sym_all):\n",
    "            if sym in symbol:\n",
    "                in_force.append(at_in.get_forces()[j])\n",
    "                #out_force.append(at_out.get_forces()[j]) \\  \n",
    "                out_force.append(at_out.arrays['force'][j]) # because QUIP and ASE use different names\n",
    "    # convert to np arrays, much easier to work with\n",
    "    #in_force = np.array(in_force)\n",
    "    #out_force = np.array(out_force)\n",
    "    # scatter plot of the data\n",
    "    ax.scatter(in_force, out_force)\n",
    "    # get the appropriate limits for the plot\n",
    "    for_limits = np.array(in_force + out_force)   \n",
    "    flim = (for_limits.min() - 1, for_limits.max() + 1)\n",
    "    ax.set_xlim(flim)\n",
    "    ax.set_ylim(flim)\n",
    "    # add line of \n",
    "    ax.plot(flim, flim, c='k')\n",
    "    # set labels\n",
    "    ax.set_ylabel('force by GAP / (eV/Å)')\n",
    "    ax.set_xlabel('force by EMT / (eV/Å)')\n",
    "    #set title\n",
    "    ax.set_title(title)\n",
    "    # add text about RMSE\n",
    "    _rms = rms_dict(in_force, out_force)\n",
    "    rmse_text = 'RMSE:\\n' + str(np.round(_rms['rmse'], 3)) + ' +- ' + str(np.round(_rms['std'], 3)) + 'eV/Å'\n",
    "    ax.text(0.9, 0.1, rmse_text, transform=ax.transAxes, fontsize='large', horizontalalignment='right', \n",
    "            verticalalignment='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:53:58.018401Z",
     "start_time": "2018-10-08T08:53:50.892329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(nrows=3, ncols=2, gridspec_kw={'hspace': 0.3})\n",
    "fig.set_size_inches(15, 20)\n",
    "ax_list = ax_list.flat[:]\n",
    "\n",
    "energy_plot('train.xyz', 'quip_train.xyz', ax_list[0], 'Energy on training data')\n",
    "energy_plot('validate.xyz', 'quip_validate.xyz', ax_list[1], 'Energy on validation data')\n",
    "force_plot('train.xyz', 'quip_train.xyz', ax_list[2], 'H', 'Force on training data - H')\n",
    "force_plot('train.xyz', 'quip_train.xyz', ax_list[3], 'O', 'Force on training data - O')\n",
    "force_plot('validate.xyz', 'quip_validate.xyz', ax_list[4], 'H', 'Force on validation data - H')\n",
    "force_plot('validate.xyz', 'quip_validate.xyz', ax_list[5], 'O',  'Force on validation data - O')\n",
    "\n",
    "# if you wanted to have the same limits on the force plots\n",
    "#for ax in ax_list[2:]:\n",
    "#    flim = (-20, 20)\n",
    "#    ax.set_xlim(flim)\n",
    "#    ax.set_ylim(flim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:48:02.357449Z",
     "start_time": "2018-10-07T12:48:02.347940Z"
    }
   },
   "source": [
    "## train our GAP_3b model from the command line\n",
    "\n",
    "Let's add three ody terms to the fit, which will hopefully improve it. We will be using the desciprtors distance_2b and angle_3b.\n",
    "\n",
    "**angle_3b**\n",
    "- `theta_fac=0.5` this takes the input data and determines the width from that; useful here, because the dimensions of the descriptor are different\n",
    "- `n_sparse=50` higher dimensional space, more sparse points\n",
    "\n",
    "## both training and quip takes significantly more time than the last one!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:12:12.409600Z",
     "start_time": "2018-10-07T17:11:09.080521Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libAtoms::Hello World: 07/10/2019   14:37:54\n",
      "libAtoms::Hello World: git version  git@github.com:libAtoms/QUIP.git,f538cd9fe-dirty\n",
      "libAtoms::Hello World: QUIP_ARCH    linux_x86_64_gfortran_openmp\n",
      "libAtoms::Hello World: compiled on  Oct  1 2019 at 21:54:22\n",
      "libAtoms::Hello World: OpenMP parallelisation with 4 threads\n",
      "WARNING: libAtoms::Hello World: environment variable OMP_STACKSIZE not set explicitly. The default value - system and compiler dependent - may be too small for some applications.\n",
      "libAtoms::Hello World: Random Seed = 52674487\n",
      "libAtoms::Hello World: global verbosity = 0\n",
      "\n",
      "Calls to system_timer will do nothing by default\n",
      "\n",
      "\n",
      "================================ Input parameters ==============================\n",
      "\n",
      "at_file = train.xyz\n",
      "gap = \"distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10 : angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform\"\n",
      "e0 = 0.0\n",
      "local_property0 = 0.0\n",
      "e0_offset = 0.0\n",
      "e0_method = isolated\n",
      "default_sigma = \"0.008 0.04 0 0\"\n",
      "sparse_jitter = 1.0e-10\n",
      "hessian_delta = 1.0e-2\n",
      "core_param_file = quip_params.xml\n",
      "core_ip_args =\n",
      "energy_parameter_name = energy\n",
      "local_property_parameter_name = local_property\n",
      "force_parameter_name = forces\n",
      "virial_parameter_name = virial\n",
      "hessian_parameter_name = hessian\n",
      "config_type_parameter_name = config_type\n",
      "sigma_parameter_name = sigma\n",
      "config_type_sigma =\n",
      "sigma_per_atom = T\n",
      "do_copy_at_file = F\n",
      "sparse_separate_file = T\n",
      "sparse_use_actual_gpcov = F\n",
      "gp_file = GAP_3b.xml\n",
