{
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   "source": [
    "# Charge and exciton transport on a model lattice\n",
    "\n",
    "This tutorial goes through the transport calculators of VOTCA-XTP on a small model system:\n",
    "\n",
    "1. a **time-of-flight** (ToF) experiment, solved exactly with the graph random walk (`grw`) and simulated with kinetic Monte Carlo (`kmctof`),\n",
    "2. **bulk transport** in a periodic box with `kmcmultiple`: mobility, diffusion, and the effect of the carrier density,\n",
    "3. why ToF and bulk mobilities differ in thin samples,\n",
    "4. **exciton diffusion** with finite lifetimes and quenchers with `kmclifetime`,\n",
    "5. the dependence of the mobility on energetic disorder.\n",
    "\n",
    "Instead of a morphology from molecular dynamics we use a cubic lattice with Gaussian site energies, written by the `latticegen` tool as an ordinary state file. All calculators work the same way on real morphologies (see the thiophene tutorial); on the lattice everything runs in seconds and the parameters are under control. Background: the chapters *Charge and Energy Transport Model* and *Transport Simulations* of the XTP manual.\n",
    "\n",
    "## Requirements\n",
    "* VOTCA installed and the environment activated with `VOTCARC.bash` (see the [installation instructions](https://github.com/votca/votca/blob/master/share/sphinx/INSTALL.rst)).\n",
    "* Python with `numpy` and `matplotlib`.\n",
    "\n",
    "In Jupyter, `!` runs a shell command."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import glob\n",
    "import os\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# latticegen does not overwrite state files: remove those of a previous run\n",
    "for name in [\"lattice.hdf5\", \"small.hdf5\"] + glob.glob(\"slab_*.hdf5\") + glob.glob(\"disorder_*.hdf5\"):\n",
    "    if os.path.exists(name):\n",
    "        os.remove(name)\n",
    "\n",
    "\n",
    "def run(command):\n",
    "    # run a VOTCA command quietly (raises if it fails)\n",
    "    subprocess.run(command, shell=True, check=True, capture_output=True)\n",
    "\n",
    "\n",
    "def results(filename):\n",
    "    # read a 'key value unit' result file into a dictionary\n",
    "    values = {}\n",
    "    with open(filename) as f:\n",
    "        for line in f:\n",
    "            if line.startswith(\"#\") or not line.strip():\n",
    "                continue\n",
    "            key, value = line.split()[:2]\n",
    "            try:\n",
    "                values[key] = float(value)\n",
    "            except ValueError:\n",
    "                values[key] = value\n",
    "    return values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2b5cd1",
   "metadata": {},
   "source": [
    "## The model system\n",
    "\n",
    "`latticegen` places sites on a simple cubic lattice with spacing $a$ = 1 nm (`spacing`), connects all pairs closer than `cutoff` (1.5 nm: 6 nearest and 12 next-nearest neighbours), gives them couplings $|J|^2 = J_2\\, e^{-(r-a)/\\ell}$ and one reorganization energy $\\lambda$, and draws Gaussian site energies with standard deviation $\\sigma$ (`energy.sigma`). The defaults follow Chen, van der Hoorn, Baumeier, arXiv:2405.15836. We use $10\\times10\\times10$ sites, $\\sigma$ = 0.1 eV (about $4\\,k_BT$ at room temperature), and write parameters for electrons, holes and singlets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee488703",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:05.111213Z",
     "iopub.status.busy": "2026-11-10T21:08:05.110863Z",
     "iopub.status.idle": "2026-11-10T21:08:05.511862Z",
     "shell.execute_reply": "2026-11-10T21:08:05.511240Z"
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\r\n",
      "========   VOTCA (http://www.votca.org)   ========\r\n",
      "==================================================\r\n",
      "\r\n",
      "please read and cite: https://doi.org/10.21105/joss.06864\r\n",
      "and submit bugs to https://github.com/votca/votca/issues\r\n",
      "\r\n",
      "xtp_tools, version 2026-dev gitid: ca10e5f (compiled Nov 10 2026, 20:22:31)\r\n",
      "\r\n",
      "Initializing tool\r\n",
      "... latticegen Evaluating tool\r\n",
      "... latticegen  Using 1 threads"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "... ... Lattice 10x10x10: 1000 sites, 9000 pairs\r\n",
      "... ... Site energies: mean -0.767305 eV, standard deviation 0.099181 eVCreating statefile lattice.hdf5\r\n",
      "Frame with id 0 was not in statefile lattice.hdf5 ,adding it now.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote MD topology (step = 0, time = 0) to lattice.hdf5\r\n",
      "... . \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "... ... Wrote lattice.hdf5\r\n",
      "... ... Wrote site energies to lattice_E.txt\r\n",
      "==============================================================================\r\n",
      "Please cite the methods used in this run:\r\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\r\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\r\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\r\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\r\n",
      "      doi:10.21105/joss.06864\r\n",
      "  [2] Chen, van der Hoorn, and Baumeier, A graph random walk method for\r\n",
      "      calculating time-of-flight charge mobility in organic semiconductors\r\n",
      "      from multiscale simulations, Multiscale Model. Simul. 23, 1439 (2025),\r\n",
      "      doi:10.1137/24M1658085\r\n",
      "==============================================================================\r\n"
     ]
    }
   ],
   "source": [
    "!xtp_tools -e latticegen -c job_name=lattice dims=\"10 10 10\" states=\"e h s\" energy.sigma=0.1 energyfile=lattice_E.txt"
   ]
  },
  {
   "cell_type": "code",
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     "shell.execute_reply": "2026-11-10T21:08:05.629732Z"
    }
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.loadtxt(\"lattice_E.txt\")[:, 4]\n",
    "plt.hist(E, bins=30)\n",
    "plt.xlabel(\"site energy (eV)\")\n",
    "plt.ylabel(\"sites\")\n",
    "plt.title(f\"mean {E.mean():.3f} eV, standard deviation {E.std():.3f} eV\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2366d42",
   "metadata": {},
   "source": [
    "## Time of flight: exact hitting times with `grw`\n",
    "\n",
    "For the ToF geometry the periodicity is cut across one box vector (`tof.axis`, default `a`): the sample becomes a slab, periodic in the two other directions. Holes are injected in a 1 nm thick source slab at one face and absorbed in a 1 nm thick sink slab at the other; a field of $2\\cdot10^7$ V/m (200 kV/cm) drives them across.\n",
    "\n",
    "`grw` computes the mean time until a carrier, injected uniformly on the source sites, reaches the sink, from a sparse linear system: no random numbers are involved. The mobility follows as $\\mu = L/(\\langle t\\rangle F)$ with the transport length $L$ printed in the log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "115f2844",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:05.632913Z",
     "iopub.status.busy": "2026-11-10T21:08:05.632374Z",
     "iopub.status.idle": "2026-11-10T21:08:05.959105Z",
     "shell.execute_reply": "2026-11-10T21:08:05.958509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\r\n",
      "========   VOTCA (http://www.votca.org)   ========\r\n",
      "==================================================\r\n",
      "\r\n",
      "please read and cite: https://doi.org/10.21105/joss.06864\r\n",
      "and submit bugs to https://github.com/votca/votca/issues\r\n",
      "\r\n",
      "xtp_run, version 2026-dev gitid: ca10e5f (compiled Nov 10 2026, 20:22:31)\r\n",
      "\r\n",
      "Initializing calculator\r\n",
      "... grw\r\n",
      "1 frames in statefile, Ids are: 0 \r\n",
      "Starting at frame 0\r\n",
      "Evaluating frame 0\r\n",
      "Import MD Topology (i.e. frame 0) from lattice.hdf5\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... \r\n",
      "... grw\r\n",
      " Using 1 threads\r\n",
      " ... \r\n",
      "-----------------------------------\r\n",
      "  GRAPH RANDOM WALK TIME OF FLIGHT\r\n",
      "-----------------------------------\r\n",
      " ... ToF graph for holes\r\n",
      "  periodicity cut across box vector a, normal (1 0 0), thickness 10 nm\r\n",
      "  sites: 1000 (0 of ignored types), hops: 17000, pairs cut at the periodic boundary: 500\r\n",
      "  sources: 100 connected, 0 without path to a sink (excluded)\r\n",
      "  sinks: 100, sites without path to a sink: 0\r\n",
      "  transport length (mean source to first sink): 9 nm\r\n",
