{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfbf4b0f",
   "metadata": {},
   "source": [
    "# Larger systems with the stochastic network model\n",
    "\n",
    "A morphology from molecular dynamics holds a few thousand molecules. For charge transport in a disordered material this is often far too small: a carrier in equilibrium sits in the low-energy tail of the density of states (DOS), and a small box samples that tail badly. The mobility of a small box then depends on its few deepest sites and changes by orders of magnitude from one box to the next.\n",
    "\n",
    "The stochastic network model (Baumeier, Stenzel, Poelking, Andrienko, Schmidt, Phys. Rev. B 86, 184202 (2012)) is a random hopping network with the statistics of the morphology that can be generated at any size. This tutorial\n",
    "\n",
    "1. fits the model to two morphologies of 1000 molecules with `networkfit`: MADN (moderate disorder) and bathocuproine, BCP (strong disorder and a trap),\n",
    "2. checks the fit against the morphology, including the transport,\n",
    "3. generates larger networks with `networkgen` and follows the mobility with system size,\n",
    "4. looks at the trap in BCP and how much the result depends on the trap density,\n",
    "5. shows why averaging over many small boxes does not replace a large one.\n",
    "\n",
    "The theory is in the XTP manual, section *Finite systems and the stochastic network model*; the options in *Transport Simulations*.\n",
    "\n",
    "## Requirements\n",
    "* VOTCA installed and the environment activated with `VOTCARC.bash`.\n",
    "* Python with `numpy` and `matplotlib`.\n",
    "\n",
    "The calculations take about 15 minutes in total. In Jupyter, `!` runs a shell command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a190532c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T20:59:46.006456Z",
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     "shell.execute_reply": "2026-11-10T20:59:46.328536Z"
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   "outputs": [],
   "source": [
    "import glob\n",
    "import gzip\n",
    "import os\n",
    "import shutil\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "kT = 8.617333e-5 * 300  # eV\n",
    "\n",
    "\n",
    "def run(command):\n",
    "    # run a VOTCA command, return its output (raises if it fails)\n",
    "    p = subprocess.run(command, shell=True, check=True, capture_output=True, text=True)\n",
    "    return p.stdout\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\n",
    "\n",
    "\n",
    "def show_log(log, first=\"Read \"):\n",
    "    # print the log of a tool from the line starting with `first`, without the banner\n",
    "    lines = log.splitlines()\n",
    "    start = next(i for i, l in enumerate(lines) if first in l)\n",
    "    print(\"\\n\".join(l.replace(\"... ... \", \"\") for l in lines[start:] if l.strip() and l.strip() != \"....\"))\n",
    "\n",
    "\n",
    "def mobility(statefile, carrier=\"hole\", field=1e7):\n",
    "    # exact mobility (cm^2/Vs) of one carrier in a periodic box (kmcmultiple, master equation)\n",
    "    tag = os.path.splitext(statefile)[0] + \"_\" + carrier\n",
    "    run(f'xtp_run -e kmcmultiple -c carriertype={carrier} \"field={field} 0 0\" runtime=1000 outputtime=0 '\n",
    "        f'ratefile={tag}_rates.dat occfile={tag}_occ.dat resultfile={tag}.res -f {statefile}')\n",
    "    for f in (f\"{tag}_rates.dat\", f\"{tag}_occ.dat\"):\n",
    "        os.remove(f)\n",
    "    if os.path.basename(statefile).startswith(\"model_\"):\n",
    "        os.remove(statefile)  # model networks are large; keep the disk clean\n",
    "    return results(f\"{tag}.res\")[\"exact_mobility\"]\n",
    "\n",
    "\n",
    "def generate(parameters, name, sites=None, seed=1, trapfactor=1.0, energyfile=False):\n",
    "    # write a model network with networkgen; returns the state file name\n",
    "    if os.path.exists(name + \".hdf5\"):\n",
    "        os.remove(name + \".hdf5\")\n",
    "    options = f\"job_name={name} parameters={parameters} seed={seed} trapfactor={trapfactor}\"\n",
    "    if sites is not None:\n",
    "        options += f\" sites={sites}\"\n",
    "    if energyfile:\n",
    "        options += f\" energyfile={name}_E.txt\"\n",
    "    run(f\"xtp_tools -e networkgen -c {options}\")\n",
    "    return name + \".hdf5\"\n",
    "\n",
    "\n",
    "def stats_blocks(filename):\n",
    "    # the blocks of a networkfit statsfile: {title: array}\n",
    "    blocks = {}\n",
    "    for block in open(filename).read().split(\"\\n\\n\\n\"):\n",
    "        lines = [l for l in block.strip().splitlines()]\n",
    "        if len(lines) < 3:\n",
    "            continue\n",
    "        title = lines[0][2:]\n",
    "        blocks[title] = np.array([[float(x) for x in l.split()] for l in lines[2:]])\n",
    "    return blocks\n",
    "\n",
    "\n",
    "# the morphologies are shipped compressed\n",
    "for name in (\"MADN\", \"BCP\"):\n",
    "    with gzip.open(f\"{name}.hdf5.gz\", \"rb\") as fin, open(f\"{name}.hdf5\", \"wb\") as fout:\n",
    "        shutil.copyfileobj(fin, fout)\n",
    "# networkgen does not overwrite state files: remove those of a previous run\n",
    "for f in glob.glob(\"model_*.hdf5\"):\n",
    "    os.remove(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef1de4e",
   "metadata": {},
   "source": [
    "## The morphologies\n",
    "\n",
    "Both state files contain 1000 molecules in a cubic box (MADN 8.8 nm, BCP 8.1 nm) with everything the transport calculators need: centers of mass, site energies (electrostatic and polarization contributions from polarizable force fields, plus internal energies), electronic couplings $|J|^2$ for holes and electrons in a neighbor list, and reorganization energies. For MADN only the holes have site energies. To keep the files small, each molecule is represented by one pseudo atom at its center of mass.\n",
    "\n",
    "## Fitting the model to MADN\n",
    "\n",
    "`networkfit` determines all parameters of the model:\n",
    "\n",
    "* the **point process** of the sites, from the nearest-neighbor distance distribution $D(r)$, the spherical contact distribution $H(r)$ and the pair correlation function $g(r)$,\n",
    "* the **site energies**: mean, width $\\sigma$ and the spatial correlation $\\kappa(r)$, described by a moving average over the $l_b$ and $l_c$ nearest sites,\n",
    "* the **connection probability** $f(r)$ of two sites at distance $r$,\n",
    "* the **couplings**: mean and variance of $\\log_{10}|J|^2$ against $r$ and the distribution of the residuals,\n",
    "* **traps** from the slow modes of the master equation,\n",
    "* for two carriers, the correlation of their site energies and couplings.\n",
    "\n",
    "It then generates `validate` (4) model boxes of the same size and compares their statistics with the morphology. The fit of the point process takes about two minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52b310f1",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-11-10T21:01:47.085667Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read MADN.hdf5 frame 0: 1000 sites, 8764 pairs, box 8.80658 x 8.80658 x 8.80658 nm^3\n",
      "Traps for h (slow modes of the master equation at 300 K): largest eigenvalue ratio 2.28 after 1 slow mode(s): no traps\n",
      "Traps for e (slow modes of the master equation at 300 K): largest eigenvalue ratio 1.15 after 1 slow mode(s): no traps\n",
      "Point process: r_h = 0.374102 nm, radius mean = 0.150445 nm, variance = 0.00803475 nm^2 (objective 0.00165453, 90 evaluations)\n",
      "Connection probability tabulated up to 2.25 nm\n",
      "Carrier h: site energies mean 5.54854 eV, sigma 0.0866746 eV (sample), 0.0945774 eV = 3.65842 kT (model, corrected for the finite system)\n",
      "    moving average w_a = 0.283967, w_b = 0.219236 (l_b = 4), w_c = 0.496797 (l_c = 320), rms of kappa 0.0208622, mean correlation over all pairs 0.160136\n",
      "    log10(J^2/eV^2) = -0.305017 + -5.10259 r/nm, variance 0.449331 + 1.51769 r/nm; residual skewness -0.813854, excess kurtosis 1.3627; 8764 couplings\n",
      "    lambda = 0.307031 eV (U_nX_nN 0.120751, U_xN_xX 0.18628), internal energy U_xX_nN 6.40378 eV\n",
      "    the density of occupied states at 300 K needs about 7.82e+07 sites\n",
      "Carrier e: site energies mean -0.973816 eV, sigma 0 eV (sample), 0 eV = 0 kT (model, corrected for the finite system); no energy disorder (all site energies equal)\n",
