{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e82b31f1",
   "metadata": {},
   "source": [
    "# Training ReaxFF-nn on rMD17 (malonaldehyde)\n",
    "\n",
    "**ReaxFF** (van Duin *et al.*, *J. Phys. Chem. A* 105, 9396, 2001) is the\n",
    "bond-order *reactive* force field: every valence term is written in terms of\n",
    "distance-derived bond orders, so bonds can break and form smoothly, and\n",
    "partial charges are re-equilibrated at every geometry (EEM). **ReaxFF-nn**\n",
    "(Guo *et al.*, *Comput. Mater. Sci.* 172, 109393, 2020; Xue *et al.*, *PCCP*\n",
    "23, 19457, 2021) replaces the closed-form bond-order correction (and,\n",
    "optionally, the bond energy) with small per-species / per-bond neural\n",
    "networks — turning the force field into a machine-learning potential whose\n",
    "*every* parameter is trainable by gradient descent, while keeping the\n",
    "physically-motivated ReaxFF functional form for everything else (angles,\n",
    "torsions, EEM electrostatics, van der Waals, hydrogen bonds).\n",
    "\n",
    "This notebook does what the `ffnn` family exists for: it **trains a force\n",
    "field**. Starting from a generic, untrained seed library\n",
    "(`xnn.ffnn.models.template_library` — plausible radii, valences and EEM\n",
    "values, randomly initialized networks), it fits energies **and forces** of\n",
    "the rMD17 *malonaldehyde* trajectory (PBE/def2-SVP). The sampled conformer is\n",
    "the dialdehyde form, O=CH–CH₂–CH=O — a small conjugated molecule whose\n",
    "chemically distinct bonds (C=O double bonds, C–C single bonds, aldehyde and\n",
    "methylene C–H) are exactly what a bond-order force field has to resolve. Two\n",
    "kinds of parameters are trained at once:\n",
    "\n",
    "* the **message-passing bond-order network** `fm` (the ReaxFF-nn core, one\n",
    "  small all-sigmoid network per element), with the bond energy kept in its\n",
    "  classical Morse-like form (`EnergyFunction = 0`) so that the dissociation\n",
    "  limit stays tied to the physical `Desi` parameters — the fully-neural bond\n",
    "  energy (`EnergyFunction = 1`) is available via\n",
    "  `template_library(..., energy_function=1)`;\n",
    "* a set of **classical parameter groups** (bond dissociation energies,\n",
    "  valence-angle and torsion prefactors, ...) refit by backprop — the other\n",