      "  detailed balance: yes (max deviation of ln(w_ij/w_ji) 8.88178e-15)\r\n",
      " ... Solver: cholesky (LDLT of the symmetrized generator), 900 unknowns, 0 iterations, backward error 2.1465e-15 after 0 refinement steps\r\n",
      " ... Time of flight (uniform injection over 100 source sites): mean 1.944e-10 s\r\n",
      " ... Standard deviation of the hitting time: 2.54188e-10 s\r\n",
      " ... Harmonic mean over the injection states: 1.89596e-10 s\r\n",
      " ... Mobility L/(tau F): 0.0231482 cm^2/Vs (mean), 0.0237347 cm^2/Vs (harmonic), with L = 9 nm and F = 2e+07 V/m along the normal"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changes have not been written to state file.\r\n",
      "\r\n",
      "==============================================================================\r\n",
      "Please cite the methods used in this run:\r\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\r\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\r\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\r\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\r\n",
      "      doi:10.21105/joss.06864\r\n",
      "  [2] Chen, van der Hoorn, and Baumeier, A graph random walk method for\r\n",
      "      calculating time-of-flight charge mobility in organic semiconductors\r\n",
      "      from multiscale simulations, Multiscale Model. Simul. 23, 1439 (2025),\r\n",
      "      doi:10.1137/24M1658085\r\n",
      "  [3] Stenzel et al., A general framework for consistent estimation of charge\r\n",
      "      transport properties via random walks in random environments, Multiscale\r\n",
      "      Model. Simul. 12, 1108 (2014), doi:10.1137/130942504\r\n",
      "==============================================================================\r\n"
     ]
    }
   ],
   "source": [
    "!xtp_run -e grw -c carriertype=hole field=\"2e7 0 0\" -f lattice.hdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d81a25",
   "metadata": {},
   "source": [
    "Besides the log, every transport calculator writes a short result file with `key value unit` lines (here `grw_results.dat`), which is convenient for scripts. `grw_occupation.dat` contains the expected time $g_A$ each site $A$ is occupied before the hole arrives at the sink (averaged over a uniform injection on the source sites), and the *fraction of the time of flight* $g_A/\\langle T\\rangle$, which adds up to 1 over all sites. Plotted against the site energy, it shows where the time of flight is spent: a few low-energy sites (traps) hold the carrier for a large part of it. (All output quantities are defined in the section *Output quantities* of the manual chapter *Transport Simulations*.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db7631b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:05.962019Z",
     "iopub.status.busy": "2026-11-10T21:08:05.961403Z",
     "iopub.status.idle": "2026-11-10T21:08:06.189362Z",
     "shell.execute_reply": "2026-11-10T21:08:06.188898Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time of flight 1.944e-10 s, mobility 2.315e-02 cm^2/Vs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 10 most occupied sites hold 77% of the time of flight\n"
     ]
    }
   ],
   "source": [
    "grw = results(\"grw_results.dat\")\n",
    "print(f\"time of flight {grw['tau_mean']:.3e} s, mobility {grw['mobility_mean']:.3e} cm^2/Vs\")\n",
    "\n",
    "occ = np.loadtxt(\"grw_occupation.dat\", usecols=(1, 2, 4, 5))\n",
    "x, energy, time, fraction = occ.T\n",
    "plt.scatter(energy, fraction, s=6)\n",
    "plt.yscale(\"log\")\n",
    "plt.ylim(1e-6, 1)\n",
    "plt.xlabel(\"site energy (eV)\")\n",
    "plt.ylabel(r\"fraction of the time of flight $g_A/\\langle T\\rangle$\")\n",
    "plt.show()\n",
    "order = np.argsort(fraction)[::-1]\n",
    "print(f\"the 10 most occupied sites hold {fraction[order[:10]].sum():.0%} of the time of flight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018fe1aa",
   "metadata": {},
   "source": [
    "Because `grw` needs no sampling, scans are cheap. Here the field dependence of the ToF mobility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b36444e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:06.191769Z",
     "iopub.status.busy": "2026-11-10T21:08:06.191277Z",
     "iopub.status.idle": "2026-11-10T21:08:07.657560Z",
     "shell.execute_reply": "2026-11-10T21:08:07.656990Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fields = np.array([0.5, 1, 2, 4, 8, 16]) * 1e7  # V/m\n",
    "mu = []\n",
    "for F in fields:\n",
    "    run(f'xtp_run -e grw -c carriertype=hole field=\"{F} 0 0\" resultfile=scan.dat sourcefile=x occupationfile=x -f lattice.hdf5')\n",
    "    mu.append(results(\"scan.dat\")[\"mobility_mean\"])\n",
    "plt.semilogy(np.sqrt(fields / 1e2), mu, \"o-\")  # sqrt(V/cm)\n",
    "plt.xlabel(r\"$\\sqrt{F}$ ($\\sqrt{\\mathrm{V/cm}}$)\")\n",
    "plt.ylabel(\"ToF mobility (cm$^2$/Vs)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a950afd",
   "metadata": {},
   "source": [
    "At high fields the mobility rises with $\\sqrt{F}$, as often observed (Poole-Frenkel-like behaviour), because the field lowers the barriers out of traps. At low fields it rises again: across a 9 nm slab diffusion alone brings carriers to the sink, so $\\langle t\\rangle$ no longer scales as $1/F$ and $L/(\\langle t\\rangle F)$ is not a drift mobility any more. A ToF mobility is only meaningful when drift dominates, i.e. for $qFL \\gg k_BT$ (here $F\\gg 3\\cdot10^6$ V/m)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b21104",
   "metadata": {},
   "source": [
    "## The same with kinetic Monte Carlo: `kmctof`\n",
    "\n",
    "`kmctof` simulates the experiment: in each of `runs` runs a hole is injected on a random source site and hops until it reaches the sink. The mean transit time agrees with `grw` within the statistical error. In addition, KMC gives the distribution of transit times (`kmctof_runs.dat`, one line per run) and the transient current (`kmctof_current.dat`), and it can handle many carriers, pulses and time-dependent rates.\n",
    "\n",
    "The transient current is computed as in an experiment, after Ramo and Shockley: every hop moves charge by $\\Delta x$ along the transport direction and induces a current $q\\,\\Delta x/L$ in the electrodes. `kmctof` sums these over all runs on a logarithmic time axis and divides by the number of injected carriers $N_\\mathrm{inj}$ and the bin width:\n",
    "\n",
    "$$I(t)=\\frac{1}{L\\,N_\\mathrm{inj}\\,\\Delta t}\\sum_{\\text{hops in }[t,t+\\Delta t)}\\Delta x \\qquad (\\text{1/s}).$$\n",
    "\n",
    "So $I(t)$ is the current per injected carrier in units of the elementary charge; for $N_\\mathrm{inj}$ carriers of charge $q$ the measured current is $q\\,N_\\mathrm{inj}\\,I(t)$. Its time integral is the mean displacement per injected carrier in units of $L$. The file also contains the *collected fraction*, the number of carriers absorbed up to the end of each bin divided by $N_\\mathrm{inj}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "564a94e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:07.659653Z",
     "iopub.status.busy": "2026-11-10T21:08:07.659405Z",
     "iopub.status.idle": "2026-11-10T21:08:08.074725Z",
     "shell.execute_reply": "2026-11-10T21:08:08.074016Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\r\n",
      "========   VOTCA (http://www.votca.org)   ========\r\n",
      "==================================================\r\n",
      "\r\n",
      "please read and cite: https://doi.org/10.21105/joss.06864\r\n",
      "and submit bugs to https://github.com/votca/votca/issues\r\n",
      "\r\n",
      "xtp_run, version 2026-dev gitid: ca10e5f (compiled Nov 10 2026, 20:22:31)\r\n",
      "\r\n",
      "Initializing calculator\r\n",
      "... kmctof\r\n",
      "1 frames in statefile, Ids are: 0 \r\n",
      "Starting at frame 0\r\n",
      "Evaluating frame 0\r\n",
      "Import MD Topology (i.e. frame 0) from lattice.hdf5\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... \r\n",
      "... kmctof\r\n",
      " Using 1 threads\r\n",
      " ... \r\n",
      "-----------------------------------\r\n",
      "  KINETIC MONTE CARLO TIME OF FLIGHT\r\n",
      "-----------------------------------\r\n",
      " ... ToF graph for holes\r\n",
      "  periodicity cut across box vector a, normal (1 0 0), thickness 10 nm\r\n",
      "  sites: 1000 (0 of ignored types), hops: 17000, pairs cut at the periodic boundary: 500\r\n",
      "  sources: 100 connected, 0 without path to a sink (excluded)\r\n",
      "  sinks: 100, sites without path to a sink: 0\r\n",
      "  transport length (mean source to first sink): 9 nm\r\n",
      "  detailed balance: yes (max deviation of ln(w_ij/w_ji) 8.88178e-15)\r\n",