      "verbosity = NORMAL\n",
      "rnd_seed = -1\n",
      "do_ip_timing = F\n",
      "template_file = template.xyz\n",
      "sparsify_only_no_fit = F\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "\n",
      "============== Gaussian Approximation Potentials - Database fitting ============\n",
      "\n",
      "\n",
      "Initial parsing of command line arguments finished.\n",
      "Found 2 GAPs.\n",
      "Descriptors have been parsed\n",
      "XYZ file read\n",
      "Old GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1}\n",
      "Old GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=8}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=1 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=8}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=1 Z2=1}\n",
      "Multispecies support added where requested\n",
      "\n",
      "===================== Report on number of descriptors found ====================\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8\n",
      "Number of descriptors:                        13192\n",
      "Number of partial derivatives of descriptors: 79152\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1\n",
      "Number of descriptors:                        63476\n",
      "Number of partial derivatives of descriptors: 380856\n",
      "---------------------------------------------------------------------\n",
      "Descriptor: distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1\n",
      "Number of descriptors:                        58986\n",
      "Number of partial derivatives of descriptors: 353916\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=8\n",
      "Number of descriptors:                        49012\n",
      "Number of partial derivatives of descriptors: 441108\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=1\n",
      "Number of descriptors:                        242862\n",
      "Number of partial derivatives of descriptors: 2185758\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=1 Z2=1\n",
      "Number of descriptors:                        261574\n",
      "Number of partial derivatives of descriptors: 2354166\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=8\n",
      "Number of descriptors:                        124922\n",
      "Number of partial derivatives of descriptors: 1124298\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=1\n",
      "Number of descriptors:                        536486\n",
      "Number of partial derivatives of descriptors: 4828374\n",
      "---------------------------------------------------------------------\n",
      "Descriptor:  angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=1 Z2=1\n",
      "Number of descriptors:                        495172\n",
      "Number of partial derivatives of descriptors: 4456548\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "E0/atom =   0.32100000000000000E+001  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.45999999999999996E+001  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000  0.00000000000000000E+000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========== Report on number of target properties found in training XYZ: ========\n",
      "\n",
      "Number of target energies (property name: energy) found: 63\n",
      "Number of target local_properties (property name: local_property) found: 0\n",
      "Number of target forces (property name: forces) found: 14829\n",
      "Number of target virials (property name: virial) found: 0\n",
      "Number of target Hessian eigenvalues (property name: hessian) found: 0\n",
      "\n",
      "================================= End of report ================================\n",
      "\n",
      "\n",
      "===== Report on per-configuration/per-atom sigma (error parameter) settings ====\n",
      "\n",
      "Number of per-configuration setting of energy_sigma found:     0\n",
      "Number of per-configuration setting of force_sigma found:      0\n",
      "Number of per-configuration setting of virial_sigma found:     0\n",
      "Number of per-configuration setting of hessian_sigma found:    0\n",
      "Number of per-atom setting of force_atom_sigma found:          0\n",
      "\n",
      "================================= End of report ================================\n",
      "\n",
      "Cartesian coordinates transformed to descriptors\n",
      "Started sparse covariance matrix calculation of coordinate 1\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 1\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1_sparse  done in .28807099999999952 cpu secs, .10717047499929322 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1         done in .31319400000000019 cpu secs, .11472131200025615 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 2\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 2\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2_sparse  done in .59233300000000000 cpu secs, .16927113200017629 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2         done in .59236500000000003 cpu secs, .16930502099967271 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 3\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 