    "  headline of this family. Exponent-type parameters (bond-order decay rates,\n",
    "  the `V2` torsion envelope, vdW steepness) are deliberately kept frozen:\n",
    "  they control the *asymptotics* of the force field, which equilibrium-only\n",
    "  training data cannot constrain.\n",
    "\n",
    "The trained model is exported back to a portable `ffield.json` library, which\n",
    "the companion notebook `reaxff_md_bond_orders.ipynb` uses for molecular\n",
    "dynamics, bond-order and charge analysis, and a bond-dissociation study. Both\n",
    "notebooks also compare against the **original classical ReaxFF** with\n",
    "published parameters — the same `ReaxFF` class runs a standard `ffield` text\n",
    "file directly, networks and training entirely optional."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c940bd12",
   "metadata": {},
   "source": [
    "## 0. Setup and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1deba3f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T19:22:02.705658Z",
     "iopub.status.busy": "2026-09-15T19:22:02.705546Z",
     "iopub.status.idle": "2026-09-15T19:22:05.807081Z",
     "shell.execute_reply": "2026-09-15T19:22:05.806412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ba2e02a9f5344988a36e2cab5a486c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:malonaldehyde train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dfed3b561d640129cb376c662aa8a63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:malonaldehyde test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800 structures of 9 atoms; energy spread 189 meV (std), range 1338 meV\n"
     ]
    }
   ],
   "source": [
    "import io, os, re, time, warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import contextlib\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "torch.manual_seed(0)\n",
    "rng = np.random.default_rng(0)\n",
    "\n",
    "from xnn.common.data import load_dataset, AtomicDataset, collate\n",
    "\n",
    "# rMD17 malonaldehyde: C3H4O2, 9 atoms, 100k configurations sampled at 500 K,\n",
    "# recomputed at PBE/def2-SVP. A modest subsample keeps this notebook to a few\n",
    "# minutes of CPU time.\n",
    "structures = load_dataset(\"rmd17\", molecule=\"malonaldehyde\", split=\"train\")\n",
    "idx = rng.permutation(len(structures))[:800]\n",