      " ... Waiting times: exponential\r\n",
      " ... 4000 runs, 1 pulse(s) of 1 carrier(s), 1 thread(s)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... 341826 hops, 0 null events (occupied site or rejected), 0.089953 s\r\n",
      " ... First arrival: 1.87874e-10 +- 3.89775e-12 s (standard error, 4000 runs), standard deviation 2.46515e-10 s, largest 1 % of the runs: 8.47518 % of the sum\r\n",
      " ... Mean transit time per carrier: 1.87874e-10 +- 3.89775e-12 s (standard error, 4000 runs), standard deviation 2.46515e-10 s, largest 1 % of the runs: 8.47518 % of the sum\r\n",
      " ... Mobility L/(tau F) from the mean transit time: 0.0239522 cm^2/VsChanges have not been written to state file.\r\n",
      "\r\n",
      "==============================================================================\r\n",
      "Please cite the methods used in this run:\r\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\r\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\r\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\r\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\r\n",
      "      doi:10.21105/joss.06864\r\n",
      "  [2] Ruehle, Lukyanov, May, Schrader, Vehoff, Kirkpatrick, Baumeier, and\r\n",
      "      Andrienko, Microscopic simulations of charge transport in disordered\r\n",
      "      organic semiconductors, J. Chem. Theory Comput. 7, 3335 (2011),\r\n",
      "      doi:10.1021/ct200388s\r\n",
      "==============================================================================\r\n"
     ]
    }
   ],
   "source": [
    "!xtp_run -e kmctof -c carriertype=hole field=\"2e7 0 0\" runs=4000 -f lattice.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ae2b2ad7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:08.077807Z",
     "iopub.status.busy": "2026-11-10T21:08:08.077223Z",
     "iopub.status.idle": "2026-11-10T21:08:08.576249Z",
     "shell.execute_reply": "2026-11-10T21:08:08.575723Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMC 1.879e-10 +- 3.9e-12 s, grw 1.944e-10 s: -1.7 standard errors\n",
      "the slowest 1 % of the runs contribute 8% of the sum of the transit times\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integral of I(t): 1.000 (mean displacement / L), collected fraction: 1.000\n"
     ]
    }
   ],
   "source": [
    "tof = results(\"kmctof_results.dat\")\n",
    "z = (tof[\"transit_mean\"] - grw[\"tau_mean\"]) / tof[\"transit_error\"]\n",
    "print(f\"KMC {tof['transit_mean']:.3e} +- {tof['transit_error']:.1e} s, grw {grw['tau_mean']:.3e} s: {z:+.1f} standard errors\")\n",
    "print(f\"the slowest 1 % of the runs contribute {tof['transit_top_percent_share']:.0%} of the sum of the transit times\")\n",
    "\n",
    "transit = np.loadtxt(\"kmctof_runs.dat\", usecols=5)\n",
    "current = np.loadtxt(\"kmctof_current.dat\")\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
    "ax1.hist(transit, bins=np.logspace(-12, -8, 41))\n",
    "ax1.axvline(grw[\"tau_mean\"], color=\"k\", ls=\"--\", label=\"grw mean\")\n",
    "ax1.set_xscale(\"log\")\n",
    "ax1.set_xlabel(\"transit time (s)\")\n",
    "ax1.set_ylabel(\"runs\")\n",
    "ax1.legend()\n",
    "ax2.loglog(current[:, 2], current[:, 3], label=\"current $I(t)$\")\n",
    "ax2.set_xlim(1e-13, 1e-8)\n",
    "ax2.set_ylim(1e5)\n",
    "ax2.set_xlabel(\"time (s)\")\n",
    "ax2.set_ylabel(r\"$I(t)$ per injected carrier (1/s)\")\n",
    "ax3 = ax2.twinx()\n",
    "ax3.semilogx(current[:, 1], current[:, 4], color=\"C1\", label=\"collected fraction\")\n",
    "ax3.set_ylim(0, 1.05)\n",
    "ax3.set_ylabel(\"collected fraction\", color=\"C1\")\n",
    "plt.show()\n",
    "\n",
    "# the integral of I(t) (with the parts outside the time axis, in the file\n",
    "# header) is the mean displacement per injected carrier in units of L\n",
    "with open(\"kmctof_current.dat\") as f:\n",
    "    header = [line for line in f if \"displacement before\" in line][0]\n",
    "# \"# displacement before t_low of the first bin: <a>, after the last bin: <b>\"\n",
    "outside = sum(float(part.split(\":\")[1]) for part in header.split(\",\"))\n",
    "integral = np.sum(current[:, 3] * (current[:, 1] - current[:, 0])) + outside\n",
    "print(f\"integral of I(t): {integral:.3f} (mean displacement / L), collected fraction: {current[-1, 4]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3655b6f",
   "metadata": {},
   "source": [
    "The transit times spread over more than two decades, with a tail of slow runs in which the hole was caught in a deep trap. How much this tail weighs in the mean is measured by the share of the slowest 1 % of the runs in the sum of all transit times: about 6 % for exponentially distributed times, and `kmctof` warns above 20 %, when a few runs dominate the mean. The transient current shows the typical ToF shape on a double-logarithmic scale: a slowly decaying part while the carriers drift and relax, then a steep drop when they arrive, while the collected fraction rises to 1. On the lattice the integral of $I(t)$ equals the collected fraction, because all source sites lie in one layer and a carrier is absorbed as soon as it reaches the first sink layer, so every collected carrier has moved exactly $L$. In a morphology the source and sink sites are spread over the slabs and the two numbers differ slightly, since $L$ is measured from the mean source position to the first sink sites.\n",
    "\n",
    "### Two carriers\n",
    "\n",
    "`grw` also handles two carriers with site exclusion, on pair states. Their number grows as $n^2/2$ (404550 for our 1000 sites, about half a minute), so we use a smaller $6\\times6\\times6$ lattice. With `observable=first`, the time until the first of two simultaneously injected holes arrives is computed; `kmctof` with `injection.carriers=2` measures the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb57ca8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:08.578112Z",
     "iopub.status.busy": "2026-11-10T21:08:08.577925Z",
     "iopub.status.idle": "2026-11-10T21:08:09.098473Z",
     "shell.execute_reply": "2026-11-10T21:08:09.097952Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknowns of the two-carrier system: 16110\n",
      "first arrival: grw 1.642e-11 s, KMC 1.634e-11 +- 2.7e-13 s\n"
     ]
    }
   ],
   "source": [
    "run('xtp_tools -e latticegen -c job_name=small dims=\"6 6 6\" energy.sigma=0.1')\n",
    "run('xtp_run -e grw -c carriertype=hole field=\"2e7 0 0\" numberofcarriers=2 observable=first resultfile=grw2.dat sourcefile=x occupationfile=x -f small.hdf5')\n",
    "run('xtp_run -e kmctof -c carriertype=hole field=\"2e7 0 0\" runs=4000 injection.carriers=2 resultfile=kmctof2.dat runfile=x current.file=x -f small.hdf5')\n",
    "grw2, tof2 = results(\"grw2.dat\"), results(\"kmctof2.dat\")\n",
    "print(f\"unknowns of the two-carrier system: {grw2['unknowns']:.0f}\")\n",
    "print(f\"first arrival: grw {grw2['tau_mean']:.3e} s, KMC {tof2['first_mean']:.3e} +- {tof2['first_error']:.1e} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8b497f",
   "metadata": {},
   "source": [
    "## Bulk transport: `kmcmultiple`\n",
    "\n",
    "In the periodic box (no cut) a fixed number of carriers moves through the periodic images. `kmcmultiple` follows their unwrapped displacements and reports the drift velocity, the mobility along the field and the diffusion tensor. With `exact` (default) it also solves the master equation for a single carrier; for one carrier the KMC must agree with it.\n",
    "\n",
    "`runtime` is a simulated time in seconds or, above 100, a number of KMC events. Here: 4 independent runs of $2\\cdot10^6$ events, so that errors come from the spread over runs. `outputtime=20` samples run 0 every 20 hops for the time file (mean energy of the carriers, distance travelled)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c743e18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:09.100616Z",
     "iopub.status.busy": "2026-11-10T21:08:09.100118Z",
     "iopub.status.idle": "2026-11-10T21:08:10.227054Z",
     "shell.execute_reply": "2026-11-10T21:08:10.226358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\r\n",
      "========   VOTCA (http://www.votca.org)   ========\r\n",
      "==================================================\r\n",
      "\r\n",
      "please read and cite: https://doi.org/10.21105/joss.06864\r\n",
      "and submit bugs to https://github.com/votca/votca/issues\r\n",
      "\r\n",
      "xtp_run, version 2026-dev gitid: ca10e5f (compiled Nov 10 2026, 20:22:31)\r\n",
      "\r\n",
      "Initializing calculator\r\n",
      "... kmcmultiple\r\n",
      "1 frames in statefile, Ids are: 0 \r\n",
      "Starting at frame 0\r\n",
      "Evaluating frame 0\r\n",