      "    log10(J^2/eV^2) = -0.572674 + -4.54076 r/nm, variance 0.593436 + 1.43016 r/nm; residual skewness -0.613832, excess kurtosis 1.36039; 8764 couplings\n",
      "    lambda = 0.377785 eV (U_nX_nN 0.157541, U_xN_xX 0.220244), internal energy U_xX_nN -0.973816 eV\n",
      "    the density of occupied states at 300 K needs about 2.28e+02 sites\n",
      "Correlation of the site energies of h and e: 0, of the coupling residuals: 0.499939\n",
      "Wrote MADN_network.xml\n",
      "Wrote MADN_E.txt\n",
      "Validation: 4 model network(s) of 1000 sites (seeds 1 to 4), mean and standard deviation over the networks; 0 trap(s) inserted in total:\n",
      "    quantity                              microscopic   model         sd\n",
      "    pairs per site                        17.53         17.91         0.1576\n",
      "    mean nearest neighbour distance (nm)  0.6833        0.6923        0.002636\n",
      "    sigma h (eV)                          0.08667       0.08438       0.001866\n",
      "    mean log10(J^2/eV^2) h                -6.373        -6.301        0.02827\n",
      "    sd log10(J^2/eV^2) h                  2.128         2.139         0.005257\n",
      "    kappa h at 0.75 nm                    0.3844        0.4496        0.08872\n",
      "    kappa h at 1.00 nm                    0.319         0.2891        0.1051\n",
      "    kappa h at 1.50 nm                    0.2155        0.2191        0.08526\n",
      "    kappa h at 2.00 nm                    0.2145        0.1441        0.05248\n",
      "    sigma e (eV)                          9.252e-15     0             0\n",
      "    mean log10(J^2/eV^2) e                -5.973        -5.919        0.01626\n",
      "    sd log10(J^2/eV^2) e                  2.024         2.027         0.006802\n",
      "    log10 D zero field h (m^2/s)          -8.794        -8.719        0.1721\n",
      "    traps found h                         0             0             0\n",
      "    log10 D zero field e (m^2/s)          -7.022        -7.063        0.01399\n",
      "    traps found e                         0             0             0\n",
      "Wrote MADN_stats.dat\n",
      "==============================================================================\n",
      "Please cite the methods used in this run:\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\n",
      "      doi:10.21105/joss.06864\n",
      "  [2] Baumeier, Stenzel, Poelking, Andrienko, and Schmidt, Stochastic modeling\n",
      "      of molecular charge transport networks, Phys. Rev. B 86, 184202 (2012),\n",
      "      doi:10.1103/PhysRevB.86.184202\n",
      "  [3] Kordt, Stenzel, Baumeier, Schmidt, and Andrienko, Parametrization of\n",
      "      extended Gaussian disorder models from microscopic charge transport\n",
      "      simulations, J. Chem. Theory Comput. 10, 2508 (2014),\n",
      "      doi:10.1021/ct500269r\n",
      "  [4] Chen, van der Hoorn, and Baumeier, Automatic identification of traps in\n",
      "      molecular charge transport networks of organic semiconductors, Phys.\n",
      "      Rev. B 111, 224209 (2025), doi:10.1103/shyl-kcfp\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "log = run(\"xtp_tools -e networkfit -c job_name=MADN statefile=MADN.hdf5 'carriers=h e' \"\n",
    "          \"statsfile=MADN_stats.dat energyfile=MADN_E.txt\")\n",
    "show_log(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c53611",
   "metadata": {},
   "source": [
    "The model has no traps for MADN. The width of the hole DOS is about $3.7\\,k_BT$ (after correcting the sample value for the finite box, see the manual). The validation table compares the morphology with the model boxes: geometry, energies, couplings, the energy correlation at a few distances and the exact zero-field diffusion coefficient of one hole. The morphology lies within the spread of the model boxes.\n",
    "\n",
    "The parameter file is plain XML and can be edited:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b540e76",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-11-10T21:01:47.091174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\"?>\n",
      "<!-- stochastic network model; lengths nm, energies eV, couplings log10(J^2/eV^2) with r in nm -->\n",
      "<stochasticnetwork>\n",
      "  <sites>1000</sites>\n",
      "  <box>8.806579366 8.806579366 8.806579366</box>\n",
      "  <seed>1</seed>\n",
      "  <segmentname>site</segmentname>\n",
      "  <points>\n",
      "    <hardcore>0.374102362</hardcore>\n",
      "    <radiusmean>0.1504448321</radiusmean>\n",
      "    <radiusvariance>0.008034751324</radiusvariance>\n",
      "    <candidates>3</candidates>\n",
      "    <maxiterations>200</maxiterations>\n",
      "  </points>\n",
      "  <connection>\n",
      "    <r>0.0249999982 0.0749999946 0.124999991 0.1749999874 0.2249999838 0.2749999802 0.3249999766 0.374999973 0.4249999694 0.4749999658 0.5249999622 0.5749999586 0.624999955 0.6749999514 0.7249999478 0.7749999442 0.8249999406 0.874999937 0.9249999334 0.9749999298 1.024999926 1.074999923 1.124999919 1.174999915 1.224999912 1.274999908 1.324999905 1.374999901 1.424999897 1.474999894 1.52499989 1.574999887 1.624999883 1.674999879 1.724999876 1.774999872 1.824999869 1.874999865 1.924999861 1.974999858 2.024999854 2.074999851 2.124999847 2.174999844 2.22499984 2.249999838</r>\n",
      "    <p>1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.9976247031 0.9888888889 0.9727095517 0.9350877193 0.8989726027 0.8215339233 0.7546099291 0.7124010554 0.6395209581 0.5413870246 0.4651898734 0.3446852425 0.284729064 0.199285076 0.1817434211 0.1335428123 0.09370529328 0.0685483871 0.04347826087 0.03107165504 0.01865889213 0.007900677201 0.007107709131 0.004573170732 0.003360537686 0.0004424778761 0.0004184100418 0</p>\n",
      "  </connection>\n",
      "  <energycorrelation>0</energycorrelation>\n",
      "  <couplingcorrelation>0.4999393332</couplingcorrelation>\n",
      "  <carrier>\n",
      "    <state>h</state>\n",
      "    <energy>\n",
      "      <mean>-0.855232802</mean>\n",
      "      <sigma>0.0945774285</sigma>\n",
      "      <w_a>0.2839667738</w_a>\n",
      "      <w_b>0.2192357483</w_b>\n",
      "      <l_b>4</l_b>\n",
      "      <l_c>320</l_c>\n",
      "    </energy>\n",
      "    <coupling>\n",
      "      <meanoffset>-0.3050167335</meanoffset>\n",
      "      <meanslope>-5.102594431</meanslope>\n",
      "      <varoffset>0.4493308662</varoffset>\n",
      "      <varslope>1.517685839</varslope>\n",
      "      <quantiles>-3.420121743 -2.637935168 -2.303488165 -2.093817843 -1.914211405 -1.806731319 -1.656580187 -1.534664686 -1.42529787 -1.331089531 -1.26618667 -1.182166496 -1.115826326 -1.058626816 -1.003037862 -0.9461941003 -0.8997000934 -0.8607168974 -0.8215968043 -0.7731326343 -0.7227289764 -0.6842595441 -0.6484713735 -0.6093153565 -0.5716427732 -0.5416754553 -0.5102307911 -0.4767094971 -0.4436469088 -0.4096935959 -0.3821298367 -0.3508875744 -0.3230685588 -0.2978219491 -0.2712625341 -0.2434024378 -0.2140584673 -0.1854267889 -0.1545693677 -0.1303622793 -0.102229902 -0.07865509707 -0.05644764608 -0.03181859111 0.0003354005276 0.02456304849 0.04606325258 0.06846111185 0.09208423877 0.1125874317 0.1366063245 0.1595519738 0.1852524914 0.2052686025 0.2286434498 0.256272584 0.2792270308 0.2990963419 0.3217181629 0.3434401665 0.3660460541 0.3871567998 0.4119165091 0.4357022876 0.4545999226 0.4767134975 0.4993714918 0.5204487183 0.5416988603 0.5638084629 0.5864318519 0.6124003791 0.6350906322 0.6566116618 0.6794067688 0.7021269771 0.7201191155 0.7467774661 0.7726600669 0.8014904574 0.8289215241 0.8598009326 0.8851296918 0.9127315122 0.9468718739 0.9772040102 1.002602816 1.033009739 1.060909165 1.095112443 1.142644063 1.202570084 1.244904916 1.303180664 1.369837251 1.436303458 1.514333396 1.618332274 1.779828203 2.068772324</quantiles>\n",
      "    </coupling>\n",
      "    <U_xX_nN>6.403777367</U_xX_nN>\n",
      "    <U_nX_nN>0.1207508793</U_nX_nN>\n",
      "    <U_xN_xX>0.1862802309</U_xN_xX>\n",
      "  </carrier>\n",
      "  <carrier>\n",
      "    <state>e</state>\n",
      "    <energy>\n",
      "      <mean>-7.363859902e-15</mean>\n",
      "      <sigma>0</sigma>\n",
      "      <w_a>1</w_a>\n",
      "      <w_b>0</w_b>\n",
      "      <l_b>1</l_b>\n",
      "      <l_c>1</l_c>\n",
      "    </energy>\n",