    "subset = [structures[i] for i in idx]\n",
    "n_train, n_val = 640, 80\n",
    "\n",
    "energies = np.array([s[\"energy\"] for s in subset])\n",
    "print(f\"{len(subset)} structures of {len(subset[0]['atomic_numbers'])} atoms; \"\n",
    "      f\"energy spread {1000 * energies.std():.0f} meV (std), \"\n",
    "      f\"range {1000 * np.ptp(energies):.0f} meV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ba1d82b",
   "metadata": {},
   "source": [
    "## 1. The seed force field\n",
    "\n",
    "`template_library` builds a complete-but-untrained ReaxFF-nn parameter\n",
    "library. Its bond-order exponentials are anchored so that BO(rₑ) ≈ 0.85 at\n",
    "the covalent bond length and BO ≈ 10⁻⁴ at the neighbor-list cutoff — bond\n",
    "orders (and with them every valence term) then vanish smoothly at the list\n",
    "boundaries, which is what keeps molecular dynamics conservative. It is a\n",
    "*starting point*, not a validated force field. The only calibration done by\n",
    "hand is the per-element reference energies (`atomic`), initialized so the\n",
    "seed model's mean energy matches the data (rMD17 stores absolute DFT\n",
    "energies); they stay trainable so the optimizer can refine the offset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd950b51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T19:22:05.808771Z",
     "iopub.status.busy": "2026-09-15T19:22:05.808525Z",
     "iopub.status.idle": "2026-09-15T19:22:06.030549Z",
     "shell.execute_reply": "2026-09-15T19:22:06.030056Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed library written to runs/reaxff_rmd17/ffield_seed.json (atomic-energy shift +806.4 eV/atom)\n"
     ]
    }
   ],
   "source": [
    "from xnn.ffnn.models import template_library, ReaxFF\n",
    "\n",
    "lib = template_library([\"C\", \"H\", \"O\"], nn=True, seed=0, energy_function=0)\n",
    "seed_model = ReaxFF(lib, trainable=(\"atomic\",))\n",
    "\n",
    "calib = collate([AtomicDataset(subset[:32], cutoff=seed_model.cutoff)[i]\n",
    "                 for i in range(32)])\n",
    "with torch.no_grad():\n",
    "    shift = (seed_model(calib)[\"energy\"].mean() - calib.energy.mean()) / 9\n",
    "    seed_model.params[\"atomic\"] += shift\n",
    "\n",
    "os.makedirs(\"runs/reaxff_rmd17\", exist_ok=True)\n",
    "seed_path = \"runs/reaxff_rmd17/ffield_seed.json\"\n",