3\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3_sparse  done in .53734700000000046 cpu secs, .15147510299993883 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3         done in .53737699999999933 cpu secs, .15150780800013308 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 4\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 4\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate4_sparse  done in 1.6066120000000002 cpu secs, .42011101099978987 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate4         done in 1.6066430000000000 cpu secs, .42014362800000526 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 5\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 5\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate5_sparse  done in 7.3996139999999997 cpu secs, 2.0708142040002713 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate5         done in 7.3996479999999991 cpu secs, 2.0708500330001698 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 6\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 6\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate6_sparse  done in 7.7948529999999998 cpu secs, 2.1017793959999835 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate6         done in 7.7948830000000005 cpu secs, 2.1018120230000932 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 7\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 7\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate7_sparse  done in 3.7665400000000027 cpu secs, .98470028799965803 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate7         done in 3.7665709999999990 cpu secs, .98473350699987350 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 8\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 8\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate8_sparse  done in 15.648033999999996 cpu secs, 3.9801498979995813 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate8         done in 15.648065000000003 cpu secs, 3.9801835840007698 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 9\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 9\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate9_sparse  done in 14.597608999999999 cpu secs, 4.0186748889991577 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate9         done in 14.597645000000000 cpu secs, 4.0187123029991199 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_LinearAlgebra       done in .58762099999999862 cpu secs, .55210713699943881 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_FunctionValues      done in .19999999999242846E-004 cpu secs, .19695999981195200E-004 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse                     done in 52.861531999999997 cpu secs, 14.585489690999566 wall clock secs.\n",
      "TIMER: GP sparsify                                        done in 53.793665000000004 cpu secs, 15.628059424999265 wall clock secs.\n",
      "\n",
      "libAtoms::Finalise: 07/10/2019   14:38:19\n",
      "libAtoms::Finalise: Bye-Bye!\n"
     ]
    }
   ],
   "source": [
    "! gap_fit energy_parameter_name=energy force_parameter_name=forces do_copy_at_file=F sparse_separate_file=T gp_file=GAP_3b.xml at_file=train.xyz default_sigma={0.008 0.04 0 0} gap={distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10 : angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:56:59.802090Z",
     "start_time": "2018-10-07T12:56:58.506422Z"
    }
   },
   "source": [
    "## use the potential with QUIP on trani.xyz and validate.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:12:47.633785Z",
     "start_time": "2018-10-07T17:12:12.412800Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate train.xyz\n",
    "\n",
    "! quip E=T F=T atoms_filename=train.xyz param_filename=GAP_3b.xml | grep AT | sed 's/AT//' > quip_3b_train.xyz\n",
    "! quip E=T F=T atoms_filename=validate.xyz param_filename=GAP_3b.xml | grep AT | sed 's/AT//' > quip_3b_validate.xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## look at the outputs - clear improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:54:43.555206Z",
     "start_time": "2018-10-08T08:54:36.324200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(nrows=3, ncols=2, gridspec_kw={'hspace': 0.3})\n",
    "fig.set_size_inches(15, 20)\n",
    "ax_list = ax_list.flat[:]\n",
    "\n",
    "energy_plot('train.xyz', 'quip_3b_train.xyz', ax_list[0], 'Energy on training data')\n",
    "energy_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[1], 'Energy on validation data')\n",
    "force_plot('train.xyz', 'quip_3b_train.xyz', ax_list[2], 'H', 'Force on training data - H')\n",
    "force_plot('train.xyz', 'quip_3b_train.xyz', ax_list[3], 'O', 'Force on training data - O')\n",
    "force_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[4], 'H', 'Force on validation data - H')\n",
    "force_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[5], 'O',  'Force on validation data - O')\n",
    "\n",
    "# if you wanted to have the same limits on the force plots\n",
    "#for ax in ax_list[2:]:\n",
    "#    flim = (-20, 20)\n",
    "#    ax.set_xlim(flim)\n",
    "#    ax.set_ylim(flim)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}