      "    <coupling>\n",
      "      <meanoffset>-0.5726738342</meanoffset>\n",
      "      <meanslope>-4.540762141</meanslope>\n",
      "      <varoffset>0.5934362375</varoffset>\n",
      "      <varslope>1.430155713</varslope>\n",
      "      <quantiles>-3.270761592 -2.518371092 -2.218640505 -2.012158393 -1.884803247 -1.724743304 -1.603330842 -1.484989195 -1.395995007 -1.309659636 -1.241890878 -1.175319334 -1.117877439 -1.045838033 -0.9936357149 -0.9484612026 -0.9097259413 -0.8588974351 -0.8166627038 -0.7760851797 -0.7328152898 -0.6885948595 -0.6519572111 -0.6148038296 -0.5837971669 -0.5476658273 -0.5091654728 -0.4820212455 -0.4492284434 -0.4160126412 -0.395611642 -0.3653803654 -0.3361533167 -0.311835723 -0.2799754546 -0.2550081101 -0.2305442382 -0.2056499708 -0.1811839018 -0.1602006343 -0.136578794 -0.1125979804 -0.08405545778 -0.06036556363 -0.0354347584 -0.01378947147 0.01184038673 0.03356875624 0.05769673729 0.0827590655 0.1073150747 0.128667251 0.1523652939 0.1713834994 0.1928778575 0.2122303152 0.2378098208 0.2587653249 0.2785546362 0.2979440231 0.3211192496 0.3424446173 0.3618077872 0.3848763938 0.4065152926 0.4273790559 0.4494480404 0.4698678366 0.4926235964 0.5174184384 0.5448283913 0.5673311892 0.591030109 0.6123844277 0.6313182528 0.6558802199 0.6798739168 0.7085168407 0.7342885342 0.770011529 0.7984282912 0.8321629095 0.8618212317 0.8954778055 0.9334699635 0.960833925 0.9993652533 1.0427388 1.086548629 1.137539015 1.196823113 1.246579621 1.304762626 1.387684571 1.443025147 1.538317982 1.654929243 1.798270573 1.99355875 2.346311823</quantiles>\n",
      "    </coupling>\n",
      "    <U_xX_nN>-0.9738158922</U_xX_nN>\n",
      "    <U_nX_nN>0.1575413626</U_nX_nN>\n",
      "    <U_xN_xX>0.2202440377</U_xN_xX>\n",
      "  </carrier>\n",
      "</stochasticnetwork>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(open(\"MADN_network.xml\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e255a3f8",
   "metadata": {},
   "source": [
    "The fitted functions from the `statsfile`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7c3d86d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:01:47.093434Z",
     "iopub.status.busy": "2026-11-10T21:01:47.093260Z",
     "iopub.status.idle": "2026-11-10T21:01:47.584841Z",
     "shell.execute_reply": "2026-11-10T21:01:47.584290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = stats_blocks(\"MADN_stats.dat\")\n",
    "fig, ax = plt.subplots(2, 3, figsize=(14, 8))\n",
    "for a, key, ylabel in ((ax[0, 0], \"nearest neighbour distance distribution D(r)\", \"D(r)\"),\n",
    "                       (ax[0, 1], \"spherical contact distribution H(r)\", \"H(r)\"),\n",
    "                       (ax[0, 2], \"pair correlation function g(r)\", \"g(r)\")):\n",
    "    d = b[key]\n",
    "    a.plot(d[:, 0], d[:, 1], \"k-\", label=\"morphology\")\n",
    "    a.plot(d[:, 0], d[:, 2], \"r--\", label=\"model (mean of boxes)\")\n",
    "    a.set_xlabel(\"r (nm)\"); a.set_ylabel(ylabel); a.legend()\n",
    "d = b[\"pairs per site in distance bins\"]\n",
    "ax[1, 0].plot(d[:, 0], d[:, 1], \"k-\", label=\"morphology\")\n",
    "ax[1, 0].plot(d[:, 0], d[:, 2], \"r--\", label=\"model\")\n",
    "f = b[\"connection probability f(r)\"]\n",
    "ax[1, 0].plot(f[:, 0], f[:, 1], \"b:\", label=\"f(r)\")\n",
    "ax[1, 0].set_xlabel(\"r (nm)\"); ax[1, 0].set_ylabel(\"pairs per site / f(r)\"); ax[1, 0].legend()\n",
    "d = b[\"energy correlation kappa(r), carrier h\"]\n",
    "ax[1, 1].plot(d[:, 0], d[:, 1], \"k.\", ms=3, label=\"morphology\")\n",
    "ax[1, 1].plot(d[:, 0], d[:, 2], \"r-\", label=\"moving average\")\n",
    "ax[1, 1].set_xlabel(\"r (nm)\"); ax[1, 1].set_ylabel(r\"$\\kappa(r)$\"); ax[1, 1].set_ylim(-0.2, 1.0); ax[1, 1].legend()\n",
    "d = b[\"mean log10(J^2/eV^2), carrier h\"]\n",
    "ax[1, 2].plot(d[:, 0], d[:, 1], \"k-\", label=\"morphology\")\n",
    "ax[1, 2].plot(d[:, 0], d[:, 2], \"r--\", label=\"model\")\n",
    "ax[1, 2].set_xlabel(\"r (nm)\"); ax[1, 2].set_ylabel(r\"mean $\\log_{10}(|J|^2/\\mathrm{eV}^2)$\"); ax[1, 2].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4936057",
   "metadata": {},
   "source": [
    "The correlation of the site energies reaches far: the best long-range average uses $l_c\\approx 300$ sites, a third of the box. How well this range is determined shows the quality of the fit for every $l_c$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54b0f1d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:01:47.587835Z",
     "iopub.status.busy": "2026-11-10T21:01:47.587152Z",
     "iopub.status.idle": "2026-11-10T21:01:47.657815Z",
     "shell.execute_reply": "2026-11-10T21:01:47.657335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scan = b[\"rms of the best kappa fit for each l_c, carrier h\"]\n",
    "plt.plot(scan[:, 0], scan[:, 1], \"o-\")\n",
    "plt.xlabel(\"$l_c$\")\n",
    "plt.ylabel(r\"rms deviation of $\\kappa(r)$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2bcc07",
   "metadata": {},
   "source": [
    "The minimum is shallow: correlations over distances comparable to the box are not well determined by a box of 1000 molecules.\n",
    "\n",
    "## Transport: morphology, model boxes, larger networks\n",
    "\n",
    "We compare the exact mobility of one hole at $10^7$ V/m (from the master equation, computed by `kmcmultiple`; no sampling noise) in the morphology with model boxes of the same size, and then with larger networks. Each model box is a new realization (`seed`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "784090bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:01:47.659739Z",
     "iopub.status.busy": "2026-11-10T21:01:47.659505Z",
     "iopub.status.idle": "2026-11-10T21:02:46.848176Z",
     "shell.execute_reply": "2026-11-10T21:02:46.847535Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MADN morphology: 7.702e-04 cm^2/Vs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 sites: 3.72e-04 8.35e-04 1.02e-03 1.16e-03 7.45e-04 5.87e-04 7.51e-04 7.54e-04 1.97e-03 7.35e-05 6.59e-04 5.47e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000 sites: 3.80e-04 2.51e-04 4.25e-04\n"
     ]
    }
   ],
   "source": [
    "mu_madn = mobility(\"MADN.hdf5\")\n",
    "print(f\"MADN morphology: {mu_madn:.3e} cm^2/Vs\")\n",
    "\n",
    "madn = {}\n",
    "for n, seeds in ((1000, range(1, 13)), (8000, range(1, 4))):\n",
    "    madn[n] = [mobility(generate(\"MADN_network.xml\", f\"model_madn_{n}_{s}\", sites=n, seed=s)) for s in seeds]\n",
    "    print(n, \"sites:\", \" \".join(f\"{m:.2e}\" for m in madn[n]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8e41826",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:02:46.850508Z",
     "iopub.status.busy": "2026-11-10T21:02:46.849987Z",
     "iopub.status.idle": "2026-11-10T21:02:47.106700Z",
     "shell.execute_reply": "2026-11-10T21:02:47.106072Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vq1Zo0KBBqZbds2dP4P4pAACIi4tDr1690LNnT8TFxQH3O8Dm5OTo65Zn33nqqadMLl8Igf/85z+YNWsWfvjhB0ybNs1geqtWrWBjY4MXX3wR3377LS5duvTIdSp27do1uLu7S05/9tlnDZ4PHz4cVlZW2L17d4nz3bVrF3r27Am1Wg2FQgFra2vMnDkTt2/f1p9KiouLg1arNTitKSU/Px+DBw/GqlWrsH37dqN2AcDMmTOh0WjQtWvXEucVHx+vX5cHFXc1eHCZO3fuxJNPPgk7OzuD/adfv37Iz8/Xn6rftWsXAgMD0a5dO4N5jBkzBkIIow7B/fr1M9gvit+H/fv3N6hXXJ6UlARU8nupR48eqF+//iPrbd26Fd27d9e3pay6d+9u0P+teD59+/Y1uML14c+isnyGajQafPDBBwgMDISNjQ2srKxgY2ODCxcu4Ny5c0ZteuKJJwyeP/zZ/yB3d3ekpKSUa90rC4OShZPJZHj++efx/fffY8mSJWjRogU6d+4sWX/p0qWwtbVFnz59cPfuXdy9exchISHw8/PDihUroNVqS73s4p32wS/2h82fPx+3bt0q16X+Li4uBs+VSiUA6Ds13r59G7gfOh7m5eUFnU6HO3fumJx38bn7oUOHwtra2uAxf/58CCGQnp4O3O9vEB0djW+++QYRERFwdnbG6NGjcf369TKvUzFnZ2ejMhsbG6NyGxsb4P6XAgDcuXMHQgjJdcYD2+X27dto0KCBUb2Hy4q3Rdu2bY22xdq1a0t1CbopdnZ2sLW1NShTKpX6dSle9t27d2FjY2O07OvXr5dq2b6+vmjatCl27NiB3NxcHDx4UB+UioPjjh07oFKp0KFDB4NtVJZ9x1Rd3O9jtXbtWjz22GPo27ev0fTitrm7u2PixIlo2rQpmjZtikWLFj1y3fLy8oy24YMe/ltaWVnBxcVFv36mHD58GFFRUQCAr7/+Gvv378eRI0cwY8YM/TJxv28egFJ1HE5LS8O2bdsQERGh38bldfv2bVhZWRm9Fx4M/MX1NBoNPvvsM6N9p1+/fsD9YVCK65bmPVNM6n34qPdnZb6XpPa3h928ebNCnbsruq6l+QydOnUq3n33XQwePBi//vorDh06hCNHjiA0NNTkhUSP+ux/kK2treTFSNXFyqxLp1IZM2YMZs6ciSVLluD999+XrPfPP//gjz/+AADJK+K2bdum/5B5lE2bNgH3OydLadWqFUaMGIGYmJhSz7e0it9MqampRtOuXbsGuVwu+YvM1dUVAPDZZ5+hffv2JusUfzC7urpi4cKFWLhwIZKSkrBp0yZMnz4daWlpJjtaV6XiTspS64wH1s3FxcVkmHu4rLj+zz//DF9f3ypquWmurq5wcXGR3I4lDXHxoMjISPzyyy+Ij4+HTqdDt27d4OjoCC8vL8TFxWHHjh3o3Lmz/gO3PPuO1PhRSqUSu3fvRu/evdGzZ0/8/vvvRq/t3LkzOnfuDK1Wi6NHj+Kzzz7DlClT4OHhgWeeeUZyvVxdXfVfNqZcv34d3t7e+ucajQa3b982+qJ50