    "seed_model.export_library().save(seed_path)\n",
    "print(f\"seed library written to {seed_path} \"\n",
    "      f\"(atomic-energy shift {float(shift):+.1f} eV/atom)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5e4537",
   "metadata": {},
   "source": [
    "## 2. Train through the standard `xnn` pipeline\n",
    "\n",
    "The model registers as `\"reaxff\"`, so the usual config → `Trainer` route\n",
    "applies. `trainable` lists the classical parameter groups the optimizer may\n",
    "refit alongside the (always-trainable) ReaxFF-nn networks. The loss is the\n",
    "standard per-atom energy + force objective; forces differentiate the whole\n",
    "functional form (EEM solve included), which is what makes classical-parameter\n",
    "refitting by backprop work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af4d0445",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T19:22:06.031798Z",
     "iopub.status.busy": "2026-09-15T19:22:06.031686Z",
     "iopub.status.idle": "2026-09-15T19:31:29.662128Z",
     "shell.execute_reply": "2026-09-15T19:31:29.661502Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trained 300 epochs in 563 s\n",
      "final: {'train': {'energy_mse': 0.00014, 'force_mse': 0.25271, 'loss': 0.07595}, 'val': {'energy_mse': 0.0001, 'force_mse': 0.21473, 'loss': 0.06452}, 'test': {'energy_mse': 0.00013, 'force_mse': 0.21937, 'loss': 0.06594}}\n"
     ]
    }
   ],
   "source": [
    "from xnn.common.config import from_dict\n",
    "from xnn.common.train import Trainer\n",
    "\n",
    "cfg = from_dict({\n",
    "    \"model\": {\n",
    "        \"name\": \"reaxff\",\n",
    "        \"cutoff\": 10.0,                      # nonbonded/EEM cutoff (standard)\n",
    "        \"ffield\": seed_path,\n",
    "        \"trainable\": [\"atomic\", \"Desi\", \"Depi\", \"Depp\", \"ovun1\",\n",
    "                      \"ang_val1\", \"ang_val2\", \"ang_theta0\",\n",
    "                      \"tor_V1\", \"tor_V2\", \"tor_V3\"],\n",
    "    },\n",
    "    \"data\": {\"cutoff\": 10.0, \"batch_size\": 16},\n",
    "    \"optim\": {\"lr\": 5e-3, \"epochs\": 300, \"energy_weight\": 1.0,\n",
    "              \"force_weight\": 0.3, \"scheduler\": \"cosine\"},\n",
    "    \"device\": \"cpu\",\n",
    "    \"output_dir\": \"runs/reaxff_rmd17\",\n",
    "})\n",
    "\n",
    "train_set = AtomicDataset(subset[:n_train], cutoff=cfg.model.cutoff)\n",