      "Import MD Topology (i.e. frame 0) from lattice.hdf5\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... \r\n",
      "... kmcmultiple\r\n",
      " Using 1 threads\r\n",
      " ... \r\n",
      "-----------------------------------\r\n",
      "      KMC FOR MULTIPLE CHARGES\r\n",
      "-----------------------------------\r\n",
      " ... Bulk hopping graph (periodic in a, b and c, no sinks) for holes\r\n",
      "  sites: 1000 (0 of ignored types), hops: 18000\r\n",
      "  injection sites: 1000\r\n",
      "  detailed balance: no (max deviation of ln(w_ij/w_ji) 7.73635)\r\n",
      " ... Rates written to rates.dat\r\n",
      " ... 1 hole(s), density 0.001 nm^-3, 4 run(s), 1 thread(s)\r\n",
      " ... Each run stops after 2000000 KMC events (hops and attempts into occupied sites)\r\n",
      " ... Waiting times: exponential"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... 8000000 hops, 0 attempts into occupied sites (0 %), 0.444735 s\r\n",
      " ... Observation window of run 0: 0 s to 4.31006e-06 s, 76 blocks of 5.62341e-08 s\r\n",
      " ... Drift velocity (nm/s): 3.592950e+10 2.264056e+09 4.958845e+08\r\n",
      "    +- 2.307981e+08 5.652040e+07 3.641007e+07 (standard error over runs)\r\n",
      " ... Mobility along the field: 1.796475e-02 +- 1.153990e-04 cm^2/Vs (1.796475e+12 nm^2/Vs)\r\n",
      " ... Diffusion tensor (nm^2/s):\r\n",
      " 2.541074e+11  3.253492e+09  5.903278e+09\r\n",
      " 3.253492e+09  4.855484e+10 -2.689300e+08\r\n",
      " 5.903278e+09 -2.689300e+08  4.518460e+10\r\n",
      " ...   +- (standard error over runs)\r\n",
      "8.260102e+09 9.910417e+09 4.713812e+09\r\n",
      "9.910417e+09 2.945042e+09 1.660013e+09\r\n",
      "4.713812e+09 1.660013e+09 1.689252e+09\r\n",
      " ...   eigenvalue 4.498102e+10 nm^2/s, direction  2.965317e-02 -1.018210e-01 -9.943607e-01\r\n",
      " ...   eigenvalue 4.854059e+10 nm^2/s, direction  1.280679e-02 -9.946778e-01  1.022354e-01\r\n",
      " ...   eigenvalue 2.543253e+11 nm^2/s, direction 9.994782e-01 1.576618e-02 2.819135e-02"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... Exact for one carrier (master equation, sparse LU, 1000 sites, backward error 3.662612e-14):\r\n",
      "  drift velocity (nm/s): 3.573738e+10 2.274177e+09 4.531325e+08\r\n",
      "  diffusion tensor (nm^2/s):\r\n",
      " 2.468030e+11  1.612440e+10  5.629060e+09\r\n",
      " 1.612440e+10  5.138358e+10 -6.576121e+08\r\n",
      " 5.629060e+09 -6.576121e+08  4.614755e+10\r\n",
      " ...   mobility along the field: 1.786869e-02 cm^2/Vs\r\n",
      " ...   KMC - exact mobility: 8.324248e-01 standard errors\r\n",
      " ... Occupation probabilities written to occupation.dat"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... Trajectory of run 0 written to trajectory.csv"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changes have not been written to state file.\r\n",
      "\r\n",
      "==============================================================================\r\n",
      "Please cite the methods used in this run:\r\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\r\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\r\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\r\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\r\n",
      "      doi:10.21105/joss.06864\r\n",
      "  [2] Ruehle, Lukyanov, May, Schrader, Vehoff, Kirkpatrick, Baumeier, and\r\n",
      "      Andrienko, Microscopic simulations of charge transport in disordered\r\n",
      "      organic semiconductors, J. Chem. Theory Comput. 7, 3335 (2011),\r\n",
      "      doi:10.1021/ct200388s\r\n",
      "==============================================================================\r\n"
     ]
    }
   ],
   "source": [
    "!xtp_run -e kmcmultiple -c carriertype=hole field=\"2e7 0 0\" numberofcarriers=1 runs=4 runtime=2000000 outputtime=20 timefile=time.dat -f lattice.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ab0f929",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:10.229678Z",
     "iopub.status.busy": "2026-11-10T21:08:10.229107Z",
     "iopub.status.idle": "2026-11-10T21:08:10.423902Z",
     "shell.execute_reply": "2026-11-10T21:08:10.423386Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMC mobility 1.796e-02 +- 1.2e-04 cm^2/Vs, exact 1.787e-02 cm^2/Vs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bulk = results(\"kmcmultiple_results.dat\")\n",
    "print(f\"KMC mobility {bulk['mobility']:.3e} +- {bulk['mobility_error']:.1e} cm^2/Vs, exact {bulk['exact_mobility']:.3e} cm^2/Vs\")\n",
    "\n",
    "t, steps, energy = np.loadtxt(\"time.dat\", skiprows=1, usecols=(0, 1, 2)).T\n",
    "# average the energy over logarithmic time bins\n",
    "edges = np.logspace(np.log10(t[1]), np.log10(t[-1]), 30)\n",
    "which = np.digitize(t, edges)\n",
    "bins = [b for b in range(1, len(edges)) if np.any(which == b)]\n",
    "plt.semilogx([t[which == b].mean() for b in bins], [energy[which == b].mean() for b in bins], \"o-\")\n",
    "plt.axhline(E.mean(), color=\"gray\", ls=\":\", label=\"mean site energy\")\n",
    "plt.xlabel(\"time (s)\")\n",
    "plt.ylabel(\"energy of the carrier (eV)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5442c995",
   "metadata": {},
   "source": [
    "The carrier starts on a random site (on average at the mean site energy) and relaxes into the low-energy tail of the density of states within a fraction of a nanosecond, i.e. within the first few samples; afterwards its energy fluctuates around the equilibrium value. For an infinite Gaussian density of states this would be $\\bar\\varepsilon-\\sigma^2/k_BT$, 0.39 eV below the mean; in 1000 sites the few deepest sites set it. In long runs this transient hardly matters; otherwise the option `equilibration` leaves it out of the averages.\n",
    "\n",
    "### Carrier density\n",
    "\n",
    "With more carriers, the deepest traps fill up and the remaining carriers move faster, while exclusion (a site holds one carrier) starts to block hops. KMC includes both; the exact value is that of an isolated carrier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2270dfc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:10.425872Z",
     "iopub.status.busy": "2026-11-10T21:08:10.425681Z",
     "iopub.status.idle": "2026-11-10T21:08:17.071874Z",
     "shell.execute_reply": "2026-11-10T21:08:17.071269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "carriers = [1, 5, 20, 50, 100, 200]\n",
    "mu, err = [], []\n",
    "for n in carriers:\n",
    "    run(f'xtp_run -e kmcmultiple -c carriertype=hole field=\"2e7 0 0\" numberofcarriers={n} runs=4 runtime=2000000 outputtime=0 exact=false resultfile=density.dat ratefile=x occfile=x -f lattice.hdf5')\n",
    "    r = results(\"density.dat\")\n",
    "    mu.append(r[\"mobility\"])\n",
    "    err.append(r[\"mobility_error\"])\n",
    "density = np.array(carriers) / 1000  # carriers per site\n",
    "plt.errorbar(density, mu, yerr=err, fmt=\"o-\", label=\"KMC\")\n",
    "plt.axhline(bulk[\"exact_mobility\"], color=\"k\", ls=\"--\", label=\"one carrier, exact\")\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(\"carriers per site\")\n",
    "plt.ylabel(\"mobility (cm$^2$/Vs)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df7ef1e",
   "metadata": {},
   "source": [
    "## Why ToF and bulk mobilities differ\n",
    "\n",
    "The ToF mobility from `grw` above is higher than the bulk mobility of the same lattice. In a thin slab the carriers cross the sample before they have relaxed into the deep tail of the density of states (dispersive transport); the bulk value describes carriers in equilibrium. Making the slab thicker along the transport direction shows the approach of the ToF mobility to the bulk value. For every slab we compare with the bulk mobility of the same lattice (the exact single-carrier value from `kmcmultiple`, whose KMC part we keep minimal), for three disorder realizations (`energy.seed`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98a14a96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:17.074154Z",
     "iopub.status.busy": "2026-11-10T21:08:17.073775Z",
     "iopub.status.idle": "2026-11-10T21:08:50.889824Z",
     "shell.execute_reply": "2026-11-10T21:08:50.889301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lengths = [6, 10, 20, 40, 80]\n",
    "for seed in [1, 2, 3]:\n",
    "    ratio = []\n",
    "    for n in lengths:\n",
    "        name = f\"slab_{n}_{seed}\"\n",
    "        run(f'xtp_tools -e latticegen -c job_name={name} dims=\"{n} 10 10\" energy.sigma=0.1 energy.seed={seed}')\n",
    "        run(f'xtp_run -e grw -c carriertype=hole field=\"2e7 0 0\" resultfile=slab_tof.dat sourcefile=x occupationfile=x -f {name}.hdf5')\n",