Jo1a2BtbY3ffvvNIIRt3LjRoJ6bmxsAIDk5GQ0bNpScH+5/nsTExODJJ5/EkCFD8NNPP5UY8Eri4uICjUaD9PR0gy/rh/fb+vXr64+kSh31aty4sX6epXnPVFRlvpdKO16Zm5ub5DAtVaksn6Hff/89Ro8ejQ8++MBg+q1bt1CvXr0KtSM9PR1+fn4VmkdF8YhSDeDt7Y033ngDAwcORHR0tGS94itVvv76a+zevdvgsWXLFlhbW2PZsmWlWmZcXBy++eYbdOjQAZ06dSqx7ty5c1FYWFiuMUNK4u/vD29vb/zwww8GpwFzcnKwbt06/dVMpnTs2BH16tXD2bNn0aZNG5OP4l9QD2rUqBEmTZqEXr164fjx45W6PqVhb2+Pxx9/HOvXrzf4FaXT6fD999/Dx8cHLVq0AO4fUt+5c6fBlS9ardboSsTevXvDysoKFy9elNwWVWXAgAG4ffs2tFqtyeX6+/vr6yqVSslfjj179sSNGzewcOFCtG/fXh+wIiMjsWHDBhw5ckR/2g0V3HdMad26NeLj45GcnIxu3bqZvBIK9099Pv744/ornh61DwUEBJR4Rc+qVasMnv/444/QaDQl/niRyWSwsrIyOK2dl5eH7777zqBeVFQUFAoFFi9eXGIbH6y/bds27N271+CKv7IqPjX38H66Zs0ag+d2dnbo3r07Tpw4gZCQEJP7T3FgjIyMxNmzZ42298qVKyGTydC9e/dytfVhZXkvlXSUpCz69u2L3bt3m7wqrCqV5TNUJpPp17fY5s2bK3zKTKPR4OrVqwgMDKzQfCqKR5RqiEeNJaHRaLBy5Uq0bNlScrCygQMHYtOmTbh586b+16ROp9Of5y8oKEBSUhK2bt2KH3/8ES1btsSPP/74yLY1btwYEyZMKNWphrKQy+X46KOP8Oyzz2LAgAH4z3/+g4KCAixYsAB3794tcZs4ODjgs88+Q3R0NNLT0zF06FC4u7vj5s2bOHnyJG7evInFixcjIyMD3bt3x8iRIxEQEABHR0ccOXIEv//+O4YMGaKfX3x8PCIjIzFz5sxS91Mqr3nz5qFXr17o3r07Xn/9ddjY2OCLL77A6dOnsXr1av0v0XfeeQebNm1Cjx49MHPmTNjZ2SE2NtboC8zPzw9z5szBjBkzcOnSJfTp0wf169fHjRs3cPjwYdjb21d6yC32zDPPYNWqVejXrx9eeeUVtGvXDtbW1khOTsbu3bsxaNAgPPnkkwCA4OBgrFmzBmvXrkWTJk1ga2uL4OBg4H5/DplMhu3btxu0tWfPnvofDw8GpYrsO1JatmyJffv2oWfPnujSpQt27NgBHx8fLFmyBLt27UL//v3RqFEj5Ofn63+QPNgmU7p164Y5c+YgNzfXZHBbv349rKys0KtXL5w5cwbvvvsuQkNDjfr3PKh///6IiYnByJEj8eKLL+L27dv4+OOPjb7I/Pz88Pbbb+O///0v8vLyMGLECKjVapw9exa3bt0yuU906tQJO3fuRJ8+fRAVFYUtW7ZArVYDAObMmYM5c+Zg586dJfZT6tOnDzp27IjXXnsNmZmZCA8Px8GDB7Fy5Urg/t+u2KJFi9CpUyd07twZEyZMgJ+fH7KyspCYmIhff/1V3/fo1VdfxcqVK9G/f3/MmTMHvr6+2Lx5M7744gtMmDBB/+OiosryXired+fPn4++fftCoVAgJCTE5A+0ksyZMwdbt25Fly5d8PbbbyM4OBh3797F77//jqlTpyIgIKBS1u1hpf0Mxf0fRCtWrEBAQABCQkJw7NgxLFiwoMLjQf3111/Izc2ttKBbbmbtSk4mPXjVW0kevEqo+EqGhQsXStYvvtz/k08+EeKBK5eKHyqVSjRq1EgMHDhQLFu2TBQUFBjN4+FLhYvdvHlTODk5lemqN1P1TF2Cv3HjRvH4448LW1tbYW9vLyIjI8X+/fsN6pgaHkDcv2y1f//+wtnZWVhbWwtvb2/Rv39/8dNPPwkhhMjPzxcvvfSSCAkJEU5OTkKlUgl/f38xa9YskZOTo59PWYcHMLWNfH19Rf/+/U2u88SJEw3K9u3bJ3r06CHs7e2FSqUS7du3F7/++qvRa/fv368fYqBBgwbijTfeEF999ZXJbbFx40bRvXt34eTkJJRKpfD19RVDhw41uMy3LFe9mbpi0tTri4qKxMcffyxCQ0OFra2tcHBwEAEBAeI///mPuHDhgr7elStXRFRUlHB0dNRf1vyg1q1bCwAGf/uUlBQBwOBqrofX+VH7TnGbb968War1TE5OFgEBAcLPz09cvHhRHDx4UDz55JPC19dXKJVK4eLiIrp27So2bdr0yO2YmJgoZDKZ0dWDxW06duyYGDhwoHBwcBCOjo5ixIgR4saNGwZ1TV31tmzZMuHv7y+USqVo0qSJmDdvnli6dKnJ/WLlypWibdu2+r9N69atxfLlyw3m//D+fPr0adGgQQMRFham326lHR5ACCHS09PF888/L+rVqyfs7OxEr1699MM+LFq0yKDu5cuXxdixY4W3t7ewtrYWbm5uokOHDmLu3LkG9f79918xcuRI4eLiIqytrYW/v79YsGCBwdVtUp89xe/v4s+FYlKfxaV5LxUUFIhx48YJNzc3IZPJDLa9qfd8MVOfM1evXhVjx44VDRo0ENbW1sLLy0sMHz7caF8wNa+Hl1PWbfCoz1AhhLhz54544YUXhLu7u7CzsxOdOnUS+/btM9o3pZZR3KYH9ztxf/gKV1dX/bAK5iITHFqZiMhsiq8I3Lp1q7mbYlY//PADnn32Wezfv7/CHcap5tNqtWjWrBlGjhxZYt/c6sCgRERkRqdPn0br1q1x4MABtG3b1tzNqRarV69GSkoKgoODIZfL8eeff2LBggX6vmBE3377LV5//XVcuHChwh3CK4p9lIiIzCgoKAjLly+v0HAUNY2joyPWrFmDuXPnIicnB56enhgzZgzmzp1r7qaRhdDpdFi1apXZQxJ4RImIiIhIGocHICIiIpLAoEREREQkgUGJiIiISAI7c1eATqfDtWvX4OjoWOrh6ImIiMi8hBDIysqCl5eXwSCnpjAoVcC1a9ceeY8kIiIiskxXr1595AjiDEoVUHy/qatXr8LJycnczSEiIqJSyMzMRMOGDUt1Y24GpQooPt3m5OTEoERERFTDlKbbDDtzExEREUlgUCIiIiKSwFNvRFRr6XQ6FBYWmrsZRGQGNjY2j7yirTQYlIioViosLMTly5eh0+nM3RQiMgO5XI7GjRvDxsamQvNhUCKiWkcIgdTUVCgUCjRs2LBSflUSUc1RPM5hamoqGjVqVKGxDhmUiKjW0Wg0yM3NhZeXF+zs7MzdHCIyAzc3N1y7dg0ajQbW1tblng9/ZhFRraPVaoH7fRSIqG4qfv8Xfx6UF4MSEdVavLUQUd1VWe9/nnqzQDqdwJXbOcjK18DR1gp+LvaQy/mBT0REVN0YlCzM6ZQMrDuejMS0bBQU6aC0lqOZuwOeCvNBkLfa3M0jIiKqU3jqzYKcTsnApzsv4FRyBuqpbODnao96KhucSr5Xfjolw9xNJKI65MqVK5DJZEhISKjQfPz8/LBw4cJKa1dF7Nq1CwEBAXV+2IgxY8Zg8ODB1brMytwPfvvtN7Ru3bpa/o4MShZCpxNYdzwZ6TmFaObuAAdbKyjkMjjYWqGZuwPScwqx/ngKdDph7qYSEdVY06ZNw4wZM/RDRqSmpmLkyJHw9/eHXC7HlClTTL5u3bp1CAwMhFKpRGBgIDZs2GBU54svvkDjxo1ha2uL8PBw7Nu3z2C6EALvvfcevLy8oFKp0K1bN5w5c6aK1rR2GzBgAGQyGX744YcqXxaDUjnExsYiMDAQbdu2rbR5Xrmdg8S0bHiqVUYd0GQyGTzVKlxIy8KV2zmVtkwiqpvq6mjlBw4cwIULFzBs2DB9WUFBAdzc3DBjxgyEhoaafN3Bgwfx9NNPY9SoUTh58iRGjRqF4cOH49ChQ/o6a9euxZQpUzBjxgycOHECnTt3Rt++fZGUlKSv89FHHyEmJgaff/45jhw5ggYNGqBXr17Iysqq4jWvnZ5//nl89tlnVb4cBqVymDhxIs6ePYsjR45U2jyz8jUoKNJBZaMwOV1lo0BBkQ5Z+ZpKWyZRnSEEkJNjnoco/VHgbt26YfLkyZgyZQrq168PDw8PfPXVV8jJycHzzz8PR0dHNG3aFFu3bjV4XXx8PNq1awelUglPT09Mnz4dGo3GYL6TJk3C1KlT4erqil69egH3f4QtXrwYffv2hUqlQuPGjfHTTz8ZtevSpUvo3r077OzsEBoaioMHDxpMX7duHR577DEolUr4+fnhk08+KXE9k5KSMGjQIDg4OMDJyQnDhw/HjRs3DOrMnTsX7u7ucHR0xLhx4zB9+nS0atUKALB3715YW1vj+vXrBq957bXX0KVLF8nlrlmzBlFRUbC1tdWX+fn5YdGiRRg9ejTUatP9QBcuXIhevXrhrbfeQkBAAN566y1ERkYanEaKiYnBCy+8gHHjxqFly5ZYuHAhGjZsiMWLFwP3jyYtXLgQM2bMwJAhQxAUFIRvv/0Wubm5JR4VKT5F9sEHH8DDwwP16tXD7NmzodFo8MYbb8DZ2Rk+Pj5YtmyZwetOnTqFHj16QKVSwcXFBS+++CKys7MllyOEwEcffYQmTZpApVIhNDQUP//8s0GdM2fOoH///nBycoKjoyM6d+6MixcvAvf3sYePxg0ePBhjxoyRXGZGRgZefPFFuLu7w8nJCT169MDJkyf100+ePInu3bvD0dERTk5OCA8Px9GjR/XTn3jiCRw+fBiXLl2SXEZlYFCyEI62VlBay5FXaHq8h7xCLZTWcjjasv89UZnl5gIODuZ55OaWqanffvstXF1dcfjwYUyePBkTJkzAsGHD0KFDBxw/fhy9e/fGqFGjkHt/vikpKejXrx/atm2LkydPYvHixVi6dCnmzp1rNF8rKyvs378fX375pb783XffxVNPPYWTJ0/iueeew4gRI3Du3DmD186YMQOvv/46EhIS0KJFC4wYMUIfxI4dO4bhw4fjmWeewalTp/Dee+/h3XffxYoVK0yunxACgwcPRnp6OuLj4xEXF4eLFy/i6aef1tdZtWoV3n//fcyfPx/Hjh1Do0aN9IEDALp06YImTZrgu+++05dpNBp8//33eP755yW37d69e9GmTZsy/DXuOXjwIKKiogzKevfujQMHDgD3j9AdO3bMqE5UVJS+zuXLl3H9+nWDOkqlEl27dtXXkbJr1y5cu3YNe/fuRUxMDN577z0MGDAA9evXx6FDh/DSSy/hpZdewtWrVwEAubm56NOnD+rXr48jR47gp59+wo4dOzBp0iTJZbzzzjtYvnw5Fi9ejDNnzuDVV1/Fc889h/j4eOD+ftalSxfY2tpi165dOHbsGMaOHWsQyMtCCIH+/fvj+vXr2LJlC44dO4awsDBERkYiPT0dAPDss8/Cx8cHR44cwbFjxzB9+nSDgSN9fX3h7u5udIqz0gkqt4yMDAFAZGRkVHheWq1OvLfptHjqi/3izZ9Piunr/tI/3vz5pHjqi/1i9qYzQqvVVUrbiWqzvLw8cfbsWZGXl3evIDtbiHvHdqr/kZ1d6nZ37dpVdOrUSf9co9EIe3t7MWrUKH1ZamqqACAOHjwohBDi7bffFv7+/kKn+/+fDbGxscLBwUFotVr9fFu1amW0PADipZdeMih7/PHHxYQJE4QQQly+fFkAEN98841++pkzZwQAce7cOSGEECNHjhS9evUymMcbb7whAgMD9c99fX3F//73PyGEENu3bxcKhUIkJSUZzfPw4cP6NkycONFgnh07dhShoaH65/PnzxctW7bUP9+4caNwcHAQ2SVsb7VaLVauXCk5vWvXruKVV14xKre2tharVq0yKFu1apWwsbERQgiRkpIiAIj9+/cb1Hn//fdFixYthBBC7N+/XwAQKSkpBnXGjx8voqKiJNsUHR0tfH199X9LIYTw9/cXnTt31j8v3k9Wr14thBDiq6++EvXr1zfYFps3bxZyuVxcv35dP99BgwYJIYTIzs4Wtra24sCBAwbLfuGFF8SIESOEEEK89dZbonHjxqKwsLDU227QoEEiOjpa//zB/WDnzp3CyclJ5OfnG7ymadOm4ssvvxRCCOHo6ChWrFghuW2EEKJ169bivffeMznN6HPgAWX5/uYRJQshl8vwVJgPnO1tkJiWjex8DbQ6gex8DRLTsuFsb4MhYd4cT4moPOzsgOxs8zzKeAuVkJAQ/f8VCgVcXFwQHBysL/Pw8AAApKWlAQDOnTuHiIgIg76NHTt2RHZ2NpKTk/VlUkdSIiIijJ4/fETpwTZ5enoaLb9jx44G9Tt27IgLFy6YHBH53LlzaNiwIRo2bKgvCwwMRL169fTLPX/+PNq1a2fwuoefjxkzBomJifjzzz8BAMuWLcPw4cNhb29vcj0BIC8vz+C0W1k83HdUCGGyP2ll1HnYY489ZnC/Qg8PD4N9ong/efBvEhoaarAtOnbsCJ1Oh/PnzxvN/+zZs8jPz0evXr3g4OCgf6xcuVJ/ai0hIQGdO3eu0K1AHnTs2DFkZ2fDxcXFYJmXL1/WL3Pq1KkYN24cevbsiQ8//FBf/iCVSqU/ulpVeB7HggR5q/FyZHP9OEo3Mu+NoxTiUw9Dwrw5jhJReclkQAlfoJbk4S8imUxmUFb8pVp8WbSpL1pxv1/Ug+UlBYiHPTy/8i7fFKlg8HD5o+bp7u6OgQMHYvny5WjSpAm2bNmCPXv2lLherq6uuHPnTol1TGnQoIFRf6i0tDR9aHV1dYVCoSixToMGDQAA169f14fNh+tIedQ+UVxW0t/kwXoPK37d5s2b4e3tbTBNqVQC9wNJSeRyudHfqKioSLK+TqeDp6enyb9ZvXr1AADvvfceRo4cic2bN2Pr1q2YNWsW1qxZgyeffFJfNz09HW5ubiW2raJ4RMnCBHmr8W7/QMx+4jHM6N8Ss594DO/0b8mQREQmBQYG4sCBAwZfUgcOHICjo6PRl54pxUdkHnweEBBQpuX/8ccfBmUHDhxAixYtoFAYX5wSGBiIpKQkfX8a3D+ikZGRgZYtWwIA/P39cfjwYYPXPdiJt9i4ceOwZs0afPnll2jatKnRka2HtW7dGmfPni31uhWLiIhAXFycQdn27dvRoUMH4P49xcLDw43qxMXF6es0btwYDRo0MKhTWFiI+Ph4fZ3KEhgYiISEBOTk/P+rpPfv3w+5XI4WLVqYrK9UKpGUlIRmzZoZPIqP/IWEhGDfvn2S4cfNzQ2pqan651qtFqdPn5ZsY1hYGK5fvw4rKyujZbq6uurrtWjRAq+++iq2b9+OIUOGYPny5fpp+fn5uHjxIlq3bl2OrVR6DEoWSC6XoYmbA0Ib1kMTNweebiMiSf/3f/+Hq1evYvLkyfj777/xyy+/YNasWZg6darB6RopP/30E5YtW4Z//vkHs2bNwuHDh0vs9Puw1157DTt37sR///tf/PPPP/j222/x+eef4/XXXzdZv2fPnggJCcGzzz6L48eP4/Dhwxg9ejS6du2qPz04efJkLF26FN9++y0uXLiAuXPn4q+//jI6GtK7d2+o1WrMnTu3xE7cD9Z/ONTh/mmlhIQEZGdn4+bNm0hISDAIVK+88gq2b9+O+fPn4++//8b8+fOxY8cOg6u8pk6dim+++QbLli3DuXPn8OqrryIpKQkvvfQScP9IzpQpU/DBBx9gw4YNOH36NMaMGQM7OzuMHDmy1Nu7NJ599lnY2toiOjoap0+fxu7duzF58mSMGjXK5NErR0dHvP7663j11Vfx7bff4uLFizhx4gRiY2Px7bffAgAmTZqEzMxMPPPMMzh69CguXLiA7777Tn8qr0ePHti8eTM2b96Mv//+G//3f/+Hu3fvSraxZ8+eiIiIwODBg7Ft2zZcuXIFBw4cwDvvvIOjR48iLy8PkyZNwp49e/Dvv/9i//79OHLkiD5M436oVyqVRqePK90jezGRpMrszE1ElaekTpyWzFSH2Ac7wBYDIDZs2KB/vmfPHtG2bVthY2MjGjRoIN58801RVFRU4nyL5xMbGyt69eollEql8PX11XcIFg905j5x4oS+7M6dOwKA2L17t77s559/FoGBgcLa2lo0atRILFiwoMR1+Pfff8UTTzwh7O3thaOjoxg2bJi+k3GxOXPmCFdXV+Hg4CDGjh0rXn75ZdG+fXujdXj33XeFQqEQ165dK2HL3pOeni5UKpX4+++/jbbDww9fX1+DOj/99JPw9/cX1tbWIiAgQKxbt85o/rGxscLX11fY2NiIsLAwER8fbzBdp9OJWbNmiQYNGgilUim6dOkiTp06VWKbH+x0Xaw0+8lff/0lunfvLmxtbYWzs7MYP368yMrKkpyvTqcTixYt0q+jm5ub6N27t8E6nDx5UkRFRQk7Ozvh6OgoOnfuLC5evCiEEKKwsFBMmDBBODs