    "val_set = AtomicDataset(subset[n_train:n_train + n_val],\n",
    "                        cutoff=cfg.model.cutoff)\n",
    "test_set = AtomicDataset(subset[n_train + n_val:], cutoff=cfg.model.cutoff)\n",
    "\n",
    "trainer = Trainer(cfg, train_set, val_set, test_set)\n",
    "t0 = time.time()\n",
    "log = io.StringIO()\n",
    "with contextlib.redirect_stdout(log):\n",
    "    history = trainer.fit()\n",
    "print(f\"trained {cfg.optim.epochs} epochs in {time.time() - t0:.0f} s\")\n",
    "print(\"final:\", {k: {m: round(float(x), 5) for m, x in v.items()}\n",
    "                 for k, v in history.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fe4d909",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T19:31:29.663626Z",
     "iopub.status.busy": "2026-09-15T19:31:29.663499Z",
     "iopub.status.idle": "2026-09-15T19:31:30.102481Z",
     "shell.execute_reply": "2026-09-15T19:31:30.101840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r88QTT9xQQEIIIYSoPWxatEuj0XB59Su3r1ZWm8mjDSGEEPWNLd99Mo9ENcoqKOWT345RYjTzxtBWzg5HCCGEuGGyaFc1MpjNfPr7ceZvOYnZXHdabYQQQtRfkkhUIz8PVwAUBfJKjE6ORgghRGUUFxezf/9+TCaTs0OpkWx+tDF37txrbour07vocHfVUmwwk1tksCQWQgghHKe4uJijR4/SsmXL665cWRkHDx6kQ4cOnD9/nqCgIAdEWLfYlEjodDqrURlXbpeViavz93DjjKGY7EIDkQHOjkYIIeqe/fv3Ex8fT1ZWFv7+/jd8Pg8PD1q1aoWLi3QrrIhNd8VolOb4G+Xn4cqZ3GJyigzODkUIIeocg8HAsWPHAEhKSsLHxwd/f38CAgI4fvw4rVq1oqCggJSUFCIjI9Hr9Rw8eBAAV1dXoqOjcXd3tzpndHQ0S5YsscyRVFhYaDmXyWQiJSWF8PBw3NzcqvfN1hDSR6Ka+XmqjzOyi0qdHIkQQtQ958+fZ8qUKQCMHz+eUaNG8dFHH7Fr1y7atGnDiy++SGRkJCNGjCAxMZGcnBxGjRrFqFGjGDJkCP7+/owfPx6D4dIvewcPHqRNmzZkZWUBWM718ssvExYWRo8ePQgICGDZsmVOec/OZlOLhKIonDhxgpiYGEvZ0aNHmTZtGllZWQwbNowxY8Y4PMgbkZ+fz+7duwF1ldLo6GinxlPWL0JaJIQQtZGiKBQZqr/ToYerrlJLWoeHh7NkyRLi4+P5888/LY82Nm/eDMCRI0dIT0+3anXYv3+/5ee0tDR69+7Nxx9/zKRJk655rdOnT5OamoqbmxvTpk3j8ccfZ9iwYbi61q/+bzYlEt9//z0LFy5k+fLlgLqmRq9evTCZTDRr1ozx48cD1KhkIjU1lRdffJG0tDTuvffecrNzVjf/i4lEdqEkEkKI2qfIYKLla2ur/bqJb92Jp9uN91F46623yj26ALWDZmpqKkVFRfTu3ZsNGzZcN5H45z//aXmc8cADD/Dyyy9z+vRpYmNjbzjO2sSmv5UZM2bw5ptvWrb/97//kZ+fz5EjRwgODubTTz9l1qxZNSqRaNasGZs3b+Y///kPZ86ccXY4lhaJXGmREEKIatekSROr7ZKSEiZMmMDSpUsJCQnB19eXCxcuEBYWdt1zhYeHW3728vIC1F+w6xubEoldu3bRoUMHy/bGjRu58847CQ4OBmDkyJG8/PLLDg2wtLSU7777joMHDzJ27FgaN25crk56ejrff/89eXl5dO/ene7duzs0BkfykxYJIUQt5uGqI/GtO51yXUe4cmThjBkz+PPPPzlx4oQlMXjttddYsWKFQ65XH9jU2dLFxYXc3FzL9pYtW+jatavVfkdO2LFw4UJiYmKYP38+b775JidPnixXZ9u2bTRr1oyffvqJEydOMGjQIF588UWHxeBo/p7SR0IIUXtpNBo83Vyq/VWZ/hFlyh5dXN5h8moOHjxI165drVoXfv75Z9tvTD1mUyLRqVMnpk2bhsFgYPXq1Rw8eJB+/fpZ9icmJtKqlePWkIiNjWXXrl3MmTPnqnWeeOIJBg8ezMqVK5k9ezaLFi3i3//+N3v27HFYHI7k6yGjNoQQoipFR0fj4eHB/Pnz2bdvHykpKVet2717d3788UeWLl3KH3/8waOPPsr27durMdraz6ZE4s033+Srr77C3d2dgQMHMmrUKKvEYd68eYwdO9ZhwXXt2pWGDRtedf/x48fZvXs348aNs5QNGDCA8PBwvvvuOwBMJhObN2/mxIkTpKSksHnzZoqLi696zpKSEnJzc61ejuTvqXbMySmSOTmEEKIq+Pj4sHDhQtatW8eDDz7IRx99hJeXF61atSo30+XYsWN5/fXXmTlzJs8++ywNGjTgvffeIy4uzlLnygmpKjqXi4sLrVq1wsPDo3reZA1i0zLiAMnJyfzyyy8EBAQwaNAgqxu5cOFCRowYUWGP2BtRNnHIr7/+Sq9evSzlq1evZuDAgZw8edJqWGfPnj0JCwtjyZIl5Ofn079/f6vzLV26lEaNGlV4rTfeeMOqQ2kZRy0jvjs5m2Gz/qCRvwd/vNj7hs8nhBBCOFqVLiMeGRnJww8/XOG+Bx980NbT3ZD8/HwA/Pz8rMr9/f0t+7y9vS3jhyvjpZde4plnnrFs5+bmEhkZ6YBoL8Zm6WwpjzaEEELUfjYlEgsWLKhUvdGjR9sVjK28vb0BtbXg8vnUs7OzKzV0pyJ6vR69Xu+I8CpUNmqjoNSEwWTGVSeTiwohhKi9bEokHnrooUrVq65EolmzZoA6U1nZow1FUTh27JjVI5CaxPeyFT9zigwEeVdd0iKEEEJUNZt+HY6IiCA6Opo33niDU6dOoShKha/qEhMTQ/v27fniiy8sZatXryY1NZXhw4dXWxyVln0a3bePsND9HUCGgAohhKj9bGqROHXqFGvWrGHu3LncdNNN9OzZk/Hjx3PXXXdVyeOA3bt38/3331tGTsybN4+NGzfSq1cvS4vDJ598Qt++fRk0aBBRUVEsXryY559/nnbt2t3QtWfNmsWsWbMcOi8Grp5w4H90B7wokkmphBBC1Ho2j9ooc+7cOebPn8/nn39ORkYGo0eP5j//+Y9DgytLJK50eSIB6iIrl89seeuttzosBlt6rlbK+y0gL43hJW/wfw8/yO3Nrz68VQghhHAGW7777E4kyuzatYsnnniCHTt2VOtjjeri8ERi4b1wZC2vGB6h1V3PcH/nqBs/pxBCCOFAtnz32TVkICsri5kzZ9KhQwduv/12OnbsyLZt2+wKtt4JbQNAS81J9qbkODkYIYQQ4sbY1Edi/fr1fPHFF3z//fd07tyZp59+mnvuuQdPT8+qis9pqqSPBEBoawBaak/xdXK2Y88thBBCVDObHm1oNBqio6MZO3as1fShV6qu4Z/VweGPNi4cgxkdKVZcaWv4kt1vDMDTzeZ5wYQQQlShxMRE+vXrx759+2jQoEG57YqcPHmSW2+9lZ07dxIaGuqwaztDlc1sqdPpSElJYerUqdesV5cSCYdr0ARcvXA3FBClpHEgLZf4xgHOjkoIIcRlSktLSU1NtbRKX7ldEaPRSGpqKkZj5ddS2rt3LwMHDiQxMdHyhV2Za9UkNvWRMBqNlXqJa9BqLY83WmtOskcebwghRI3XqlUrkpOTCQhw7C9+ZUmD2Wyu8mtVFZmf2RnCOwDQTnuMBEkkhBDCYcxmMx06dODrr7+2Kk9OTiYyMpLdu3dz/vx5IiIiiIiIoEmTJvTu3Ztly5Zd87xHjhyha9eu5ORc6iS/Z88e7rzzTpo1a8Zdd93F7t27rY653nVSUlIYNGgQoCYPERERTJw4scJrHTp0iPvuu4+4uDg6duzI9OnTrVosduzYQUREBD///DODBg2iefPmDB06lEOHDtl8D20lD+edISIetn1CB+1RZh3PRFEUNBqNs6MSQojrUxQwFFb/dV09oRL/T2q1Wm655Ra+/PJLq2UdFi1ahIuLC+3atUNRFLZu3QqoLQJ//vkn48aNw9vbmwEDBlR43isfN2RlZdG7d29GjBjBe++9R1JSEo8//rjVMYGBgde8TlhYGF9++SWDBg1i/fr1+Pr64unpyenTp62ulZOTQ48ePejXrx/ffvstp0+fZsKECWRnZzN9+nQASkpKSE1N5dlnn+XDDz8kJCSEKVOmMGLECPbt21el3zGSSFxFlY3aAGh0MwAtNafIzc/n6Ll8