    "        run(f'xtp_run -e kmcmultiple -c carriertype=hole field=\"2e7 0 0\" runtime=1000 outputtime=0 resultfile=slab_bulk.dat ratefile=x occfile=x -f {name}.hdf5')\n",
    "        ratio.append(results(\"slab_tof.dat\")[\"mobility_mean\"] / results(\"slab_bulk.dat\")[\"exact_mobility\"])\n",
    "    plt.semilogx(lengths, ratio, \"o-\", label=f\"seed {seed}\")\n",
    "plt.axhline(1, color=\"k\", ls=\"--\")\n",
    "plt.xlabel(\"number of layers along the transport direction\")\n",
    "plt.ylabel(\"ToF mobility / bulk mobility\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c14c65",
   "metadata": {},
   "source": [
    "Thin slabs give ToF mobilities 25-80 % above the bulk value; from about 20 layers on, the two agree within about 10-30 %, with a large scatter between realizations. The bulk mobility itself varies by up to an order of magnitude between realizations of these small lattices, because it is set by the few deepest sites: real studies need large systems and several realizations.\n",
    "\n",
    "## Exciton diffusion with finite lifetimes: `kmclifetime`\n",
    "\n",
    "Excitons hop like charges (here: singlets with the same rate parameters) but decay with a site-dependent rate. `kmclifetime` reads the lifetime of every site from an XML file. We first give all sites a lifetime of 1 ns, then turn 2 % of the sites into quenchers with a lifetime of 1 ps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24d80d46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:50.891985Z",
     "iopub.status.busy": "2026-11-10T21:08:50.891778Z",
     "iopub.status.idle": "2026-11-10T21:08:50.903102Z",
     "shell.execute_reply": "2026-11-10T21:08:50.902650Z"
    }
   },
   "outputs": [],
   "source": [
    "def write_lifetimes(filename, lifetimes):\n",
    "    with open(filename, \"w\") as f:\n",
    "        f.write(\"<lifetimes>\\n\")\n",
    "        for i, tau in enumerate(lifetimes):\n",
    "            f.write(f'  <site id=\"{i}\">{tau:.6e}</site>\\n')\n",
    "        f.write(\"</lifetimes>\\n\")\n",
    "\n",
    "\n",
    "lifetimes = np.full(1000, 1e-9)\n",
    "write_lifetimes(\"lifetimes.xml\", lifetimes)\n",
    "rng = np.random.default_rng(3)\n",
    "quenchers = rng.choice(1000, size=20, replace=False)\n",
    "lifetimes[quenchers] = 1e-12\n",
    "write_lifetimes(\"lifetimes_quenched.xml\", lifetimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fcf24db0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:50.905208Z",
     "iopub.status.busy": "2026-11-10T21:08:50.904766Z",
     "iopub.status.idle": "2026-11-10T21:08:51.906297Z",
     "shell.execute_reply": "2026-11-10T21:08:51.905720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\r\n",
      "========   VOTCA (http://www.votca.org)   ========\r\n",
      "==================================================\r\n",
      "\r\n",
      "please read and cite: https://doi.org/10.21105/joss.06864\r\n",
      "and submit bugs to https://github.com/votca/votca/issues\r\n",
      "\r\n",
      "xtp_run, version 2026-dev gitid: ca10e5f (compiled Nov 10 2026, 20:22:31)\r\n",
      "\r\n",
      "Initializing calculator\r\n",
      "... kmclifetime\r\n",
      "1 frames in statefile, Ids are: 0 \r\n",
      "Starting at frame 0\r\n",
      "Evaluating frame 0\r\n",
      "Import MD Topology (i.e. frame 0) from lattice.hdf5\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... \r\n",
      "... kmclifetime\r\n",
      " Using 1 threads\r\n",
      " ... \r\n",
      "-----------------------------------\r\n",
      "      KMC WITH FINITE LIFETIMES\r\n",
      "-----------------------------------\r\n",
      " ... Bulk hopping graph (periodic in a, b and c, no sinks) for singlets\r\n",
      "  sites: 1000 (0 of ignored types), hops: 18000\r\n",
      "  injection sites: 1000\r\n",
      "  detailed balance: yes (max deviation of ln(w_ij/w_ji) 7.10543e-15)\r\n",
      " ... Rates written to rates.dat"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... Exact for one singlet (sparse LU, backward error 6.11635e-11):\r\n",
      " ...   lifetime 1.000000e-09 s, diffusion length sqrt(<R^2>) 1.545079e+01 nm, per direction sqrt(<R_a^2>) 8.904415e+00 9.153696e+00 8.697594e+00 nm\r\n",
      " ... 1 singlet(s) at a time, 20000 decays in 4 run(s), 1 thread(s), waiting times: exponential"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... 20000 decays, 9099297 hops, 0 attempts into occupied sites, 1.994883e-05 s simulated, 5.648991e-01 s\r\n",
      " ... Average lifetime: 9.974416e-10 +- 7.082321e-12 s (standard error)\r\n",
      "  hops per singlet: 4.549649e+02\r\n",
      "  mean distance <|R|>: 1.262636e+01 +- 6.274532e-02 nm\r\n",
      "  diffusion length sqrt(<R^2>): 1.543245e+01 +- 8.386178e-02 nm\r\n",
      "  per direction sqrt(<R_a^2>): 8.812034e+00 9.143360e+00 8.769700e+00 nm\r\n",
      "  D = <R^2>/(6 <lifetime>): 3.979525e+10 nm^2/s\r\n",
      " ...   KMC - exact: lifetime -3.612327e-01 standard errors, <R^2> -2.187758e-01 standard errors"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      " ... Decays written to trajectory.csvChanges have not been written to state file.\r\n",
      "\r\n",
      "==============================================================================\r\n",
      "Please cite the methods used in this run:\r\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\r\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\r\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\r\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\r\n",
      "      doi:10.21105/joss.06864\r\n",
      "  [2] Ruehle, Lukyanov, May, Schrader, Vehoff, Kirkpatrick, Baumeier, and\r\n",
      "      Andrienko, Microscopic simulations of charge transport in disordered\r\n",
      "      organic semiconductors, J. Chem. Theory Comput. 7, 3335 (2011),\r\n",
      "      doi:10.1021/ct200388s\r\n",
      "  [3] Wehner and Baumeier, Multiscale simulations of singlet and triplet\r\n",
      "      exciton dynamics in energetically disordered molecular systems based on\r\n",
      "      many-body Green's functions theory, New J. Phys. 22, 033033 (2020),\r\n",
      "      doi:10.1088/1367-2630/ab7a04\r\n",
      "==============================================================================\r\n"
     ]
    }
   ],
   "source": [
    "!xtp_run -e kmclifetime -c numberofinsertions=20000 runs=4 -f lattice.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f8d27f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:51.908749Z",
     "iopub.status.busy": "2026-11-10T21:08:51.908288Z",
     "iopub.status.idle": "2026-11-10T21:08:52.550415Z",
     "shell.execute_reply": "2026-11-10T21:08:52.549895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "without quenchers: lifetime 9.974e-10 s (exact 1.000e-09), diffusion length 15.43 nm (exact 15.45)\n",
      "with 2 % quenchers: lifetime 4.707e-10 s (exact 4.755e-10), diffusion length 11.59 nm (exact 11.61)\n",
      "fraction of the excitons that decay on a quencher: KMC 0.524, exact 0.525\n"
     ]
    }
   ],
   "source": [
    "run(\"xtp_run -e kmclifetime -c lifetimefile=lifetimes_quenched.xml numberofinsertions=20000 runs=4 decayprobfile=decayprob.dat resultfile=quenched.dat trajectoryfile=x -f lattice.hdf5\")\n",
    "pure, quenched = results(\"kmclifetime_results.dat\"), results(\"quenched.dat\")\n",
    "for name, r in [(\"without quenchers\", pure), (\"with 2 % quenchers\", quenched)]:\n",
    "    print(f\"{name}: lifetime {r['lifetime']:.3e} s (exact {r['exact_lifetime']:.3e}), \"\n",
    "          f\"diffusion length {r['diffusion_length'] * 1e9:.2f} nm (exact {r['exact_diffusion_length'] * 1e9:.2f})\")\n",
    "decay = np.loadtxt(\"decayprob.dat\")\n",
    "print(f\"fraction of the excitons that decay on a quencher: KMC {decay[quenchers, 3].sum():.3f}, exact {decay[quenchers, 4].sum():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8c0509",
   "metadata": {},
   "source": [
    "Only 2 % of the sites are quenchers, yet about half of the excitons decay on one, because an exciton visits many sites during its lifetime (the diffusion length without quenchers is about 15 lattice spacings). Such estimates are the basis of measurements of exciton diffusion by fluorescence quenching.\n",
    "\n",
    "## Energetic disorder\n",
    "\n",
    "Finally, the mobility depends strongly on the width $\\sigma$ of the density of states. The Gaussian disorder model (Miller-Abrahams rates, large lattices) predicts for the equilibrium mobility at low field $\\ln\\mu \\propto -(C\\sigma/k_BT)^2$ with $C\\approx 2/3$; with Marcus rates and a small lattice we expect the same trend with a somewhat different $C$. The exact single-carrier bulk mobility from `kmcmultiple` (with a very short KMC part, which we do not use here) makes this scan quick:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ed3570f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:08:52.552752Z",