7C3d3dzFv3rwSO3MLIURmZqaYPHmy8PLyEtbW1qJhw4bi2WefFUlJSaKgoEA888wzomHDhsLGxkZ4eXmJSZMmGbynX3zxRfGf//xHcttVVmdumSjpZDKVKDMzE2q1GhkZGXBycjJ3c4jovvz8fFy+fFk/SjKZJpPJsGHDhmq/lUV59OrVCw0aNDAYEgAAxo8fjxs3bmDTpk2lms+0adOQkZFhMEQC1Tw3b95EQEAAjh49isaNG5usU9LnQFm+v9mZm4iILEpubi6WLFmC3r17Q6FQYPXq1dixY4dB/56MjAwcOXIEq1atwi+//FLqec+YMQOxsbHQarUm+1BRzXD58mX9LWOqGoMSERFZFJlMhi1btmDu3LkoKCiAv78/1q1bh549e+rrDBo0CIcPH8Z//vMf/UjjpaFWq/H2229XUcupurRr185oyIiqwqBERFRHWWrPC5VKhR07dpRY51FDARBVFl71RkRERCSBQYmIai1LPWJCRFWvst7/DEpEVOsUd9ItLCw0d1OIyEyK3/8V7bTPPkpEVOtYWVnBzs4ON2/ehLW1dakGXiSi2kOn0+HmzZuws7ODlVXFog6DEhHVOjKZDJ6enrh8+TL+/fdfczeHiMxALpejUaNGj7zp8KMwKBFRrWRjY4PmzZvz9BtRHWVjY1MpR5MZlIio1pLL5RyZm4gqhCfuiYiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJVuZuABnT6QSu3M5BVr4GjrZW8HOxh1wuM3eziIiI6hwGJQtzOiUD644nIzEtGwVFOiit5Wjm7oCnwnwQ5K02d/OIiIjqFAYlC3I6JQOf7ryA9JxCeKpVUKkVyCvU4lRyBlLu5OHlyOYMS0RERNWIfZQshE4nsO54MtJzCtHM3QEOtlZQyGVwsLVCM3cHpOcUYv3xFOh0wtxNJSIiqjMYlCzElds5SEzLhqdaBZnMsD+STCaDp1qFC2lZuHI7x2xtJCIiqmsYlCxEVr4GBUU6qGwUJqerbBQoKNIhK19T7W0jIiKqqxiULISjrRWU1nLkFWpNTs8r1EJpLYejLbuVERERVRcGJQvh52KPZu4OSM3IgxCG/ZCEEEjNyENzd0f4udibrY1ERER1DYOShZDLZXgqzAfO9jZITMtGdr4GWp1Adr4GiWnZcLa3wZAwb46nREREVI0YlCxIkLcaL0c2R7CPGnfzCnHlVg7u5hUixKcehwYgIiIyA3Z4sTBB3moEejpxZG4iIiILwKBkgeRyGZq4OZi7GURERHUeT70RERERSeARJQvEm+ISERFZhjodlLKystCjRw8UFRVBq9Xi5Zdfxvjx483aJt4Ul4iIyHLU6aBkZ2eH+Ph42NnZITc3F0FBQRgyZAhcXFzM0h7eFJeIiMiy1Ok+SgqFAnZ2dgCA/Px8aLVao8EeqwtviktERGR5LDoo7d27FwMHDoSXlxdkMhk2btxoVOeLL75A48aNYWtri/DwcOzbt69My7h79y5CQ0Ph4+ODadOmwdXVtRLXoPR4U1wiIiLLY9FBKScnB6Ghofj8889NTl+7di2mTJmCGTNm4MSJE+jcuTP69u2LpKQkfZ3w8HAEBQUZPa5duwYAqFevHk6ePInLly/jhx9+wI0bN6pt/R7Em+ISERFZHovuo9S3b1/07dtXcnpMTAxeeOEFjBs3DgCwcOFCbNu2DYsXL8a8efMAAMeOHSvVsjw8PBASEoK9e/di2LBhJusUFBSgoKBA/zwzM7OMayTtwZviOpi48S1viktERFT9LPqIUkkKCwtx7NgxREVFGZRHRUXhwIEDpZrHjRs39GEnMzMTe/fuhb+/v2T9efPmQa1W6x8NGzas4Fr8f7wpLhERkeWpsUHp1q1b0Gq18PDwMCj38PDA9evXSzWP5ORkdOnSBaGhoejUqRMmTZqEkJAQyfpvvfUWMjIy9I+rV69WeD2K8aa4RERElqfGn8d5uOOzEMKoTEp4eDgSEhJKvSylUgmlUlnmNpZW8U1xi8dRupF5bxylEJ96GBLmzaEBiIiIqlmNDUqurq5QKBRGR4/S0tKMjjLVJLwpLhERkeWosafebGxsEB4ejri4OIPyuLg4dOjQwWztqgzFN8UNbVgPTdwcGJKIiIjMxKKPKGVnZyMxMVH//PLly0hISICzszMaNWqEqVOnYtSoUWjTpg0iIiLw1VdfISkpCS+99JJZ201ERES1g0UHpaNHj6J79+7651OnTgUAREdHY8WKFXj66adx+/ZtzJkzB6mpqQgKCsKWLVvg6+trxlYTERFRbSET5rpnRy2QmZkJtVqNjIwMODk5mbs5REREVApl+f6usX2UiIiIiKoagxIRERGRBAYlIiIiIgkMSuUQGxuLwMBAtG3b1txNISIioirEztwVwM7cRERENU9Zvr8teniAukqnExyZm4iIyAIwKFmY0ykZ+nu9FRTdu9dbM3cHPBXmw3u9ERERVTMGJQtyOiUDn+68gPScQniqVVCpFcgr1OJUcgZS7uTh5cjmDEtERETViJ25LYROJ7DueDLScwrRzN0BDrZWUMhlcLC1QjN3B6TnFGL98RTodOxSRkREVF0YlCzElds5SEzLhqdaBZnMsD+STCaDp1qFC2lZuHI7x2xtJCIiqmsYlCxEVr4GBUU6qGwUJqerbBQoKNIhK19T7W0jIiKqqxiULISjrRWU1nLkFWpNTs8r1EJpLYejLbuVERERVRcGJQvh52KPZu4OSM3Iw8NDWwkhkJqRh+bujvBzsTdbG4mIiOoaBiULIZfL8FSYD5ztbZCYlo3sfA20OoHsfA0S07LhbG+DIWHeHE+JiIioGjEoWZAgbzVejmyOYB817uYV4sqtHNzNK0SITz0ODUBERGQG7PBSDrGxsYiNjYVWa7o/UUUEeasR6OnEkbmJiIgsAO/1VgH6e8Vcu1ap93rTaHQ4eOkWbmUXwtXBBhFNXGFlxYN/RERElSEzMxNqLy/e663aeHlV6uysAHSu1DkSERFRefAwBREREZEEHlGqDNeuARU89abR6DAwdh+u3s6HQg7YWCkgkwFCAIUaLbQ6oJGLCpsmduJpOCIioorIzCz12SAGpcpgb3/vUQF/nL+Bi7kyCFtbqKwVKHhwohIoKNIiMRf443ouuvl7VLTFREREdVcZLsbioQkLcTY1E1qtDtYSV7dZy2XQanU4m5pZ7W0jIiKqqxiULITK2gqQAVKXIAoAkN2vR0RERNWCQclCdGzmAqWVAkVaAd1DIzbohECRVsDWSoGOzVzM1kYiIqK6hkHJQjRzc0Rb3/oAgPwiHTS6e4FJoxPIL9IBANr4OqOZm6OZW0pERFR3MChZCLlchjf6BKBlA0dYK2Qo0uqQX6RDkVYHa4UMLRs44o0+/hyhm4iIqBqxw4sFCfJWY95TIfjpaBKO/nsXuQVa2CkVaOtbH0PbNOS93oiIiKoZg5KFuXevtyDe642IiMgCMCiVQ1XeFBf3T8M1cXOoknkTERFR6fGmuBWgvyluKW6qVxY6neARJSIioipSlu9vHlGyMKdTMrDueDIS07JRUKSD0lqOZu4OeCrMh32UiIiIqhmDkgU5nZKBT3deQHpOITzVKqjUCuQVanEqOQMpd/LwcmRzhiUiIqJqxOEBLIROJ7DueDLScwrRzN0BDrZWUMhlcLC1QjN3B6TnFGL98RTodDxTSkREVF0YlCzElds5SEzLhqdaBZnMsD+STCaDp1qFC2lZuHI7x2xtJCIiqmsYlCxEVr4GBUU6qGwUJqerbBQoKNIhK19T7W0jIiKqqxiULISjrRWU1nLkFZoeciCvUAultRyOtuxWRkREVF34rVtFYmJiEBMTU6bXZOZroNHqoJDL8ODJNwFAqxOwVsixe1bZ/mRTp07F1KlTy/QaIiIiuodBqYpkZmYiJSWlUueZByCzHO0gIiKi8mFQqiJOTk7w9vYu8+uKtAJ5RVpodToIAchkgJVcDltrBawVZR90sjIHwiQiIqprODJ3BXBkbiIiopqHI3PXcLzXGxERkWVgULJAPKJERERkGRiULAzv9UZERGQ5GJQsCO/1RkREZFk44GQ5xMbGIjAwEG3btq20efJeb0RERJaHQakcJk6ciLNnz+LIkSOVNk/e642IiMjyMChZCN7rjYiIyPIwKFkI3uuNiIjI8jAoWQg/F3s0c3dAakYedDodMvOKcDu7AJl5RdDpdEjNyENzd0f4udibu6lERER1Bg9PWAi5XIanwnxw7lomdp+/Cd0DA6bLZTI0cbXHkDBvjqdERERUjXhEydI8mIPEA0XMR0RERNWOR5QsRPHwABqtQA9/N2QXalGk0cHaSg4HGwUSb+Zg/fEUBHo68agSERFRNWFQshAGwwPI5XC0NTzY9+DwALwPHBERUfXgqTcLweEBiIiILA+DkoXg8ABERESWp1KCUlFREa5evYrz588jPT29MmZZ5zw4PIAQhrcpEUJweAAiIiIzKHdQys7Oxpdffolu3bpBrVbDz88PgYGBcHNzg6+vL8aPH1+pt/io7YqHB3C2t0FiWjay8zXQ6gSy8zVITMuGs70NhwcgIiKqZuUKSv/73//g5+eHr7/+Gj169MD69euRkJCA8+fP4+DBg5g1axY0Gg169eqFPn364MKFC5Xf8looyFuNlyObI9hHjbt5hbhyKwd38woR4lMPL0c2R5C32txNJCIiqlNk4uHzPKUwbNgwzJw5E8HBwSXWKygowNKlS2FjY4Nx48ZVpJ0WKTMzE2q1GhkZGXBycqq0+ep0Aldu5yArXwNHWyv4udjzSBIREVElKcv3d7mCEt1TVUGJiIiIqk5Zvr951RsRERGRhAoHpby8POTm5uqf//vvv1i4cCG2b99e0VkTERERmVWFg9KgQYOwcuVKAMDdu3fx+OOP45NPPsGgQYOwePHiymgjERERkVlUOCgdP34cnTt3BgD8/PPP8PDwwL///ouVK1fi008/rYw2WpzY2FgEBgaibdu25m4KERERVaEKB6Xc3Fw4OjoCALZv344hQ4ZALpejffv2+PfffyujjRZn4sSJOHv2LMeJIiIiquUqHJSaNWuGjRs34urVq9i2bRuioqIAAGlpabwSjIiIiGq0CgelmTNn4vXXX4efnx8ef/xxREREAPePLrVu3boy2khERERkFuUeRykhIQGtWrUCAFy/fh2pqakIDQ2FXH4vex0+fBhOTk4ICAio3BZbEI6jREREVPNUyzhKYWFhCA8Px+LFi6FSqdC6dWt9SAKAdu3a1eqQRERERLVfuYPS/v37ERYWhunTp8PT0xPPPfccdu/eXbmtIyIiIjKjcgeliIgIfP3117h+/ToWL16M5ORk9OzZE02bNsX777+P5OTkym0pERERUTWrcGdulUqF6Oho7NmzB//88w9GjBiBL7/8Eo0bN0a/fv0qp5VEREREZlDpN8XNzs7GqlWr8Pbbb+Pu3bvQarWVOXuLws7cRERENU9Zvr+tKmuh8fHxWLZsGdatWweFQoHhw4fjhRdeqKzZExEREVW7CgWlq1evYsWKFVixYgUuX76MDh064LPPPsPw4cNhb29fea0kIiKiukWnA9IvAQUZgFINODcB5BXuMVRm5Q5KvXr1wu7du+Hm5obRo0dj7Nix8Pf3r9zWERERUd2TehJIWA3cPA9o8gErW8DNH2g1AvAMrdamlDsoqVQqrFu3DgMGDIBCoajcVhEREVHdlHoSiJ8P5N4GnLwBazugKBdIPQFkJAFd36zWsFTuY1iPPfYYPD09GZKIiIiocuh0944k5d4GXP0BpSMgV9z719X/XvnJNffqVZNyB6Xr169jwIAB8PT0xIsvvojNmzejoKCgcltHREREdUf6pXun25y8AZnMcJpMdq887e979apJuYPS8uXLcePGDfz444+oV68eXnvtNbi6umLIkCFYsWIFbt26VbktJSIiotqtIONenyRrO9PTrVX3phdkVFuTKtR9XCaToXPnzvjoo4/w999/4/Dhw2jfvj2+/vpreHt7o0uXLvj444+RkpJSeS0mIiKi2kmpvtdxuyjX9PSivHvTlepqa1KlXmfXsmVLTJs2Dfv370dycjKio6Oxb98+rF69ujIXQ0RERLWRc5N7V7dlpgAPj4ctxL1y94B79apJmUfmzsvLQ3p6Ory9vQ3Kz5w5g8cee6yy22fRODI3ERFRJTO66k1170hSZgpg5wp0nVbhq97K8v1dpiNKP//8M1q0aIF+/fohJCQEhw4d0k8bNWpU+VtMREREBNwLQV3fBDxbA7l3gNuX7v3rFVYpIamsyjSO0ty5c3H8+HG4ubnh6NGjiI6OxowZMzBy5EhU8i3jiIiIqK7yDAU8gmveyNxFRUVwc3MDALRp0wZ79+7FkCFDkJiYCNnDl/HVYrGxsYiNja3VN/wlIiKiMvZR6t69OxYtWoSQkBB9WWFhIaKjo/HTTz9Bo9FUVTstEvsoERERVYEqvoVJWb6/yxSUkpOTYWVlhQYNGhhN279/Pzp27Fi+FtdQDEpERESVTOoWJpkpgJ1LpdzCpCzf32U69ebj4yM5ra6FpKqk0wlcuZ2DrHwNHG2t4OdiD7m87pzaJCKiOurhW5gUd+spvoXJrfP3bmHiEVxt/ZXKfVPcB+Xn5+Ovv/5CWloadA/df+WJJ56ojEXUGadTMrDueDIS07JRUKSD0lqOZu4OeCrMB0He1TfAFhERUbUryy1MXJtVS5MqHJR+//13jB492uQtS2QyGTs8l8HplAx8uvMC0nMK4alWQaVWIK9Qi1PJGUi5k4eXI5szLBERUe1VqluYpNacW5gAwKRJkzBs2DCkpqZCp9MZPBiSSk+nE1h3PBnpOYVo5u4AB1srKOQyONhaoZm7A9JzCrH+eAp0Og7DQEREtVRtvIVJWloapk6dCg8Pj8ppUR115XYOEtOy4alWGQ21IJPJ4KlW4UJaFq7czjFbG4mIiKqUBd7CpMJBaejQodizZ0/ltKYOy8rXoKBIB5WNwuR0lY0CBUU6ZOXXrSEYiIioDpHL7w0BYOdyr+N2QRag09z799b5e7cwCX2mWgeerHAfpc8//xzDhg3Dvn37EBwcDGtra4PpL7/8ckUXUSc42lpBaS1HXqEWDrbGf5a8Qi2U1nI4mphGRERUaxTfwkQ/jlLqvdNtXmH3QpIl38LElB9++AHbtm2DSqXCnj17DE4byWQyBqVS8nOxRzN3B5xKzkAzpYPBdhRCIDUjDyE+9eDnYm/WdhIREVW5mnoLE1PeeecdzJkzB9OnT4fcDCtQW8jlMjwV5oOUO3n6vkoqm3tXvaVm5MHZ3gZDwrw5nhIREdUNcnm1DQFQYjMqOoPCwkI8/fTTDEmVIMhbjZcjmyPYR427eYW4cisHd/MKEeJTj0MDEBERmUGFjyhFR0dj7dq1ePvttyunRXVckLcagZ5OHJmbiIjIAlQ4KGm1Wnz00UfYtm0bQkJCjDpzx8TEVHQRdY5cLkMTNwdzN4OIiKjOq3BQOnXqFFq3bg0AOH36tMG0h8cDIiIiorojJibGIg6YTJ06FVOnTi3XaysclHbv3l3RWRAREVEtlJmZiZSUFHM3A5mZmeV+LQflISIioirh5OQEb29vczcDTk5O5X6tTIiHxwgvm3nz5sHDwwNjx441KF+2bBlu3ryJN998syKzt2iZmZlQq9XIyMio0B+BiIiIqk9Zvr8rfE3/l19+iYCAAKPyxx57DEuWLKno7ImIiIjMpsJB6fr16/D09DQqd3NzQ2pqakVnT0RERGQ2FQ5KDRs2xP79+43K9+/fDy8vr4rOnoiIiMhsKtyZe9y4cZgyZQqKiorQo0cPAMDOnTsxbdo0vPbaa5XRRiIiIiKzqHBQmjZtGtLT0/F///d/KCwsBADY2trizTffxFtvvVUZbSQiIiIyiwpf9VYsOzsb586dg0qlQvPmzaFUKitjthaNV70RERHVPFV+1VtSUpJRmYODA9q2bYugoCCjkGQJg00RERERlVW5glLbtm0xfvx4HD58WLJORkYGvv76awQFBWH9+vUVaSMRERGRWZSrj9K5c+fwwQcfoE+fPrC2tkabNm3g5eUFW1tb3LlzB2fPnsWZM2fQpk0bLFiwAH379q38lhMRERFVsQr1UcrPz8eWLVuwb98+XLlyBXl5eXB1dUXr1q3Ru3dvBAUFVW5rLQz7KBEREdU8Zfn+rrTO3HURgxIREVHNU623MKmLYmNjERgYiLZt25q7KURERFSFeESpAnhEiYiIqObhESUiIiKiSsCgRERERCShwkHp6tWrldMSIiIiIgtT4Xu9+fr6on79+ggNDUVoaChatWqF0NBQFBQUIDY2FitXrqyclhIRERFVswoHpUuXLiEhIQEJCQk4ceIEfv75Z1y7dg0A2MGZiIiIarQKByU/Pz/4+flh8ODB+rKDBw8iOjoa8+fPr+jsiYiIiMymSjpzR0REYNGiRZg7d25VzJ6IiIioWlQ4KBUVFZksb968Oc6cOVPR2RMRERGZTYVPvdnb2yMwMBCtW7dGq1at0Lp1a3h5eeGzzz5DVFRU5bSSiIiIyAwqPDL3H3/8gZMnT+LkyZNISEjAmTNnkJeXBwCIiopCeHg4QkJCEBISgpYtW1ZWuy0CR+YmIiKqecx6U1ydTofz58/rr4QrDlFpaWnQarWVuSizY1AiIiKqecwalKTcuHEDHh4e1bGoasOgREREVEV0OiD9ElCQASjVgHMTQF4516CV5fu7wn2USqu2hSQiIiKqIqkngYTVwM3zgCYfsLIF3PyBViMAz9BqbUq1BSUiIiKiR0o9CcTPB3JvA07egLUdUJQLpJ4AMpKArm9Wa1jiTXGJiIjIMuh0944k5d4GXP0BpSMgV9z719X/XvnJNffqVRMGJSIiIrIM6ZfunW5z8gZkMsNpMtm98rS/79WrJgxKREREZBkKMu71SbK2Mz3dWnVvekFGtTWJQYmIiIgsg1J9r+N2Ua7p6UV596Yr1dXWJAYlIiIisgzOTe5d3ZaZAjw8epEQ98rdA+7VqyYMSkRERGQZ5PJ7QwDYuQC3zgMFWYBOc+/fW+cBO1cg9JlKG0+pVE2qtiURERERPYpn6P0hAFoDuXeA25fu/esVBnSdxnGUiIiIqI7zDAU8gqtsZO6yYFAiIiIiyyOXA67NzN0KnnojIiIiksKgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAqh9jYWAQGBqJt27bmbgoRERFVIZkQQpi7ETVVZmYm1Go1MjIy4OTkZO7mEBERUSmU5fubR5SIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSgByc3Ph6+uL119/3dxNISIiIgvCoATg/fffx+OPP27uZhAREZGFqfNB6cKFC/j777/Rr18/czeFiIiILIxFB6W9e/di4MCB8PLygkwmw8aNG43qfPHFF2jcuDFsbW0RHh6Offv2lWkZr7/+OubNm1eJrSYiIqLawqKDUk5ODkJDQ/H555+bnL527VpMmTIFM2bMwIkTJ9C5c2f07dsXSUlJ+jrh4eEICgoyely7dg2//PILWrRogRYtWlTjWhEREVFNIRNCCHM3ojRkMhk2bNiAwYMH68sef/xxhIWFYfHixfqyli1bYvDgwaU6SvTWW2/h+++/h0KhQHZ2NoqKivDaa69h5syZJusXFBSgoKBA/zwzMxMNGzZERkYGnJycKryOREREVPUyMzOhVqtL9f1t0UeUSlJYWIhjx44hKirKoDwqKgoHDhwo1TzmzZuHq1ev4sqVK/j4448xfvx4yZBUXF+tVusfDRs2rPB6EBERkeWqsUHp1q1b0Gq18PDwMCj38PDA9evXq2SZb731FjIyMvSPq1evVslyiIiIyDJYmbsBFSWTyQyeCyGMykpjzJgxj6yjVCqhVCrLPG8iIiKqmWrsESVXV1coFAqjo0dpaWlGR5mIiIiIyqPGBiUbGxuEh4cjLi7OoDwuLg4dOnQwW7uIiIio9rDoU2/Z2dlITEzUP798+TISEhLg7OyMRo0aYerUqRg1ahTatGmDiIgIfPXVV0hKSsJLL71k1nYTERFR7WDRQeno0aPo3r27/vnUqVMBANHR0VixYgWefvpp3L59G3PmzEFqaiqCgoKwZcsW+Pr6mrHVREREVFvUmHGULFFZxmEgIiIiy1AnxlEiIiIiqmoMSkREREQSGJSIiIiIJDAolUNsbCwCAwPRtm1bczeFiIiIqhA7c1cAO3MTERHVPOzMTURERFQJGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAolQPv9UZERFQ38F5vFcB7vREREdU8vNcbERERUSWwMncDyJhOJ3Dldg6y8jVwtLWCn4s95HKZuZtFRERU5zAoWZjTKRlYdzwZiWnZKCjSQWktRzN3BzwV5oMgb7W5m0dERFSnMChZkNMpGfh05wWk5xTCU62CSq1AXqEWp5IzkHInDy9HNmdYIiIiqkbso2QhdDqBdceTkZ5TiGbuDnCwtYJCLoODrRWauTsgPacQ64+nQKdj33siIqLqwqBkIa7czkFiWjY81SrIZIb9kWQyGTzVKlxIy8KV2zlmayMREVFdw6BkIbLyNSgo0kFlozA5XWWjQEGRDln5mmpvGxERUV3FoGQhHG2toLSWI69Qa3J6XqEWSms5HG3ZrYyIiKi6MChZCD8XezRzd0BqRh4eHgNUCIHUjDw0d3eEn4u92dpIRERU1zAoWQi5XIanwnzgbG+DxLRsZOdroNUJZOdrkJiWDWd7GwwJ8+Z4SkRERNWIQcmCBHmr8XJkcwT7qHE3rxBXbuXgbl4hQnzqcWgAIiIiM2CHl3KIjY1FbGwstFrT/YkqIshbjUBPJ47MTUREZAF4U9wK4E1xiYiIah7eFJeIiIioEjAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJPAWJhVQPKh5ZmamuZtCREREpVT8vV2am5MwKFVAVlYWAKBhw4bmbgoRERGVUVZWFtTqkm84z3u9VYBOp8O1a9fg6OgImaxyb1qbmZmJhg0b4urVq7yPHFWqtm3b4siRI+ZuRq1Vl7dvTV93S26/pbTNHO2oimUKIZCVlQUvLy/I5SX3QuIRpQqQy+Xw8fGp0mU4OTkxKFGlUigU3KeqUF3evjV93S25/ZbSNnO0o6qW+agjScXYmZuojpk4caK5m1Cr1eXtW9PX3ZLbbyltM0c7zL3uPPVmoTIzM6FWq5GRkWERvyKIiIjqIh5RslBKpRKzZs2CUqk0d1OIiIjqLB5RIiIiIpLAI0pEREREEhiUiIiIiCQwKBERERFJYFAionLLyspC27Zt0apVKwQHB+Prr782d5Nqlbq6fevqelcXbt+yYWduIio3rVaLgoIC2NnZITc3F0FBQThy5AhcXFzM3bRaoa5u37q63tWF27dseESphuEvAbIkCoUCdnZ2AID8/HxotdpS3WSSSqeubt+6ut7Vhdu3bBiUahg7OzvEx8cjISEBhw4dwrx583D79m1zN4sq2bx589C2bVs4OjrC3d0dgwcPxvnz5yt1GXv37sXAgQPh5eUFmUyGjRs3mqz3xRdfoHHjxrC1tUV4eDj27dtnMP3u3bsIDQ2Fj48Ppk2bBldX10ptZ1VYvHgxQkJC9LcIioiIwNatWyt1GZa+fefNmweZTIYpU6ZUyvyKWfp6V7WUlBQ899xzcHFxgZ2dHVq1aoVjx45V2vzr+vY1BwalGoa/BOqG+Ph4TJw4EX/++Sfi4uKg0WgQFRWFnJwck/X379+PoqIio/K///4b169fN/manJwchIaG4vPPP5dsx9q1azFlyhTMmDEDJ06cQOfOndG3b18kJSXp69SrVw8nT57E5cuX8cMPP+DGjRvlWufq5OPjgw8//BBHjx7F0aNH0aNHDwwaNAhnzpwxWb+2bd8jR47gq6++QkhISIn1att6V7U7d+6gY8eOsLa2xtatW3H27Fl88sknqFevnsn63L41hKBqFR8fLwYMGCA8PT0FALFhwwajOrGxscLPz08olUoRFhYm9u7dazD9zp07IiQkRKhUKvH5559XY+vJXNLS0gQAER8fbzRNq9WK0NBQMXToUKHRaPTl58+fFw0aNBDz589/5Pyl9sV27dqJl156yaAsICBATJ8+3eR8XnrpJfHjjz+Wcq0sS/369cU333xjVF7btm9WVpZo3ry5iIuLE127dhWvvPKKyXq1bb2rw5tvvik6depUqrrcvjUHjyhVs0f9GuAvATIlIyMDAODs7Gw0TS6XY8uWLThx4gRGjx4NnU6HixcvokePHnjiiScwbdq0ci2zsLAQx44dQ1RUlEF5VFQUDhw4AAC4ceMGMjMzgfv3J9y7dy/8/f3LtTxz0Wq1WLNmDXJychAREWE0vbZt34kTJ6J///7o2bNnifVq23pXh02bNqFNmzYYNmwY3N3d0bp1a8l+pNy+NYeVuRtQ1/Tt2xd9+/aVnB4TE4MXXngB48aNAwAsXLgQ27Ztw+LFizFv3jyDuh4eHggJCcHevXsxbNiwKm87mYcQAlOnTkWnTp0QFBRkso6Xlxd27dqFLl26YOTIkTh48CAiIyOxZMmSci/31q1b0Gq18PDwMCj38PDQnxZITk7GCy+8ACEEhBCYNGnSI0/nWIpTp04hIiIC+fn5cHBwwIYNGxAYGGiybm3ZvmvWrMHx48dx5MiRUtWvLetdXS5duoTFixdj6tSpePvtt3H48GG8/PLLUCqVGD16tFF9bt+agUHJghT/Epg+fbpB+cO/BFQqFZycnPS/BCZMmGCmFlN1mDRpEv766y/88ccfJdZr1KgRVq5cia5du6JJkyZYunQpZDJZhZf/8DyEEPqy8PBwJCQkVHgZ5uDv74+EhATcvXsX69atQ3R0NOLj4yXDUk3fvlevXsUrr7yC7du3w9bWttSvq+nrXZ10Oh3atGmDDz74AADQunVrnDlzBosXLzYZlMDtWyPw1JsFKe0vgS5duiA0NBSdOnXiL4FabvLkydi0aRN2794NHx+fEuveuHEDL774IgYOHIjc3Fy8+uqrFVq2q6srFAqFUafStLQ0o320JrKxsUGzZs3Qpk0bzJs3D6GhoVi0aJFk/Zq+fY8dO4a0tDSEh4fDysoKVlZWiI+Px6effgorKytotVqTr6vp612dPD09jYJ2y5YtDbpOPIzb1/LxiJIF4i8BEkJg8uTJ2LBhA/bs2YPGjRuXWP/WrVuIjIxEy5Yt8dNPP+HChQvo1q0blEolPv7443K1wcbGBuHh4YiLi8OTTz6pL4+Li8OgQYPKNU9LJoRAQUGByWm1YftGRkbi1KlTBmXPP/88AgIC8Oabb0KhUBi9pjasd3Xq2LGj0TAe//zzD3x9fU3W5/atIczdm7wue/iKhYKCAqFQKMT69esN6r388suiS5cuZmghmcuECROEWq0We/bsEampqfpHbm6uUV2tVivCw8NFv379REFBgb78r7/+Ei4uLiImJsbkMrKyssSJEyfEiRMnBAARExMjTpw4If799199nTVr1ghra2uxdOlScfbsWTFlyhRhb28vrly5UkVrXj3eeustsXfvXnH58mXx119/ibffflvI5XKxfft2o7q1efs+6qq32rreVeXw4cPCyspKvP/+++LChQti1apVws7OTnz//fdGdbl9aw4GJTMydWlnu3btxIQJEwzKWrZsKXlZJ9VOAEw+li9fbrL+9u3bRV5enlH5iRMnRFJSksnX7N692+QyoqOjDerFxsYKX19fYWNjI8LCwkwOUVDTjB07Vr9Obm5uIjIy0mRIKlZbt29JQUnU4vWuSr/++qsICgoSSqVSBAQEiK+++kqyLrdvzcB7vVWz7OxsJCYmAvc7+sXExKB79+5wdnZGo0aNsHbtWowaNQpLlixBREQEvvrqK3z99dc4c+aM5OFbIiIiqhoMStVsz5496N69u1F5dHQ0VqxYAdwfev6jjz5CamoqgoKC8L///Q9dunQxQ2uJiIjqNgYlIiIiIgkcHoCIiIhIAoMSERERkQQGJSIiIiIJDEpEREREEhiUiIiIiCQwKBERERFJYFAiIiIiksCgRERERCSBQYmIiIhIAoMSEdVZY8aMweDBg83dDCKyYLyFCRHVWRkZGRBCoF69egCAbt26oVWrVli4cKG5m0ZEFsLK3A0gIjIXtVpt7iYQkYXjqTciqvV+/vlnBAcHQ6VSwcXFBT179kROTo7BqbcxY8YgPj4eixYtgkwmg0wmw5UrVwAAZ8+eRb9+/eDg4AAPDw+MGjUKt27deuT8iajmY1AiolotNTUVI0aMwNixY3Hu3Dns2bMHQ4YMwcO9DhYtWoSIiAiMHz8eqampSE1NRcOGDZGamoquXbuiVatWOHr0KH7//XfcuHEDw4cPL9P8iahm4qk3IqrVUlNTodFoMGTIEPj6+gIAgoODjeqp1WrY2NjAzs4ODRo00JcvXrwYYWFh+OCDD/Rly5YtQ8OGDfHPP/8gOzu7VPMnopqJR5SIqFYLDQ1FZGQkgoODMWzYMHz99de4c+dOqV9/7Ngx7N69Gw4ODvpHQEAAAODixYsVnj8RWTYGJSKq1RQKBeLi4rB161YEBgbis88+g7+/Py5fvlyq1+t0OgwcOBAJCQkGjwsXLqBLly4Vnj8RWTYGJSKq9WQyGTp27IjZs2fjxIkTsLGxwYYNG4zq2djYQKvVGpSFhYXhzJkz8PPzQ7NmzQwe9vb2ZZo/EdU8DEpEVKsdOnQIH3zwAY4ePYqkpCSsX78eN2/eRMuWLY3q+vn54dChQ7hy5Qpu3boFnU6HiRMnIj09HSNGjMDhw4dx6dIlbN++HWPHjoVWqy3T/Imo5mFQIqJazcnJCXv37kW/fv3QokULvPPOO/jkk0/Qt29fo7qvv/46FAoFAgMD4ebmhqSkJHh5eWH//v3QarXo3bs3goKC8Morr0CtVkMul5dp/kRU83BkbiIiIiIJPKJEREREJIFBiYiIiEgCgxIRERGRBAYlIiIiIgkMSkREREQSGJSIiIiIJDAoEREREUlgUCIiIiKSwKBEREREJIFBiYiIiEgCgxIRERGRhP8H2UaJ52kVtWoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n, values in madn.items():\n",
    "    plt.semilogy([n] * len(values), values, \"o\", alpha=0.6)\n",
    "    plt.semilogy(n, np.exp(np.mean(np.log(values))), \"k_\", ms=25, mew=2)\n",
    "plt.axhline(mu_madn, color=\"r\", label=\"morphology (1000 molecules)\")\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(\"sites\")\n",
    "plt.ylabel(r\"$\\mu$ (cm$^2$/Vs)\")\n",
    "plt.legend()\n",
    "plt.title(\"MADN holes: model networks (black: geometric mean)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e756bda",
   "metadata": {},
   "source": [
    "The morphology lies within the spread of the model boxes of the same size: the model reproduces its transport. The boxes of 1000 sites scatter by a factor of a few, single boxes by more than an order of magnitude; boxes of 8000 sites scatter less, and their mean is lower. Even 8000 sites are not converged: for $\\sigma\\approx3.7\\,k_BT$ the criterion of Baumeier et al., $(\\sigma/k_BT)^2\\le-5.7+1.05\\ln N$, asks for about $10^8$ sites. In a study one would continue to $10^5$–$10^6$ sites (the exact solution takes minutes for $10^5$ sites; beyond that, use the kinetic Monte Carlo results) and extrapolate.\n",
    "\n",
    "## BCP: strong disorder and a trap\n",
    "\n",
    "BCP has a much broader DOS. The fit also looks for traps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7d20a78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:02:47.108563Z",
     "iopub.status.busy": "2026-11-10T21:02:47.108346Z",
     "iopub.status.idle": "2026-11-10T21:04:39.945488Z",
     "shell.execute_reply": "2026-11-10T21:04:39.944891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read BCP.hdf5 frame 0: 1000 sites, 9040 pairs, box 8.1484 x 8.1484 x 8.1484 nm^3\n",