     "iopub.status.busy": "2026-11-10T21:08:52.552204Z",
     "iopub.status.idle": "2026-11-10T21:08:55.501758Z",
     "shell.execute_reply": "2026-11-10T21:08:55.501211Z"
    }
   },
   "outputs": [
    {
     "data": {
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2w/akpCQ6derEK6+8kmfZC5TbzgYnWXKpSXGtVsPYNNkwXg8wjLcqGsa52Gy8SEQke5w5KW5+ioyMNID0x7UJaW+cVLV169YZ9snJV+OCBQsMwFiyZEmmbTabzTh//rwTf5rrYmJiDMD48ccf09dt27bNAIz//e9/Wb4uMjLS6NatW7bOcbMJdwsSZ02KqxalwsLNDVo8D0+shp4z7ANWXqNLcSKSl1IvZf24kpyDfS9nb98ceO+995gwYQIVK1YkLi7uplcCvv76aypWrMiECRPSW1musVgszJs3L8vjf/bZZ9x1111069Yt0zaLxYK/v3+Wry1RosQtH507d87ytdu2bcPf35+mTZumr7vnnnvw9/dn69atWb6Oq5PxBgUFUaNGDQYPHkx8fPwt9y/qNDxAYVPxb2MsHfoeNk+BntPBv6JZqUSkMHurfNbb7uwAj355ffnf1e3dBG6mSgt4fMX15XdrQ9LZzPuNT8h2NH9/f/z8/HB3dyc4OPim+wQEBODu7o6fn1+mfe66665bFjuHDh3irrvuynaeG0VH33qmBV9f3yy3nT59mqCgoEzrg4KCOH36dJav69y5M71796ZKlSrExsYybtw47rvvPnbt2oW3t3cOf4KiQYVSYWazwncvwrnfYOq90PUDCOtqdioRkQLjf//73y23G4aBxWLJ1bGrV6+ey1R2Nzvv7fL06dMn/Xl4eDiNGjWiSpUqrFixgp49ezqUp7BSoVSYublD/0Ww6Ek4uQsWDoAGkdDpbfAqZnY6ESksXjmV9TaLe8blFw/fYt+/9QYZudfBYHmvRo0aHDhwIFevLVGixC23t2zZku++++6m24KDg/njjz8yrf/zzz8pV65ctjOEhIRQpUoVDh3S8DJZUaFU2AXcAU+sgnVvwuZ3Yfd8OP4j9JoNwbXNTicihYFXcfP3dZCXlxdWqzXHr+vXrx+PPPIIS5cuzdRPyTAMEhMTs7x058ilt2bNmpGQkMD27dtp0qQJAD/99BMJCQk0b9482/nPnj3LiRMnCAkJyfZrihp15i4K3D2h3Xh4bAmUCIYzB2HmfXD2iNnJRERcQtWqVdm4cSMnT57kzJkz6etr1qzJ4sWLs3zdww8/TJ8+fejbty8TJ05k586dHDt2jG+++YZ27dqxbt26LF9bvXr1Wz4qVKiQ5WvvvvtuOnXqxODBg/nxxx/58ccfGTx4MA8++GCGPlM35r948SKjR49m27ZtHD16lPXr1xMREUFgYCA9evRIf83p06eJjo7m8GF769/evXuJjo7m3LlzOfiNFh4qlIqSO9rYx1yq0RnCH4Iy1cxOJCLiEiZMmMDRo0epVq0aZcuWTV9/8OBBEhKy7jxusVj473//y+TJk1m8eDGtW7emTp06jB8/nm7dutGxY8c8y/zZZ59Ru3ZtOnToQIcOHahTpw6ffPJJhn1uzO/u7s7evXvp1q0bNWrUIDIykho1arBt27YMY0pNmzaN+vXrM3jwYABatWpF/fr1WbZsWZ79LK7MYhi6dzy3rjWpJiQkULJkyWy8wkUYBlhTwePqHQ6XzsAf++yFlIjITSQnJxMbG0toaCg+Pj5mxxG5rVv9zebk+1stSkWRxXK9SDIMWDIUPu4Gq8dBWqrZ6URERFyGCqWiznrl+vhKW9+HOR3Ud0lEROQqFUq54BKT4jqLhxc8OAX6fAo+peDUHpjWEqL/qxG9RUSkyFOhlAsuNSmus9wdYe/oXaUFXLlkvxy36ElITjQ7mYiIiGlUKMl1/hUgchm0/T/7IHEnd5qdSERcjO7/kYLCWX+rGnBSMnJzh9Yvwh2twc0DfK7eDWAYYNjs20WkyPH09AQgKSnplgMhiriKpCT7nILX/nZzS4WS3FylJhmXt8+EA8ug5wwoeYsJMEWkUHJ3d6dUqVLpM80XK1Ys13OcieQlwzBISkoiPj6eUqVK4e7u2D/wVSjJ7aVchA1v22fxntocukVBzQfMTiUi+Sw4OBggvVgScWWlSpVK/5t1hAacdECBHXAyN84chkWDIO7q3ESNn4QOb4CnmuBFihqr1cqVK1fMjiGSJU9Pz1u2JOXk+1uFkgOKVKEE9sEo106ArR/Yl4PC4KHZUC7M7GQiIiLZppG5JW94eNlbkfp/DcWDID4GZreHpKI5UaKIiBR+6qMkOVf9fvuYS0uGQuWmUCzA7EQiIiJ5QoWS5E6JstBvYcZ18Qfg0p8Q2sqsVCIiIk6lS2+Se25u9gfAlcvw1RMwvyv8MME+h5yIiEgBp0JJnMMwoGIjwIBN78CcTnAu1uxUIiIiDlGhJM7hVQy6fgC954G3v336k2kt4ZcvzU4mIiKSayqUxLlq9YChm6HSPZB6Ab5+Er5+GlKTzE4mIiKSYyqUxPlKVYaBK6D1GLC4QcIJ8PBO32y1GWw7cpal0SfZduQsVpuG8hIREdeku95yISoqiqioKKxWq9lRXJe7B7R92T65bqnK6ZPprvrlGK9/8yunElPTdw3x9+G1iDA6hYeYGFhERCQzjcztgCI3MreDVu6L4/QXz3GH5RQvXBnCn5QG4Nq0mlP7N1CxJCIieU4jc4vLsdoMopZt5mH39bRy38tK7zG0ddsD9vvkAHh9eYwuw4mIiEtRoST5YnvsOfYmFiMi9Q3226pQxnKBuV7/5jWP+XiTigHEJSSzPVbToYiIiOtQoST5Iv5CMgBHjAr0SJ3A7LTOADzusYrFXq9RzXIyw34iIiKuQIWS5IsgP5/056l48s+0AQxMfZEzRknC3I7xmddbeHElw34iIiJmU6Ek+aJJaAAh/j7pHbcB1tvq0znlbTZaazPhymOU8fejSagm2BUREdehQknyhbubhdciwuCGu9wA/qQUkVfG8J2tKa9FhOHuZoEj6+DoFtOyioiIXKNCSfJNp/AQpvZvQLB/xstrwf6+14cGuPAHLHoS5j8Ia98Ea5ppeUVERDSOkgM0jlLuWG0G22PPEX8hmSA/H5qEBthbkgBSLsJ3L0H0Z/blSk2h50woXcXUzCIiUnjk5PtbhZIDVCjlob1fwTfPQ0qifZLdiHchvKfZqUREpBDQgJNS8NXuBUM2QcXGkJIAXz0OS4fpUpyIiOQrFUriukpXhce/g5aj7V3AjatzyImIiOQTfeuIa3P3hPvHQfX7IbjO9fUpF8GzGLip1hcRkbyjQkkKhirNrz+32eDLSPvz7lOhRJBpsUREpHDTP8el4ImPgaOb4fD3MPVe+39FRETygAolKXiCw+Gp9RBUCy7Fw6cPwaqxkJZidjIRESlkVChJwRR0Nwz+AZo8ZV/e9iHMagdnDpmdTEREChEVSlJwefpCl3/DI5+DbwCc/gW+fBw0NJiIiDiJCiUp+Gp2gaFboHo76PoeWCzZeJGIiMjt6a43KRxKlof+izKui/4vBFSDyk3NSiUiIgWcWpSkcDq9D5Y/B3M7w4ZJYLOanUhERAogFUq5EBUVRVhYGI0bNzY7imSlVGWo1QMMK6x7E+ZHQMLvZqcSEZECRpPiOkCT4hYAP38BK16A1IvgUwq6fgBhXc1OJSIiJtKkuCLX1H3EPrluhYaQfB4WDoAVo81OJSIiBYQKJSn8Au6AJ1ZBi+ftk+uWLG92IhERKSB015sUDe6e0G481IyA8vWvr790BoqV0ZACIiJyU2pRkqKlYkNwu/pnn5oE8x6A//axF0wiIiJ/o0JJiq6Tu+BcLBxaBVObw5G1ZicSEREXo0JJiq7QljB4LZStCRf/gE96wJpXIS3V7GQiIuIiVChJ0RYcDoPXQcPH7ctb3oM5HeDsEbOTiYiIC1ChJOJVDCLehYc/sY+1dGoPrHrF7FQiIuICdNebyDVhXaFCA/juH9B5ktlpRETEBahFSeRG/hXhkc/Av8L1dRv/A7/vNDOViIiYRIWSyK38uhrW/hPmdIRN72hyXRGRIkaFksitVGoCtXqCLQ1+mAAfd4PEU2anEhGRfKJCSeRWfEtBrznQ7SPwLA5HN8HUe+F/35qdTERE8oEKJZHbsVig/qPw9EYIqQuXz8EXfeH7181OJiIieUyFkkh2BVaHQd9Ds2ftyzfOGSciIoWShgcQyQkPL+j4JtQfAEE1r68/fxz8K2lyXRGRQkYtSiK5cWORdOE0zGgDXzwKSefMTCUiIk6mQknEUb/vgOREOLjCPrlu7EazE4mIiJOoUBJx1N0RMPgHKHMnXIiD+V3tQwlYr5idTEREHKRCScQZQurC0xugwWOAYR+cck4nOBdrdjIREXGACiURZ/EqDl0/gN7zwNsfTu6EbVFmpxIREQc45a63K1eucPr0aZKSkihbtiwBAQHOOKxIwVSrB1RoCOsmQnuNtSQiUpDlukXp4sWLTJ8+nTZt2uDv70/VqlUJCwujbNmyVKlShcGDB7Njxw7nphUpKEpVhh5T7a1MADYbLB8JJ3ebnUxERHIgV4XSlClTqFq1KjNnzuS+++7j66+/Jjo6moMHD7Jt2zZee+010tLSaN++PZ06deLQoUPOTy5SkOycDbvmwuz2sOU9e+EkIiIuz2IYhpHTF/Xu3ZtXX32V2rVr33K/lJQUZs+ejZeXF08++aQjOV1SYmIi/v7+JCQkULJkSbPjiCu7/Bcsfw5iltqX72gLPaaBX7DZyUREipycfH/nqlASOxVKkiOGAbs/hu/+AWmXoVggdJ8KNTqYnUxEpEjJyfe37noTyS8WCzSMtA8jUK42JJ2B//aGze+anUxERLLgcKF0+fJlkpKS0pePHTvGu+++y+rVqx09tEjhVPYuePJ7aDoU3L3gjtZmJxIRkSw4XCh169aNjz/+GIDz58/TtGlT3nnnHbp168bUqVOdkVGk8PH0gc5vw/BdUL7+9fXxB+yX6ERExCU4XCjt3r2bli1bAvDVV19Rrlw5jh07xscff8z777/vjIwihVepytefx/0M01vBwsfsnb9FRMR0DhdKSUlJ+Pn5AbB69Wp69uyJm5sb99xzD8eOHXNGRpGi4fQ+MGxwYBlMbQHHtpqdSESkyHO4UKpevTpLlizhxIkTrFq1ig4d7HfwxMfHF4g7wXr06EHp0qXp1auX2VGkqKv/KAxaAwF3QOLvMO8BWPcWWNPMTiYiUmQ5XCi9+uqrjB49mqpVq9K0aVOaNWsGV1uX6tevf9vXm23EiBHpfaxETFehATy9Eer2s7cubfiXvWA6f9zsZCIiRVKuC6Xo6GgAevXqxfHjx9m5cycrV65M337//fczZcoU56TMQ23btk2/dCjiErz97NOf9JwF3iXhxI/XB6oUEZF8letCqUGDBjRs2JCpU6fi6+tL/fr1cXO7frgmTZpQs2ZNh8Jt3LiRiIgIypcvj8ViYcmSJZn2+eijjwgNDcXHx4eGDRuyadMmh84p4jLq9IYhm6DZs3DPMLPTiIgUSbkulLZs2UKDBg0YM2YMISEh9O/fn3Xr1jk13KVLl6hbty4ffvjhTbcvWLCAkSNHMnbsWPbs2UPLli3p3Lkzx49fv0zRsGFDwsPDMz1OnTrl1KwieaJ0Vej4Jlz7R0hqEnzez36HnIiI5DmHpzC5fPkyCxcuZO7cuWzatImqVavyxBNPEBkZScWKFZ0X1GJh8eLFdO/ePX1d06ZNadCgQYbxmu6++266d+/OxIkTs33s9evX8+GHH/LVV1/dcr+UlBRSUlLSlxMTE6lUqZKmMJH8s+ZV+6S67l7Qbrx90Eo3DbAvIpIT+TqFia+vL5GRkaxfv55ff/2Vvn37Mn36dEJDQ+nSpYujh89Samoqu3btSr/L7poOHTqwdWve3FY9ceJE/P390x+VKlXKk/OIZOnekXDXA2BNhVWv2KdAuRhvdioRkULLqf8UrVatGmPGjGHs2LGULFmSVatWOfPwGZw5cwar1Uq5cuUyrC9XrhynT5/O9nE6duxI7969+fbbb6lYsSI7duzIct+XX36ZhISE9MeJEycc+hlEcqxYADzyGTzwDnj4wOHvYeq99v+KiIjTeTjrQBs2bGDOnDksWrQId3d3Hn74YQYNGuSsw2fJYrFkWDYMI9O6W8lJMeft7Y23t3eO8ok4ncUCjZ+Eys1h0SCIj4FPH4KI9+2T7oqIiNM4VCidOHGCefPmMW/ePGJjY2nevDkffPABDz/8MMWLF3deypsIDAzE3d09U+tRfHx8plYmkUKpXBgMXmvvt7RvEdzZIRsvEhGRnMj1pbf27dsTGhrKRx99RK9evThw4ACbN2/m8ccfz/MiCcDLy4uGDRuyZs2aDOvXrFlD8+bN8/z8Ii7B0xe6/BuGbYeSIdfXH/9Jk+uKiDhBrluUfH19WbRoEQ8++CDu7u7OTXXVxYsXOXz4cPpybGws0dHRBAQEULlyZUaNGsWAAQNo1KgRzZo1Y8aMGRw/fpwhQ4bkSZ5roqKiiIqKwmq15ul5RLKteOD15weWw4L+UKsHPPgu+JYyM5mISIGW60KpVq1ahISE5FmRBLBz507atm2bvjxq1CgAIiMjmTdvHn369OHs2bNMmDCBuLg4wsPD+fbbb6lSpUqeZQIYNmwYw4YNS7+9UMSlJJ4CizvsXwy/74KHZkHlpmanEhEpkHI9jtLjjz/OihUrcHd3JyIigm7dutGuXbsi1dk5J+MwiOSr33fCV0/A+WP2oqnNGGj5Arjl3T9sREQKinwZR2nu3Ln88ccfLFy4kFKlSvHCCy8QGBhIz549mTdvHmfOnMntoUXEURUbwZDNUPthMKyw7k2YHwEJv5udTESkQHF4ZO4bHThwgOXLl7N06VJ27txJ06ZN6dq1K3379qVChQrOOo3LUIuSFAg/fwErXoDUi9DnM7j7QbMTiYiYKiff304tlG70559/smzZMpYtW0bLli0ZPXp0XpzGVCqUpMA4ewQOfgvNh5udRETEdHlaKF2+fJlz585laiHav38/tWrVyl3iAkqFkhRYiadg0ZPQeRIEh5udRkQkX+VZH6WvvvqKGjVq0KVLF+rUqcNPP/2Uvm3AgAG5T1zAREVFERYWRuPGjc2OIpI7q8fBsS0w8z74aYbGXBIRyUKOWpTq1avHmjVrKFu2LDt37iQyMpKxY8fSr18/6tevz549e/I2rYtRi5IUWJfOwJJn4NDVKXxqdIJuURnHYxIRKaTyrEXpypUrlC1bFoBGjRqxceNGpk+fzoQJE3I0v5qImKx4IPRbYL/05u4Nv660T67723qzk4mIuJQcFUpBQUH88ssv6ctlypRhzZo1HDhwIMN6EXE9VpvBtiNnWRp9km1HzmI1gKZP2+eLC7wLLp6Gj7vbR/YWERHI6aW333//HQ8PD4KDgzNt27JlC/fee6+z87k0XXqTgmLlvjheXx5DXEJy+roQfx9eiwijU3gIpCbBqpfh2DZ4aj14FTM1r4hIXnKJ4QGKAhVKUhCs3BfH0E938/cP+rWL5VP7N7AXSwApF8Dbz/7cZoMja+HOdvmaV0Qkr+Xk+zvXc73dKDk5mV9++YX4+HhsNluGbV27dnXGKUQkF6w2g9eXx2QqkgCMq8XS68tjaB8WjLub5XqRBLD1ffj+Nfvo3g+8Az76x4CIFD0OF0orV67kscceu+mUJRaLBavV6ugpXE5UVBRRUVGF8meTwmV77LkMl9v+zgDiEpLZHnuOZtXK/G2jzT5P3N6F8Pt2eGi2fWoUEZEiJNdzvV3z7LPP0rt3b+Li4rDZbBkehbWQGDZsGDExMezYscPsKCK3FH8h6yLptvu1HAWPfwf+leGvozCnI2x6B2yF83MtInIzDhdK8fHxjBo1inLlyjknkYg4TZCfj2P7VW4KQzZBrZ5gS4MfJsDH3ewje4uIFAEOF0q9evVi/XqNvSLiipqEBhDi70NWo5xZrt791iQ0IOuD+JaCXnOg64fgWQxO/ASX/syryCIiLsXhu96SkpLo3bs3ZcuWpXbt2nh6embYPmLECEczuizd9SYFwbW73rjaJ+mam971djtnDkHcz1C71/V1hgEacFZECpB8HR5g1qxZDBkyBF9fX8qUKZNhhG6LxcJvv/3myOFdmgolKShuO45Sbp3aA8tHQo9pEHS3c8KKiOSxfC2UgoODGTFiBGPGjMHNzeEreQWKCiUpSKw2g+2x54i/kEyQn/1ym7ubgy1BczrD8a3g4QMd34RGg9S6JCIuL18LpYCAAHbs2EG1atUcOUyBpEJJiryL8bBkKBz+3r5c80Ho+gEUu0WfJxERk+XZpLg3ExkZyYIFCxw9jIgURCWCoN+X0PEtcPOE/31jn1w3dqPZyUREnMLhASetViuTJk1i1apV1KlTJ1Nn7smTJzt6CpejASdFbuDmBs2GQdUW8NUTcPYwzO9qH4OpSjOz04mIOMThS29t27bN+uAWC2vXrnXk8C5Nl95E/iblIqz8ByTGwaNf2YsoEREXo0lx84kKJZEspKWAh7f9eeolOLIO7n7Q7FQiIpDffZRERDK5ViQBrBwDCx6FJc/YW5xERAoQhwuliRMnMmfOnEzr58yZw7/+9S9HDy8iBZlhgF95sLhB9GcwvZV97CURkQLC4UJp+vTp1KxZM9P6WrVqMW3aNEcPLyIFmcUCbV+GgSugZEU4dwRmtYct74PNZnY6EZHbcrhQOn36NCEhmUf2LVu2LHFxcY4eXkQKgyrNYehmuLsr2K7AmnHwaU+48IfZyUREbsnhQqlSpUps2bIl0/otW7ZQvnx5Rw8vIoWFb2l4+GOIeA88fOH0L3+bfU5ExPU4PI7Sk08+yciRI7ly5Qr33XcfAD/88AMvvfQSL7zwgjMyikhhYbFAw4FQuRlc/AP8gq9vs6aBu8P/SxIRcSqH/6/00ksvce7cOZ555hlSU1MB8PHx4R//+Acvv/yyMzKKSGFT9i7745qYZbBxEjw0O+N6ERGTOW0cpYsXL3LgwAF8fX2588478fb2zsarCqYbR+b+9ddfNY6SiCNsVohqYh/R28MXOr8NDSI1ua6I5Jk8H3Dy+PHjVK5cOdv7nzx5kgoVKuT0NC5PA06KOMmF07D4afhtvX05rJu9L5NvabOTiUghlOcDTjZu3JjBgwezffv2LPdJSEhg5syZhIeH8/XXX+fmNCJSVPgFQ//F0H4CuHlAzFKY2gKObTU7mYgUcbnqo3TgwAHeeustOnXqhKenJ40aNaJ8+fL4+Pjw119/ERMTw/79+2nUqBH//ve/6dy5s/OTi0jh4uYG9z4HVVvCokFw7jeY9yA8uwPKVDM7nYgUUQ71UUpOTubbb79l06ZNHD16lMuXLxMYGEj9+vXp2LEj4eHhzk3rYnTpTSSPpFyAb18Cr+LwwH/MTiMihYwmxc0nKpRE8pjNCm7u9ucJJ+H3HVCru9mpRKSA06S4IlI4XCuSbFb4+in4MhKWPgupl8xOJiJFhAolEXF9hgGV7wEssOcTmN4a4n42O5WIFAEqlETE9bl7wP3jIHIZ+JWHs4dgVjvYFqXJdUUkT6lQEpGCI7QVDN0Cdz0A1lRY9Qr8tzcknTM7mYgUUiqURKRgKRYAj3wGXf4DHj6Q8Dt4+pqdSkQKKYcLpYEDB7Jx40bnpBERyQ6LBZoMhsHroPe864WSzQppqWanE5FCxOFC6cKFC3To0IE777yTt956i5MnTzonmQuLiooiLCyMxo0bmx1FpGgrFwZBd19f3vIezG4HZw6bmUpEChGnjKN09uxZPv30U+bNm8e+ffto164dgwYNolu3bnh6ejonqQvSOEoiLiTlIrxfHy7Fg2dx6DIJ6j2qyXVFJJN8H0epTJkyPPfcc+zZs4ft27dTvXp1BgwYQPny5Xn++ec5dOiQM04jIpI17xLw9Ab7FChXLsHSYfDVE3D5vNnJRKQAc2pn7ri4OFavXs3q1atxd3enS5cu7N+/n7CwMKZMmeLMU4mIZFayPDy2FO5/FSzusP9rmNYSjv9kdjIRKaAcvvR25coVli1bxty5c1m9ejV16tThySef5NFHH8XPzw+AL774gqFDh/LXX385K7dL0KU3ERd2Yod9ct3zx8CzGDy/337HnIgUeTn5/vZw9GQhISHYbDb69u3L9u3bqVevXqZ9OnbsSKlSpRw9lYhI9lVqDEM2wYoXoEIjFUkikisOF0rPPfccL7zwAsWKFcuw3jAMTpw4QeXKlSldujSxsbGOnkpEJGd8/KHnzIzrTu6GxJNwd4RZqUSkAHG4j9L48eO5ePFipvXnzp0jNDTU0cOLiDjGYrl+51vKBfvluAX94ZvnITXJ7HQi4uIcLpSy6uJ08eJFfHx8HD28iIjzuHtfb0naOQdmtoU/9pudSkRcWK4vvY0aNQoAi8XCq6++muHSm9Vq5aeffrppfyUREdN4eEH7CXBHW1j8NPz5P5jRFjq8YR/pW2Muicjf5LpQ2rNnD1xtUdq7dy9eXl7p27y8vKhbty6jR492TkoREWeq1haGboUlz8ChVfDdi3BkLfSaDV7FzU4nIi4k14XSunXrAHj88cd57733dHu8iBQsxQOh3wLYPgNWjwPbFfDQ5LoikpFTpjApqjSOkkghcXovlCgHJYLsy6lJ4O5pf4hIoZPn4yiNGjWKf/7znxQvXjy9r1JWJk+enJtTiIjkn+DaGZe/fRHiY+yX4gLuMCuViLiAXBVKe/bs4cqVK+nPs2JRx0gRKWgS4+B/30Dyefv0Jw+8A3UfMTuViJhEl94coEtvIoVUwu+waDAc32pfrv2wvWDy0edcpDDIyfe3UyfFLSqioqIICwujcePGZkcRkbzgXxEGfgNtXgGLG+xdCNNbwu87zU4mIvksVy1Kt+uXdKPC3EdJLUoiRcDxH2HRk5BwAvxC4LmfwcPb7FQi4oA878x9q35JN1IfJREp8CrfA0M226c8qddPRZJIEaM+Sg5Qi5JIEXbgG3Bzh7s6m51ERHJIfZRERPJSwu/2Ub0/fwRWjIYrl81OJCJ5ROMoiYjkVPGy0GAAbPsQdsyEY1ug1xwIutvsZCLiZBpHSUQkpzy8oeOb9sl1lwyxD045ow10fAsaPaHJdUUKEfVRcoD6KIkIF+NhyVA4/L19+e4I6DUP3HM9laaI5DHT+igZhoHqLhEpUkoEQb8v7a1Jbp7gV15Fkkgh4pRCafbs2YSHh+Pj44OPjw/h4eHMmjXLGYcWEXF9bm7QbBg8tR7aT7i+PukcWK+YmUxEHOTwP3vGjRvHlClTGD58OM2aNQNg27ZtPP/88xw9epQ33njDGTlFRFxfcPj15zYrfPEo2K7AQ7OgdFUzk4lILjncRykwMJAPPviAvn37Zlj/+eefM3z4cM6cOeNoRpelPkoikqU/YmBOJ0hJAO+S8OAUqN3L7FQikt99lKxWK40aNcq0vmHDhqSlpTl6eBGRgqlcGAzZBJWaQkoiLBpkH3sp5aLZyUQkBxwulPr378/UqVMzrZ8xYwaPPvqoo4cXESm4SleBgd9C63/YJ9eN/gymt4JT2ZsGSkTMl+sBJ6+xWCzMmjWL1atXc8899wDw448/cuLECR577DHnJRURKYjcPaDtKxDaGr4eDOeOwPLn4KkNGm9JpADIVR+ltm3bZu/gFgtr167NTa4CQX2URCRHks7Bdy9Byxc0ireIiXLy/a0BJx2gQklEHPbTdCgdCjU6mJ1EpMjIyfe3U0ZFO3/+PLNnz+bAgQNYLBbCwsJ44okn8Pf3d8bhRUQKp5O7YeXLYFjhnmeg3Xj79Cgi4jIc7sy9c+dOqlWrxpQpUzh37hxnzpxh8uTJVKtWjd27dzsnpYhIYRQUBk0G25//+BHMvB/+PGh2KhG5gcOX3lq2bEn16tWZOXMmHh72Bqq0tDSefPJJfvvtNzZu3OisrC5Hl95ExCkOroSlz0DSWfDwhc5vQ4NIdfYWySP52kfJ19eXPXv2ULNmzQzrY2JiaNSoEUlJSY4c3qWpUBIRp7lwGhY/Db+tty/X6QM9pqtYEskD+TrgZMmSJTl+/Him9SdOnMDPz8/Rw4uIFA1+wdB/MbR7Hdw8oEIjFUkiLsDhztx9+vRh0KBB/Oc//6F58+ZYLBY2b97Miy++mGlaExERuQU3N2gxEu7qAoF3Xl9//jj4lbePySQi+crhT91//vMfLBYLjz32WPqUJZ6engwdOpS3337bGRldTlRUFFFRUVitVrOjiEhhVLbG9efJiTDvQfALgYdmQqnKZiYTKXKcNo5SUlISR44cwTAMqlevTrFixZxxWJemPkoikueObYPPekPqBfD2h67vQa0eZqcSKdA04GQ+UaEkIvni3G+w6Ek4ucu+3OAx6PQ2eBU3O5lIgZTvhVJycjK//PIL8fHx2Gy2DNu6du3q6OFdlgolEck31iuw7i3YPAUwILAGPDQbQuqYnUykwMnXkblXrlzJY489xpkzZzJts1gs6scjIuIM7p7Q7jW4o419GIEzv8KGf8Ejn5mdTKRQc3h4gGeffZbevXsTFxeHzWbL8FCRJCLiZHe0hiFboH5/ePBds9OIFHoOF0rx8fGMGjWKcuXKOSeRiIjcWvEy0C0KSpS9vm7VWDj8g5mpRAolhwulXr16sX79euekERGRnDvwDWz7ED7taS+Y0lLNTiRSaDjcmTspKYnevXtTtmxZateujaenZ4btI0aMcDSjy1JnbhFxCVcu2wuknbPtyyF14aE5EFjd7GQiLilf73qbNWsWQ4YMwdfXlzJlymC5Ych9i8XCb7/95sjhXZoKJRFxKQe+gWXPwuW/wLM4dPk31OunqVBE/iZfC6Xg4GBGjBjBmDFjcHNz+EpegaJCSURcTsJJ+11xRzfZl5s8DV0mmZ1KxKXk66S4qamp9OnTp8gVSSIiLsm/Ajy2FO5/Fdw8oUYHsxOJFGgOVzeRkZEsWLDAOWlERMRxbu7Q8gV4Lhqqt7u+Pv4A2DRsi0hOODzgpNVqZdKkSaxatYo6depk6sw9efJkR08hIiK54V/x+vO/jsHsDhBcB3rOsLc8ichtOVwo7d27l/r16wOwb9++DNss6kAoIuIazvwKhg2ObYapzaHbh3B3hNmpRFyeJsV1gDpzi0iBcvYILBoEp/bYlxs+Dh3fAq9iZicTyVf52plbREQKiDLV4InVcO9z9uVdc2FmWzi973avFCmyVCiJiBQlHl7QfgIMWAwlysGf/4OfPzc7lYjLUqEkIlIUVbvPPrnuPc/YhxIQkZtSoSQiUlSVKAudJoKHt33ZmgYLI+E3zd8pco3DhdKnn36a5bYXX3zR0cOLiEh+2TETYpbAx91hzWtgvWJ2IhHTOVwoPfvss3zzzTeZ1j///PO3LKJERMQcVpvBtiNnWRp9km1HzmK1Xb35ucFj0CASMGDLuzCnI5wrvPN1imSHw+MoffHFFzzyyCMsW7aMVq1aATB8+HC+/vpr1q1b54yMIiLiJCv3xfH68hjiEpLT14X4+/BaRBidwkOg6/v2/kvLR8DJXTCtFTzwDtTtY2puEbM43KLUqVMnpk2bRvfu3dm5cyfPPPNMepFUs2ZN56QUERGHrdwXx9BPd2cokgBOJyQz9NPdrNwXZ19Rq7u9o3fl5pB6ARY/BesmmhNaxGQOtygBPPLII/z111+0aNGCsmXLsmHDBqpXr+6MQ4uIiBNYbQavL4/hZiMMG4AFeH15DO3DgnF3s0CpShC5HDa9A1vfh1o9TEgtYr5cFUqjRo266fqgoCDq16/PRx99lL5Oc72JiJhve+y5TC1JNzKAuIRktseeo1m1MvaV7h7Q5h/Q+EkoXub6zsd/goqNwU03Tkvhl6tCac+ePTddX61aNRITE9O3a643ERHXEH8h6yLptvv9vUia2xmqtoAe06FkiBNTirieXBVK6qQtIlKwBPn5OGe/C6fs4y7FbrBPrtv9I7irs3NCirggtZuKiBQBTUIDCPH3Iat2fsvVu9+ahAbc+kC1esDTGyG4Nlw+B58/At++CFey12IlUtBYDMO4Wd++W8qqj9LNFOY+SjmZfVhExGzX7nrjap+ka64VT1P7N7APEZAdaSnw/evwY5R9OagW9JoDQbrbWVxfTr6/ndpH6e/UR0lExHV0Cg9hav8GmcZRCr5xHKXs8vCGTm/Zx1xaMgTi98OxzSqUpNDJVYuS2KlFSUQKIqvNYHvsOeIvJBPkZ7/c5u7mwD9sL8bD7o+h5QugfyBLAZDnLUoiIlJwubtZrg8B4AwlgqDV6OvLyQnw30eg7SsQ2tJ55xExgcOF0oQJE265/dVXX3X0FHnmxIkTDBgwgPj4eDw8PBg3bhy9e/c2O5aISMG2YRIc3wrzI+ytTG3GgLun2alEcsXhS2/169fPsHzlyhViY2Px8PCgWrVq7N6929GMeSYuLo4//viDevXqER8fT4MGDTh48CDFixfP1ut16U1E5CZSLsJ3/4DoqxOjV2wMD82C0lXNTiYC+X3p7WYduxMTExk4cCA9erj2kPchISGEhNg7LwYFBREQEMC5c+eyXSiJiMhNeJeA7lFQ/T5YPhJ+3wHTWsKDU6B2L7PTieRInoyjVLJkSSZMmMC4ceMcOs7GjRuJiIigfPnyWCwWlixZkmmfjz76iNDQUHx8fGjYsCGbNm3K1bl27tyJzWajUqVKDmUWEZGrwh+CIZuhYhNISYRFg+CnGWanEsmRPBtw8vz58yQkJDh0jEuXLlG3bl0+/PDDm25fsGABI0eOZOzYsezZs4eWLVvSuXNnjh8/nr5Pw4YNCQ8Pz/Q4depU+j5nz57lscceY8aMW3+AU1JSSExMzPAQEZFbKF0FHv8OWr0IJSvYiyeRAsThPkrvv/9+hmXDMIiLi+OTTz6hVatWfP75545mhKtjMi1evJju3bunr2vatCkNGjRg6tSp6evuvvtuunfvzsSJE7N13JSUFNq3b8/gwYMZMGDALfcdP348r7/+eqb16qMkIpINKRftl+Wu+XUVVG+vyXUl3+VrH6UpU6ZkWHZzc6Ns2bJERkby8ssvO3r4LKWmprJr1y7GjBmTYX2HDh3YunVrto5hGAYDBw7kvvvuu22RBPDyyy9nGJU8MTFRl+pERLLrxiLp5wWw+Cn7gJXdp4FfOTOTiWTJ4UIpNjY2/fm1xqn8GJH7zJkzWK1WypXL+OEqV64cp0+fztYxtmzZwoIFC6hTp056/6dPPvmE2rVr33R/b29vvL29nZBeRKSIs6WBhy8cWQvT7oXuU+HO9manEsnEKe2ds2fPJjw8HB8fH3x8fAgPD2fWrFnOOPRt/b0oMwwj24VaixYtsNlsREdHpz+yKpJERMSJ6j8KT623zxF36U/4rBesfNk+h5yIC3G4UBo3bhzPPfccERERfPnll3z55ZdERETw/PPP83//93/OSXkTgYGBuLu7Z2o9io+Pz9TKJCIiLiioJgxeC02eti//+BHMuh/+/NXsZCLpHL70NnXqVGbOnEnfvn3T13Xt2pU6deowfPhw3njjDUdPcVNeXl40bNiQNWvWZBivac2aNXTr1i1PzikiIk7m6QNdJtn7Ki19Bk7vhcTfoWwNs5OJgDMKJavVSqNGjTKtb9iwIWlpaQ4d++LFixw+fDh9OTY2lujoaAICAqhcuTKjRo1iwIABNGrUiGbNmjFjxgyOHz/OkCFDHDrv7URFRREVFYXVas3T84iIFBl3dYIhW+Dw9/ai6RrD0ES7YiqHhwcYPnw4np6eTJ48OcP60aNHc/nyZaKionJ97PXr19O2bdtM6yMjI5k3bx5cHXBy0qRJxMXFER4ezpQpU2jVqlWuz5kTmsJERCQPnYuFLx6FBydD5XvMTiOFSE6+v3NVKN14i3xaWhrz5s2jcuXK3HOP/Q/5xx9/5MSJEzz22GN88MEHufkZCgQVSiIieeirJ2DfIrC4Qet/QMvR4O7whRCRvC+UbtbKc9ODWyysXbs2p4cvMFQoiYjkoZQLsGI0/PKFfblyM+g5E0pp/DpxTJ4XSmKnQklEJB/8shC+GQWpF8DHHyLeh1rds/FCkZvLyfe3xo0XERHXVudhGLIRKjSE5AT4MhL2LzY7lRQRKpRERMT1BdwBT6yCFqOgfH24q4vZiaSIUKGUC1FRUYSFhdG4cWOzo4iIFB3untDuNXhiNXhcnU7Kmga/fGkfRkAkD6iPkgPUR0lExGRr34SNk+DODtDtIyhR1uxEUgCoj5KIiBQNfsHg7g2HVtsn1z38g9mJpJBRoSQiIgVX40Hw1Dooezdc/AM+7Qmr/w/SUs1OJoWECiURESnYytWyF0uNBtmXt34As9vD2SNmJ5NCQIWSiIgUfJ6+9qlO+nwGvqXhzK9g03yc4jiNBS8iIoXH3Q/ahw/4Yx+UrXF9vTVN059IrqhFKRc0PICIiAvzrwA1Ol5fPrYVohrDiR1mppICSsMDOEDDA4iIFABzH4Bjm8HiDm1ftg9a6eZudioxkYYHEBERuabvfyG8FxhWWPsGfNwNEk6anUoKCBVKIiJSuPn4w0OzoPtU8CwORzfZx1w68I3ZyaQAUKEkIiKFn8UC9frB0xshpB5c/gsWPGrvvyRyC7oFQEREio7A6jBoDaydAOePQ+VmZicSF6dCSUREihYPL+jwBths9pYmgMvn4cByqN//+joRXXoTEZEiy+3qV6BhwDfPw7Jn4Yt+cOms2cnEhahQEhERqdQU3L3g4Lf2jt6/bTA7kbgIFUq5oAEnRUQKEYsF7hkCT/4AgTXgQpx9CIHvx4P1itnpxGQacNIBGnBSRKSQSb0EK1+G3fPtyxUaQu/5UKqS2cnEiTTgpIiISG54FYeu79uLIx9/SIyzr5MiS3e9iYiI/F2t7vbWpIvxUCzAvs4wIC0ZPH3NTif5SC1KIiIiN1OqElRseH3558/ho2ZwcpeZqSSfqVASERG5HZsVtrwPf8XC7A6weYp9HCYp9FQoiYiI3I6bOzyxEsK6gy3NfkfcJ93tfZikUFOhJCIikh2+paD3POj6AXgWg9gN9jGXDn5ndjLJQyqUREREsstigQaPwVMbILg2JJ2Fz/vCmcNmJ5M8orveREREcqpsDfsAld+/bp87LrC62Ykkj6hQyoWoqCiioqKwWq1mRxEREbN4eEOnt+zDBlxz7jf79CcNB2py3UJCI3M7QCNzi4hIOusVmNPRPnxAzQftfZmujcEkLkUjc4uIiOQ3izvU6gFunvC/b2BaCzi62exU4iAVSiIiIs7g5gbNh8OTayCgGiSehHkPwto3wZpmdjrJJRVKIiIizlS+Pjy9Eer1BwzYOAnmdoaLf5qdTHJBhZKIiIizeZeA7lHw0GzwLgnWVPsku1Lg6K43ERGRvFK7F1RsDIbVPowAVzt9p6XYiylxeWpREhERyUulq0DAHdeX178NM1rDqWgzU0k2qVASERHJL6mX4JeFcPYwzGoHWz/U5LouToWSiIhIfvEqDk9vgLsjwHYFVo+Fz3rBxXizk0kWVCiJiIjkp2IB8PAn8OAU8PCFIz/A1OZw6Huzk8lNqFASERHJbxYLNHoCnloPQbXg0p/wZSQknTM7mfyN7nrLBc31JiIiThFUEwavhTWvQnBtTXnigjTXmwM015uIiOSJo1vg3BGoP0CT6+aBnHx/q0VJRETElVw+D18Ptk+BcvgHiHgPfEuZnarIUh8lERERV+JdEpoMBjcPiFlin1z3+I9mpyqyVCiJiIi4Ejc3aPE8PLEaSleFhBP2ueLWv63JdU2gQklERMQVVWwIT2+COn3AsMH6iTD/QUi5YHayIkWFkoiIiKvyKQk9Z0CPGeBVAkoE2f8r+UaduUVERFxd3T5QqTH4lr5+F1xygr0fk1dxs9MVampREhERKQgC7rAXSgCGAcuGw4w2cHqv2ckKNRVKIiIiBc3FP+D4T3DmV5h5H/w41V48idOpUBIRESlo/IJh6Fao0RmsqbByDPy3D1w6Y3ayQkeFkoiISEFUvAz0/Rw6/xvcveHQKvvkukfWmp2sUFGhJCIiUlBZLND0Kft8cWVr2i/JLRsBaSlmJys0dNebiIhIQRccDk+th1VjIbwneHibnajQUKEkIiJSGHj6woOTM677eQEYVqjbV5Pr5pIKJRERkcLor6PwzfNw5ZJ9ct0HJ4OPv9mpChz1UcqFqKgowsLCaNy4sdlRREREbs6/kn3OOIs77PsKprWEEzvMTlXgWAxDAy/kVmJiIv7+/iQkJFCyZEmz44iIiGR2YjssGgTnj9uLprav2AsoN3ezk5kmJ9/falESEREpzCo1gSGbIbyXvb/S2n/CJ93BmmZ2sgJBhZKIiEhh5+MPD82C7lPBszhUbAzu6qacHfotiYiIFAUWC9TrB5WbgX/F6+sTT9nnkPP0NTOdy1KLkoiISFESEArunvbnaanweV+Y0Rb+iDE7mUtSoSQiIlJU/RVrb1H68wDMaAPbZ2py3b9RoSQiIlJUlb3LPrnunR3AmgLfjoYv+sGls2YncxkqlERERIqyEmWh30Lo9Da4e8HBb2HavRC70exkLkGFkoiISFFnscA9Q+HJH6DMnXAhDn6YoMtwKpREREQkXUgdeHoDNB4MPaZrfjgNDyAiIiIZeBWHB/6Tcd2Gf0PpKlDnYbNSmUaFkoiIiGTtxA5Y9yZg2CfXfeA/4O1ndqp8o0tvIiIikrXy9aH1P8DiBr98AdNbwcldZqfKNyqUREREJGvuHtD2ZRi4AkpWhHO/wewOsPldsNnMTpfnVCiJiIjI7VVpDkM3Q1g3sKXB96/BwgGF/s44FUoiIiKSPb6lofd8iHgfPHyh5gOF/s44deYWERGR7LNYoGEk3Nke/EKur//zIJSqAp4+ZqZzOrUoiYiISM6VLH+9NSnpHHzcHWbdby+YChEVSiIiIuKYc7FgTYU/9sH01rBzbqHpu6RCSURERBxTsaF9ct072kLaZfhmpL2jd9K5XB/SajPYduQsS6NPsu3IWaw2cwovi2EUkpLPBImJifj7+5OQkEDJkiXNjiMiImIumw22fWifJ852BUpWgJ4zoeq9OTrMyn1xvL48hriE5PR1If4+vBYRRqfwkFu+Njty8v2tFiURERFxDjc3uHcEPLkGAqpB4knYOSdHh1i5L46hn+7OUCQBnE5IZuinu1m5L87JoW9NhVIuREVFERYWRuPGjc2OIiIi4nrK14enN0Lz4fDAO9l+mdVm8PryGG52qevauteXx+TrZTgVSrkwbNgwYmJi2LFjh9lRREREXJN3CejwBviWsi8bBiwZBvsWZfmS7bHnMrUk3cgA4hKS2R6b+75POaVCSURERPJezFKI/hS+esJeMKVczLRL/IWsi6Tc7OcMKpREREQk79V8AFqOBiz2gmlGazgVnWGXIL/sDVaZ3f2cQYWSiIiI5D13T7h/HEQuB7/ycPYwzGoHWz9Mn1y3SWgAIf4+ZDUpiuXq3W9NQgPyLbYKJREREck/oS1h6Bao+aB9CIHVY2H5CADc3Sy8FhEGV4uiG11bfi0iDHe3/JtfToWSiIiI5K9iAdDnU3hgMngWh3r90jd1Cg9hav8GBPtnvLwW7O/D1P4NnDKOUk5owEkHaMBJERERByWdsxdO15zYASF1sLp5sT32HPEXkgnys19uc1ZLUk6+vz2cckYRERGR3LixSPrzV/i4K5SpjnuvOTSrdqeZyUCX3kRERMRlXIgDDx84/QtMbwW7PzZ9cl0VSiIiIuIa7mht7+gd2gquJMH2GWBLMzWSLr2JiIiI6yhZHgYsga0f2Mdecvc0NY4KJREREXEtbu7QYqTZKUCX3kRERESypkJJREREJAsqlERERESyoEJJREREJAsqlERERESyoEJJREREJAsqlERERESyoEJJREREJAsqlERERESyoEJJREREJAsqlERERESyoEJJREREJAsqlERERESyoEJJREREJAseZgcoyAzDACAxMdHsKCIiIpJN1763r32P34oKJQdcuHABgEqVKpkdRURERHLowoUL+Pv733Ifi5GdckpuymazcerUKfz8/LBYLCQmJlKpUiVOnDhByZIlzY5X5On9cD16T1yL3g/Xo/ckfxiGwYULFyhfvjxubrfuhaQWJQe4ublRsWLFTOtLliypP3AXovfD9eg9cS16P1yP3pO8d7uWpGvUmVtEREQkCyqURERERLKgQsmJvL29ee211/D29jY7iuj9cEl6T1yL3g/Xo/fE9agzt4iIiEgW1KIkIiIikgUVSiIiIiJZUKEkIiIikgUVSiIiIiJZUKHkJB999BGhoaH4+PjQsGFDNm3aZHakImv8+PFYLJYMj+DgYLNjFRkbN24kIiKC8uXLY7FYWLJkSYbthmEwfvx4ypcvj6+vL23atGH//v2m5S0KbveeDBw4MNNn5p577jEtb2E3ceJEGjdujJ+fH0FBQXTv3p2DBw9m2EefE9ehQskJFixYwMiRIxk7dix79uyhZcuWdO7cmePHj5sdrciqVasWcXFx6Y+9e/eaHanIuHTpEnXr1uXDDz+86fZJkyYxefJkPvzwQ3bs2EFwcDDt27dPnztRnO927wlAp06dMnxmvv3223zNWJRs2LCBYcOG8eOPP7JmzRrS0tLo0KEDly5dSt9HnxMXYojDmjRpYgwZMiTDupo1axpjxowxLVNR9tprrxl169Y1O4bYhx4xFi9enL5ss9mM4OBg4+23305fl5ycbPj7+xvTpk0zKWXR8vf3xDAMIzIy0ujWrZtpmYq6+Ph4AzA2bNhgGPqcuBy1KDkoNTWVXbt20aFDhwzrO3TowNatW03LVdQdOnSI8uXLExoayiOPPMJvv/1mdiQBYmNjOX36dIbPi7e3N61bt9bnxWTr168nKCiIGjVqMHjwYOLj482OVGQkJCQAEBAQAPqcuBwVSg46c+YMVquVcuXKZVhfrlw5Tp8+bVquoqxp06Z8/PHHrFq1ipkzZ3L69GmaN2/O2bNnzY5W5F37TOjz4lo6d+7MZ599xtq1a3nnnXfYsWMH9913HykpKWZHK/QMw2DUqFG0aNGC8PBw0OfE5XiYHaCwsFgsGZYNw8i0TvJH586d05/Xrl2bZs2aUa1aNebPn8+oUaNMzSZ2+ry4lj59+qQ/Dw8Pp1GjRlSpUoUVK1bQs2dPU7MVds8++yy//PILmzdvzrRNnxPXoBYlBwUGBuLu7p6pyo+Pj8/0rwExR/HixalduzaHDh0yO0qRd+3uQ31eXFtISAhVqlTRZyaPDR8+nGXLlrFu3ToqVqyYvl6fE9eiQslBXl5eNGzYkDVr1mRYv2bNGpo3b25aLrkuJSWFAwcOEBISYnaUIi80NJTg4OAMn5fU1FQ2bNigz4sLOXv2LCdOnNBnJo8YhsGzzz7L119/zdq1awkNDc2wXZ8T16JLb04watQoBgwYQKNGjWjWrBkzZszg+PHjDBkyxOxoRdLo0aOJiIigcuXKxMfH88Ybb5CYmEhkZKTZ0YqEixcvcvjw4fTl2NhYoqOjCQgIoHLlyowcOZK33nqLO++8kzvvvJO33nqLYsWK0a9fP1NzF2a3ek8CAgIYP348Dz30ECEhIRw9epRXXnmFwMBAevToYWruwmrYsGH897//ZenSpfj5+aW3HPn7++Pr64vFYtHnxJWYfdtdYREVFWVUqVLF8PLyMho0aJB+m6fkvz59+hghISGGp6enUb58eaNnz57G/v37zY5VZKxbt84AMj0iIyMN4+qtz6+99poRHBxseHt7G61atTL27t1rduxC7VbvSVJSktGhQwejbNmyhqenp1G5cmUjMjLSOH78uNmxC62bvReAMXfu3PR99DlxHRbD/qaJiIiIyN+oj5KIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKISD46ceIEbdq0ISwsjDp16vDll1+aHUlEbkFzvYmI5KO4uDj++OMP6tWrR3x8PA0aNODgwYMUL17c7GgichNqURKRAuns2bMEBQVx9OhRh47zwgsvEBER4bRcN+rVqxeTJ0/OsC4kJIR69eoBEBQUREBAAOfOncuT84uI41QoiUiBNHHiRCIiIqhatWqOXztw4EDGjBkDQHR0dHrhkhMrV67EYrHc8tGyZUvefPNNEhMTb3qMnTt3YrPZqFSpUo7PLyL5Q4WSiBQ4ly9fZvbs2Tz55JM5fq3NZmPFihV069YNgJ9//pn69evn+DitW7cmLi4u/VGmTBleeeWVDOuGDRtG1apV+eyzzzK9/uzZszz22GPMmDEjx+cWkfyjQklECpzvvvsODw8PmjVrlmH9jz/+yP33309gYGCm1p3z588DsGXLFtzc3GjatCknTpzg7Nmz6S1K58+fJyIigubNmxMXF3fLDL6+vgQHBxMcHIzVauXs2bO0aNEifV1wcDAeHh507dqVzz//PMNrU1JS6NGjBy+//DLNmzd3+u9HRJxHhZKIFDgbN26kUaNGGdb9/PPPtGnThrp167Jx40ZWrlxJQEAAbdu2ZcGCBZQqVQqAZcuWERERgZubG9HR0fj7+xMaGsrevXtp3LgxISEhrF+/npCQkGzn2bNnDwANGzbMtK1JkyZs376dlJQUAAzDYODAgdx3330MGDDAwd+EiOQ1FUoiUuAcPXqU8uXLZ1g3YsQIunXrxuTJkwkLC6Njx4707duXCxcu8PDDD6fvt2zZsvTLbtHR0dStW5fPP/+cVq1aMXr0aGbMmIGXl1eO8uzevZsKFSoQFBSUaVuFChVISUnh9OnTcLVFa8GCBSxZsoR69epRr1499u7dm8vfhIjkNQ+zA4iI5NTly5fx8fFJX/7jjz/YvHkza9euzbBf8eLFsVgs6csHDhzg999/p127dnC1UNq7dy/PPvssK1asyHQZ7J133mHKlCkEBgaSkJBA165dee+99zLl2b17Nw0aNLhpVl9fXwCSkpIAaNGiBTabzaGfX0Tyj1qURKTACQwM5K+//kpf3rVrFzabjbp162bYb9euXRku0S1btoz27dunFy/R0dE89NBDJCcnp/dhutG+ffuIiooiOjqaffv2MWPGDC5fvpxpv927d9/0shuQfut/2bJlHfiJRcQsKpREpMCpX78+MTEx6cvXWmhuLGL27t3Lxo0b6d+/f/q6pUuX0rVrVwAuXLhAbGwszzzzDB999BF9+/Zl//79Gc6zb9++9I7ee/bsoWbNmulF1jVnz57lxIkTWbYo7du3j4oVKxIYGOiUn11E8pcuvYlIgdOxY0defvll/vrrL0qXLk3Tpk3x9fXlpZdeYuzYsRw5coThw4czZMiQ9Mtp8fHx7NixgyVLlsDV1iR3d3fCwsKoX78++/fvJyIigu3btxMYGIhhGPz6669069aNpKQkEhIS2LJlS6Ysu3btAsiyUNq0aRMdOnTI09+HiOQdtSiJSIFTu3ZtGjVqxMKFC+HqZa2FCxeyfft26tSpw4gRIxgyZAjvvvtu+muWL19O06ZN0ztc//zzz9SsWRNvb28A/vWvfxEWFkbPnj1JTU3lt99+4+677yY6Oppff/2VZ5555qb9k/bs2UNQUBAVKlTItC05OZnFixczePDgPPxtiEhe0lxvIlIgffvtt4wePZp9+/bh5nb7f/N17dqVFi1a8NJLL2Xr+EuXLuXrr79m/vz5ACxatIgFCxakF2fZERUVxdKlS1m9enW2XyMirkWX3kSkQOrSpQuHDh3i5MmT2ZoCpEWLFvTt2zfbx9+3bx933XUXAGlpaXzxxRfpd8tll6enJx988EGOXiMirkUtSiIiN9GvXz82b95MmTJlsFgsPPDAA0yYMCHDcAMiUvipUBIRERHJgjpzi4iIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGRBhZKIiIhIFlQoiYiIiGTh/wECCyzRPGCUDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas = np.array([0.025, 0.05, 0.075, 0.1, 0.125])\n",
    "kT = 0.025852  # eV at 300 K\n",
    "mu_sigma = []\n",
    "for s in sigmas:\n",
    "    run(f'xtp_tools -e latticegen -c job_name=disorder_{s} dims=\"10 10 10\" energy.sigma={s}')\n",
    "    run(f'xtp_run -e kmcmultiple -c carriertype=hole field=\"1e6 0 0\" runtime=1000 outputtime=0 resultfile=disorder.dat ratefile=x occfile=x -f disorder_{s}.hdf5')\n",
    "    mu_sigma.append(results(\"disorder.dat\")[\"exact_mobility\"])\n",
    "x = (sigmas / kT) ** 2\n",
    "slope, offset = np.polyfit(x, np.log(mu_sigma), 1)\n",
    "plt.semilogy(x, mu_sigma, \"o\", label=\"exact, one carrier\")\n",
    "plt.semilogy(x, np.exp(offset + slope * x), \"--\", label=f\"fit: C = {np.sqrt(-slope):.2f}\")\n",
    "plt.xlabel(r\"$(\\sigma/k_BT)^2$\")\n",
    "plt.ylabel(\"bulk mobility (cm$^2$/Vs)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1fa321e",
   "metadata": {},
   "source": [
    "## Summary and next steps\n",
    "\n",
    "* `grw` gives exact ToF times and mobilities for one or two carriers; `kmctof` simulates the same experiment and adds transit-time distributions, transient currents, many carriers and pulses.\n",
    "* `kmcmultiple` gives bulk mobilities and diffusion tensors at a given carrier density, with the exact single-carrier values as reference.\n",
    "* ToF mobilities of thin samples exceed the bulk mobility when transport is dispersive.\n",
    "* `kmclifetime` gives exciton lifetimes, diffusion lengths and where excitons decay, again with exact single-exciton values.\n",
    "\n",
    "On a real morphology, the state file comes from the mapping and rate workflow (site energies, couplings, reorganization energies) shown in the other tutorials; the transport calculators are used exactly as here. The *Transport Simulations* chapter of the manual lists the options and a checklist."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.8"
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}