      "Traps for h (slow modes of the master equation at 300 K): largest eigenvalue ratio 658 after 1 slow mode(s)\n",
      "    trap of 1 site(s): 66 (-0.913 eV)\n",
      "      equilibrium weight 1; release rate 0.000635 1/s, capture rate 2.67 1/s (slow mode 2.68 1/s); hops out 1.24e+03 1/s\n",
      "Traps for e (slow modes of the master equation at 300 K): largest eigenvalue ratio 14.8 after 1 slow mode(s)\n",
      "    trap of 1 site(s): 922 (-0.768 eV)\n",
      "      equilibrium weight 0.998; release rate 79.1 1/s, capture rate 4.18e+04 1/s (slow mode 4.19e+04 1/s); hops out 1.84e+06 1/s\n",
      "Point process: r_h = 0.323292 nm, radius mean = 0.149429 nm, variance = 0.00928965 nm^2 (objective 0.00357693, 89 evaluations)\n",
      "Connection probability tabulated up to 1.9 nm\n",
      "Carrier h: site energies mean 6.23058 eV, sigma 0.190251 eV (sample), 0.193911 eV = 7.50081 kT (model, corrected for the finite system)\n",
      "    moving average w_a = 0.278266, w_b = 0.450593 (l_b = 10), w_c = 0.271141 (l_c = 120), rms of kappa 0.0181197, mean correlation over all pairs 0.0373959\n",
      "    log10(J^2/eV^2) = -1.13814 + -4.81721 r/nm, variance 0.555497 + 2.09411 r/nm; residual skewness -0.532485, excess kurtosis 0.541252; 9040 couplings\n",
      "    lambda = 0.493971 eV (U_nX_nN 0.252321, U_xN_xX 0.241649), internal energy U_xX_nN 7.20386 eV\n",
      "    the density of occupied states at 300 K needs about 4.25e+25 sites\n",
      "Carrier e: site energies mean -0.670845 eV, sigma 0.1956 eV (sample), 0.200109 eV = 7.74056 kT (model, corrected for the finite system)\n",
      "    moving average w_a = 0.251142, w_b = 0.545231 (l_b = 2), w_c = 0.203626 (l_c = 360), rms of kappa 0.0172329, mean correlation over all pairs 0.044557\n",
      "    log10(J^2/eV^2) = -1.04847 + -4.66106 r/nm, variance 0.500034 + 1.75677 r/nm; residual skewness -0.620295, excess kurtosis 0.911702; 9040 couplings\n",
      "    lambda = 0.5702 eV (U_nX_nN 0.290565, U_xN_xX 0.279635), internal energy U_xX_nN -0.194843 eV\n",
      "    the density of occupied states at 300 K needs about 1.38e+27 sites\n",
      "Correlation of the site energies of h and e: -0.899183, of the coupling residuals: 0.569213\n",
      "Wrote BCP_network.xml\n",
      "Wrote BCP_E.txt\n",
      "Validation: 4 model network(s) of 1000 sites (seeds 1 to 4), mean and standard deviation over the networks; 8 trap(s) inserted in total:\n",
      "    quantity                              microscopic   model         sd\n",
      "    pairs per site                        18.08         18.52         0.08562\n",
      "    mean nearest neighbour distance (nm)  0.6264        0.634         0.003033\n",
      "    sigma h (eV)                          0.1923        0.1918        0.004267\n",
      "    mean log10(J^2/eV^2) h                -6.434        -6.366        0.01519\n",
      "    sd log10(J^2/eV^2) h                  2.106         2.138         0.001556\n",
      "    kappa h at 0.75 nm                    0.4619        0.4259        0.08725\n",
      "    kappa h at 1.00 nm                    0.3663        0.3462        0.05554\n",
      "    kappa h at 1.50 nm                    0.1754        0.1949        0.0451\n",
      "    kappa h at 2.00 nm                    0.0822        0.09835       0.05064\n",
      "    sigma e (eV)                          0.1985        0.1977        0.005532\n",
      "    mean log10(J^2/eV^2) e                -6.173        -6.115        0.02196\n",
      "    sd log10(J^2/eV^2) e                  1.977         2.004         0.02672\n",
      "    kappa e at 0.75 nm                    0.4583        0.3775        0.088\n",
      "    kappa e at 1.00 nm                    0.3233        0.3147        0.06236\n",
      "    kappa e at 1.50 nm                    0.1624        0.1917        0.06001\n",
      "    kappa e at 2.00 nm                    0.08519       0.1017        0.06399\n",
      "    site energy correlation               -0.8966       -0.9041       0.01014\n",
      "    log10 D zero field h (m^2/s)          -19.03        -18.72        0.4978\n",
      "    traps found h                         1             1.5           0.866\n",
      "    log10 D zero field e (m^2/s)          -16           -16.33        1.752\n",
      "    traps found e                         1             1.25          0.433\n",
      "Wrote BCP_stats.dat\n",
      "==============================================================================\n",
      "Please cite the methods used in this run:\n",
      "  [1] Baumeier, Wehner, Renaud, Zapata Ruiz, Halver, Madhikar, Gerritsen,\n",
      "      Tirimbo, Sijen, Rosenberger, Brown, Sundaram, Krajniak, Bernhardt, and\n",
      "      Junghans, VOTCA: multiscale frameworks for quantum and classical\n",
      "      simulations in soft matter, J. Open Source Softw. 9, 6864 (2024),\n",
      "      doi:10.21105/joss.06864\n",
      "  [2] Baumeier, Stenzel, Poelking, Andrienko, and Schmidt, Stochastic modeling\n",
      "      of molecular charge transport networks, Phys. Rev. B 86, 184202 (2012),\n",
      "      doi:10.1103/PhysRevB.86.184202\n",
      "  [3] Kordt, Stenzel, Baumeier, Schmidt, and Andrienko, Parametrization of\n",
      "      extended Gaussian disorder models from microscopic charge transport\n",
      "      simulations, J. Chem. Theory Comput. 10, 2508 (2014),\n",
      "      doi:10.1021/ct500269r\n",
      "  [4] Chen, van der Hoorn, and Baumeier, Automatic identification of traps in\n",
      "      molecular charge transport networks of organic semiconductors, Phys.\n",
      "      Rev. B 111, 224209 (2025), doi:10.1103/shyl-kcfp\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "log = run(\"xtp_tools -e networkfit -c job_name=BCP statefile=BCP.hdf5 'carriers=h e' \"\n",
    "          \"statsfile=BCP_stats.dat energyfile=BCP_E.txt\")\n",
    "show_log(log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86093d25",
   "metadata": {},
   "source": [
    "For holes `networkfit` finds a single-molecule trap: one molecule 0.9 eV below the mean holds practically all of the equilibrium weight. A hole leaves it at a rate of about $10^{-3}$ s$^{-1}$, although it hops out at about $10^3$ s$^{-1}$: almost every hop out is followed by a hop back. For electrons there is a trap, too. The trap is far outside the Gaussian DOS of the rest of the morphology:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1060082e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:04:39.948035Z",
     "iopub.status.busy": "2026-11-10T21:04:39.947610Z",
     "iopub.status.idle": "2026-11-10T21:04:40.076027Z",
     "shell.execute_reply": "2026-11-10T21:04:40.075429Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigma/kT = 7.36; sites needed by the criterion: 5.6e+24\n"
     ]
    }
   ],
   "source": [
    "E = np.loadtxt(\"BCP_E.txt\")  # id, x, y, z, E_h, E_e, hole trap, electron trap\n",
    "eh, trap = E[:, 4], E[:, 6]\n",
    "bulk = eh[trap == 0]\n",
    "x = np.linspace(eh.min() - 0.1, eh.max() + 0.1, 300)\n",
    "plt.hist(eh, bins=50, density=True, alpha=0.5, label=\"morphology\")\n",
    "plt.plot(x, np.exp(-(x - bulk.mean())**2 / (2 * bulk.std()**2)) / np.sqrt(2 * np.pi) / bulk.std(), \"k-\",\n",
    "         label=f\"Gaussian, $\\\\sigma$ = {bulk.std():.3f} eV = {bulk.std() / kT:.1f} $k_BT$\")\n",
    "for e in eh[trap == 1]:\n",
    "    plt.axvline(e, color=\"r\", ls=\"--\", label=\"hole trap\")\n",
    "plt.axvline(bulk.mean() - bulk.std()**2 / kT, color=\"g\", ls=\":\", label=r\"equilibrium energy $-\\sigma^2/k_BT$\")\n",
    "plt.xlabel(\"hole site energy (eV)\")\n",
    "plt.ylabel(\"density\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "s = bulk.std() / kT\n",
    "print(f\"sigma/kT = {s:.2f}; sites needed by the criterion: {np.exp((s**2 + 5.7) / 1.05):.1e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854dc4a3",
   "metadata": {},
   "source": [
    "In an infinite Gaussian DOS of this width a hole would sit around $-\\sigma^2/k_BT$, far below every site of the box. With $\\sigma/k_BT\\approx7.5$ the criterion asks for about $10^{25}$ sites, which no simulation reaches.\n",
    "\n",
    "The trap is stored as a template in the parameter file (its energies for both carriers relative to the bulk mean) with the density of the morphology, one per 1000 sites; `networkgen` inserts it at random places. How much does it matter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f3273ee2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:04:40.078179Z",
     "iopub.status.busy": "2026-11-10T21:04:40.077769Z",
     "iopub.status.idle": "2026-11-10T21:04:52.542991Z",
     "shell.execute_reply": "2026-11-10T21:04:52.542383Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCP morphology: 2.664e-14 cm^2/Vs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "without traps: 1.0e-08 1.5e-06 5.1e-08 3.0e-07 1.1e-08 7.3e-08 7.8e-08 7.7e-10\n",
      "with traps:    7.1e-14 2.9e-12 5.1e-14 5.6e-14 2.4e-13 2.7e-14 4.7e-14 1.7e-14\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu_bcp = mobility(\"BCP.hdf5\")\n",
    "print(f\"BCP morphology: {mu_bcp:.3e} cm^2/Vs\")\n",
    "bcp_traps = [mobility(generate(\"BCP_network.xml\", f\"model_bcp_t{s}\", seed=s)) for s in range(1, 9)]\n",
    "bcp_plain = [mobility(generate(\"BCP_network.xml\", f\"model_bcp_p{s}\", seed=s, trapfactor=0)) for s in range(1, 9)]\n",
    "print(\"without traps:\", \" \".join(f\"{m:.1e}\" for m in bcp_plain))\n",
    "print(\"with traps:   \", \" \".join(f\"{m:.1e}\" for m in bcp_traps))\n",
    "plt.semilogy([1] * 8, bcp_plain, \"o\", label=\"model, Gaussian only\")\n",
    "plt.semilogy([2] * 8, bcp_traps, \"s\", label=\"model with the trap template\")\n",
    "plt.axhline(mu_bcp, color=\"r\", label=\"morphology\")\n",
    "plt.xticks([1, 2], [\"no traps\", \"with traps\"])\n",
    "plt.xlim(0.5, 2.5)\n",
    "plt.ylabel(r\"$\\mu$ (cm$^2$/Vs)\")\n",
    "plt.title(\"BCP holes, boxes of 1000 sites\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a5279d",
   "metadata": {},
   "source": [
    "Without the trap the model boxes are four to eight orders of magnitude faster than the morphology; with one trap per box most come within a factor of a few (the trap template is the same, its surroundings differ). A Gaussian model alone cannot describe BCP.\n",
    "\n",
    "The trap density, however, is the least certain parameter: one trap in 1000 molecules fixes it only to between 0.025 and 5.6 times $10^{-3}$ per site (95 % Poisson interval). `trapfactor` multiplies the density; a scan with networks of 8000 sites:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1933cb8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:04:52.545438Z",
     "iopub.status.busy": "2026-11-10T21:04:52.544858Z",
     "iopub.status.idle": "2026-11-10T21:07:09.859350Z",
     "shell.execute_reply": "2026-11-10T21:07:09.858797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors = [0, 0.25, 1, 4]\n",
    "scan = {f: [mobility(generate(\"BCP_network.xml\", f\"model_bcp8k_{f}_{s}\", sites=8000, seed=s, trapfactor=f))\n",
    "            for s in (1, 2)] for f in factors}\n",
    "for i, f in enumerate(factors):\n",
    "    plt.semilogy([i] * 2, scan[f], \"o\")\n",
    "plt.axhline(mu_bcp, color=\"r\", label=\"morphology (1000 molecules)\")\n",
    "plt.xticks(range(len(factors)), [f\"{f}\\n({int(f * 8)} traps)\" for f in factors])\n",
    "plt.xlabel(\"trapfactor (hole traps in 8000 sites)\")\n",
    "plt.ylabel(r\"$\\mu$ (cm$^2$/Vs)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc376e4d",
   "metadata": {},
   "source": [
    "The mobility is set by the trap density over many orders of magnitude. A prediction for BCP therefore needs the trap density from a larger or independent source (more morphologies, or the chemistry of the trap); the model makes this dependence explicit instead of hiding it in one box.\n",
    "\n",
    "## Many small boxes are not one large box\n",
    "\n",
    "With such broad distributions it is tempting to average over many small boxes. We generate 24 boxes of 1000 sites and 4 of 8000 sites of the Gaussian BCP model (no traps; two of the large ones are reused from the scan above) and compare averages of groups of 8 small boxes with the large boxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b571c656",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T21:07:09.861667Z",
     "iopub.status.busy": "2026-11-10T21:07:09.861241Z",
     "iopub.status.idle": "2026-11-10T21:08:02.437023Z",
     "shell.execute_reply": "2026-11-10T21:08:02.436430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log10 mobility (cm^2/Vs), mean +- standard deviation\n",
      "  single boxes of 1000 sites    -7.93 +- 1.18\n",
      "  boxes of 8000 sites           -8.67 +- 0.65\n",
      "  arithmetic          of 8 small  -7.12 +- 0.40\n",
      "  geometric           of 8 small  -7.93 +- 0.35\n",
      "  harmonic            of 8 small  -9.21 +- 0.74\n",
      "  Boltzmann-weighted  of 8 small  -8.83 +- 0.66\n"
     ]
    }
   ],
   "source": [
    "small, lnZ = [], []\n",
    "for s in range(1, 25):\n",
    "    state = generate(\"BCP_network.xml\", f\"model_bcp_small_{s}\", seed=100 + s, trapfactor=0, energyfile=True)\n",
    "    small.append(mobility(state))\n",
    "    x = -np.loadtxt(f\"model_bcp_small_{s}_E.txt\")[:, 4] / kT      # hole energies\n",
    "    lnZ.append(x.max() + np.log(np.exp(x - x.max()).sum()))         # ln of the Boltzmann sum\n",
    "small, lnZ = np.array(small), np.array(lnZ)\n",
    "large = scan[0] + [mobility(generate(\"BCP_network.xml\", f\"model_bcp8k_0_{s}\", sites=8000, seed=s, trapfactor=0))\n",
    "                   for s in (3, 4)]\n",
    "\n",
    "rng = np.random.default_rng(1)\n",
    "means = {\"arithmetic\": [], \"geometric\": [], \"harmonic\": [], \"Boltzmann-weighted\": []}\n",
    "for _ in range(5000):\n",
    "    k = rng.choice(len(small), 8, replace=False)\n",
    "    w = np.exp(lnZ[k] - lnZ[k].max())\n",
    "    means[\"arithmetic\"].append(np.log10(small[k].mean()))\n",
    "    means[\"geometric\"].append(np.log10(small[k]).mean())\n",
    "    means[\"harmonic\"].append(np.log10(8 / np.sum(1 / small[k])))\n",
    "    means[\"Boltzmann-weighted\"].append(np.log10((w * small[k]).sum() / w.sum()))\n",
    "print(\"log10 mobility (cm^2/Vs), mean +- standard deviation\")\n",
    "print(f\"  single boxes of 1000 sites   {np.log10(small).mean():6.2f} +- {np.log10(small).std():.2f}\")\n",
    "print(f\"  boxes of 8000 sites          {np.log10(large).mean():6.2f} +- {np.log10(large).std():.2f}\")\n",
    "for key, v in means.items():\n",
    "    print(f\"  {key:19s} of 8 small {np.mean(v):6.2f} +- {np.std(v):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a733b3ee",
   "metadata": {},
   "source": [
    "The mobilities of the small boxes spread over orders of magnitude, and their plain means do not agree with the large boxes: the arithmetic mean is dominated by rare fast boxes, the harmonic mean by rare slow ones, and the geometric mean describes a typical box, which is not what a large system does.\n",
    "\n",
    "The reason is the equilibrium of a single carrier. In a large system made of sub-boxes $k$ the carrier is found in sub-box $k$ with the probability $P_k = Z_k/\\sum_l Z_l$, with the Boltzmann sum $Z_k=\\sum_{A\\in k}e^{-E_A/k_BT}$, and the drift velocity is $\\mathbf v=\\sum_k P_k\\mathbf v_k$. If the sub-boxes behave as isolated boxes, the mobility of the large system is the Boltzmann-weighted mean $\\sum_k Z_k\\mu_k/\\sum_k Z_k$, which agrees with the boxes of 8000 sites much better. It is still an approximation: carriers also leave a deep region across the boundaries of a sub-box, which isolated boxes do not have.\n",
    "\n",
    "For a Gaussian DOS, $Z/N$ fluctuates from box to box unless $N\\gg e^{(\\sigma/k_BT)^2}$. Below that, $Z$ is dominated by the deepest site of the box, and so is the mobility. Many realizations of small boxes are therefore, at best, one larger box in pieces: they do not converge to the mobility of a large system. Converged values need large enough networks, or an extrapolation in size or temperature (Kordt et al., J. Chem. Theory Comput. 10, 2508 (2014)).\n",
    "\n",
    "## Summary\n",
    "\n",
    "* `networkfit` turns a morphology into a stochastic network model; check the validation table, the fitted functions and the transport of model boxes of the same size.\n",
    "* `networkgen` generates networks of any size; follow the mobility with size until it stops changing, using several realizations per size.\n",
    "* For broad DOS, finite boxes are dominated by their deepest sites; plain averages over many small boxes are biased.\n",
    "* Traps found in the morphology are inserted as templates; their density is uncertain and should be scanned with `trapfactor`."
   ]
  }
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