mob4OP46QgjhaIZCeDu8+q/7chq4eVWq6oMPPshtt91GamoqjRo1AmDhwoU8+OCDaDQaNBoNERERlvoxMTHs3buXefPmXTWRuNKnn36Kn58fn3zyCVqtljZt2pCcnMzkyZMtdbRa7TWvo9PpaNhQnZQwPDzcsvjk6dOnra41e/Zs3Nzc+OKLL3B1daVdu3a8//77jB8/npdeeslqBexPP/2ULl26ADB16lRat25NWlqa5T5UBXm0cRUTJ04kMTGRHTt2OP7kDRqDZyBuGiOtNCfZcvyC468hhBD11C233ELjxo1ZvHgxAPv27WPfvn1WAwEWLVpEnz59aNq0KREREXz22WecPHmy0tfYs2cP3bp1Q6u99DV62223lat3o9e5/FqurpcWfezVqxclJSXlHl20bt3a8nNISAgAGRkZNl3PVtIi4QwaDTTqBEfW0l57lD+PXmBMt8bOjkoIIa7P1VNtHXDGdW3wwAMPsHDhQiZPnszChQu5+eabad68OQDLly/niSeeYMaMGcTHx+Pr68uMGTNYu3Ztpc9fUlJi1RIA4ObmZrXtiOuUXcvLy7o1pmx9q5KSEqtynU5X7viqnnXaphaJxo0bW207em2NeiWiEwAdtEfZeuICZnPdm15cCFEHaTTqI4bqftn4jH/06NHs3r2b/fv3s3jxYqvWiHXr1jFkyBAefvhhWrZsSUREBKdOnbLp/E2bNmXfvn1WZXv27LHarsx1yr74r/Vl36xZs3LnTkhIsMThbDYlElfegKefftqhwdQrUV0BuEWbSHZhKXtSsp0bjxBC1CHNmjXj5ptvZuLEiaSmpnL//fdb9kVERLBjxw4yMjJQFIXly5ezfPlym84/YcIE9uzZw5w5c1AUhdOnT/Ovf/3Lqk5lrhMervY3udboiscee4zDhw/z3//+F7PZzLlz53jppZe49957b2jiK0eRPhLOEtkFXD0J0uTQXJPMz0nnnB2REELUKaNHj+b333+nT58+lv4CAP/4xz9o3Lgx4eHh+Pn58eabb3LvvffadO64uDi+/PJLXnjhBXx9fYmPj+e+++6zqlOZ64SEhDBp0iR69epFo0aNmDhxYrlrNWnShKVLl/L+++/j6+trGVL68ccf2xRzVbF5iuzLq1+5XZdcPmrj8OHDjpsi+3IL7oGj6/mX4QE2Bd/Pmn/0cOz5hRCiHistLeXcuXP4+vpW+P93fn4+RqMRf39/8vPzKSwstIyiMBgMnD17lvDwcLRabbntMkajkdzcXAICAjCZTKSnpxMWFmbVV+Fa1yljMBg4f/487u7u+Pj4VHgtUDtOenp6llvjquy9Xj5KxGw2k5aWRkhIiFVHzcqosmXENRoNc+bMsWxPmDDBarvMo48+akO4NZvD19q43JZZsPZlfje3YUzpS2x+4XYiGtS9BdCEEELULlWWSLi4VG6QR12aJrtKE4mziTC7G6W40ab4M14Z1pGHukY79hpCCCGEjWz57pO1NpypYQvwDsWNUjppD7H9RKazIxJCCCFsIp0tnUmjgdjbAbhNu5/tJy7U2T4nQggh6iZJJJwt5mIiodvH2dwSTmc6YQ57IYQQwk6SSDhbTC8AWmlOEkCuPN4QQghRq0gi4Ww+IRCizo1+q3a/JBJCCCFqFUkkrmLWrFm0bNmS+Pj4qr/YxVaJW7X7+OXgOUqN5qq/phBCCOEANicSRqORY8eOWZbXzs3NZdGiRSxevJjs7GxHx+c0Vbr655ViewPQ02U/FwpK2JB0tuqvKYQQQjiATYnEgQMHiIqKIi4ujm7dunHy5EnatGnD6NGjeeCBB2jZsqXNC58IIPoW0OkJ4QKxmjQWbz99/WOEEEKIGsCmROKll17illtu4bfffqNx48YMHDiQzp07k52dzYULF2jfvj3//Oc/qyrWusvVw7KI123afWw6kkFadpGTgxJCCCGur3JTVV60ZcsW/vrrL6KiooiJiSEyMpKVK1daZr169913GTJkSJUEWufF3g4nfmOwVxLzcvuz6ch57ouPcnZUQgghxDXZ1CJhMBgICAgAoFGjRgA0btzYsj8mJoYzZ844Lrr6pOmdALQ37MaHQn4/kuHkgIQQQojrsymR6NatG3/99RegLuD1wgsvoNFoLPtPnTpFXFycYyOsLxq2gKBmuCgG+mp38sfRDExmmeVSCCFEzWZTIvHiiy/y/vvvW7anT59utf+rr75iwoQJjomsvtFooPVwAIa5biO70MCBtBwnByWEEEJcm02JRM+ePVmxYsVV999+++088cQTNxxUvdVKTSS6a/biRz6b5PGGEEKIGs6hE1L17dsXV1dXR57Saap1QqoywTdBUDN0mOimTeT3w+er79pCCCGEHRyaSDz55JM8+eSTjjyl01TrhFSXu2yWy12nsygokWXZhRBC1FwOTSTWrFnDmjVrHHnK+udiItHTNRGDSWHbiQvOjUcIIYS4BocmEkePHuXo0aOOPGX907g7aLREKumEcYHfD0s/CSGEEDWXLNpV07j7QXhHAG7V7WPjoXMoigwDFUIIUTPZNLMlgMlkYsOGDfz555+cPXsWRVEIDQ3llltuoU+fPuh0uqqIs36J7Q2pO+nnksCyC73YeSqL+MYBzo5KCCGEKMemRCIlJYXBgwezZ88eGjVqRHBwMBqNhnPnzvHWW2/Rrl07Vq5caZn1UtipxRD4/d/00u7Bk2KWbE+WREIIIUSNZNOjjYkTJxIUFMThw4dJSUkhISGBXbt2kZKSwqFDhwgODmbixIlVFWv9EdoGGjTBVSmll3Y3K/elkVtscHZUQgghRDk2JRIbNmxg3rx5NG3atNy+m266iS+//JINGzY4LLh6S6OBlkMBGOn5F8UGM78ePOfkoIQQQojybEok9Ho9OTlXn7Y5JycHvV5/w0EJoMVdANxiTsAVoyQSQgghaiSbEomRI0cyfPhwvv32WzIzM1EUBUVRyMzM5LvvvuPuu+9m5MiRVRVr/RLeAbyC0ZsLuVl7mI2Hz8siXkIIIWocmxKJDz74gPj4eEaOHElgYCBubm64uroSGBjIPffcQ5cuXawW9RI3QKuF2DsAuNNtH9mFBhJOZzk5KCGEEMKaRrFjkoK0tDS2bt3KmTNnAAgNDaVbt26EhYU5PEBnmTVrFrNmzcJkMnH48GFycnLw9fWt3iD2LoPvHiXVLYbuuVP5e69Ynu/fvHpjEEIIUe/k5ubi5+dXqe8+uxKJ+sSWm+lwBRfg3VhAoWvxDPxDG7PmHz2qNwYhhBD1ji3ffTKzZU3mFQgR6uqjd+p2cvBMHmnZRU4OSgghhLhEEomartUwAO7z/AuAXw/J6A0hhBA1hyQSNV1LdRhoC8MBGpLFL0mSSAghhKg5JJGo6fwiILILGhQG6rax+WgG2YWlzo5KCCGEACSRqB0utkoM89hNidHMsp0pTg5ICCGEUN1wIhEREeGIOMS13NQfgLamRLwpZMG2U5hlciohhBA1wA0nEqmpqY6IQ1xLYCwENkWrGOnnnsipC4X8eeyCs6MSQggh5NFGrXHTnQA82CAJgDUH0p0ZjRBCCAHYkUhkZGRYva5WJhzs4uONNgVbccPA+sSz8nhDCCGE07nYekBwcPB1y2SyzCoQ1Q18wnDLS+cut79YltuVvak5tI/0d3ZkQggh6jGbWySysrKsXlcrEw6mc4GbHwHgCc9fAFiz/4wzIxJCCCFsTyT8/f2tXlcrE1Wg4xjQuhBbvJ/mmtMs2naKnEKDs6MSQghRj0lny9rENwyaqp0uR/vuIbfYyMe/HXVyUEIIIeqzG04koqOjHRGHqKzmAwEY6rkPgHl/nJRWCSGEEE5zw4nEyZMnHRCGqLSm/QDwzdrPLQ1LKTGaWbE3zclBCSGEqK/k0cZVzJo1i5YtWxIfH+/sUKx5N4RGNwPwt/BjACzfmezMiIQQQtRjkkhcxcSJE0lMTGTHjh3ODqW8mwYA0KV4Ey5aDXtScjh8Ns/JQQkhhKiPJJGojdrcA2hwO7mRe2PU/hE/7JapyoUQQlQ/SSRqo4AmENcHgHHuvwKwcm+6TAQmhBCi2kkiUVvFPwpAbMr/8HUxcvJCIQfScp0clBBCiPrGpkRi+vTpVtsbN24sV+eJJ564oYBEJTXtC35RaIqzeTZ8PwAr98lCXkIIIaqXRrGhPVyj0Vg1n1+5fbWy2iw3Nxc/Pz9ycnLw9fV1djjWNn0AP79JdoM2tE9/CR+9Cxue7UmIr7uzIxNCCFGL2fLdJ482arMOD4HODf+sfdwTeo68EiOv/3DA2VEJIYSoRySRqM28g6HFUABebrQbF62GNQfOsCc527lxCSGEqDckkajt2o0CIODETwxtoy7nvmDrKWdGJIQQoh5xsfWAuXPnXnNbVLOYXuAZCIUZPBGVwnd73PhxbxqvDGqJn6ers6MTQghRx9nU2dLFpXJ5h9FotDugmqZGd7Yss/JZ2DEXpc1IBiSP4eCZPF7o35y/9Yp1dmRCCCFqoSrrbGk0Giv1EtWs/QMAaPZ/x6SOarL36e/HyCuWVUGFEEJULekjURc0uhli7wDFxJ0XFhAb7EV2oYHPN59wdmRCCCHqOJsSCUVROH78uFXZ0aNHGT9+PMOHD+err75yaHDCBre/DIB27xKm3OIFwNxNJ8gqKHVmVEIIIeo4mxKJ77//nueff96ynZeXR69evVi1ahWZmZmMHz9ekglniegEsb1BMXF71je0DPMlv8TIJ78dc3ZkQggh6jCbEokZM2YwadIky/b//vc/8vPz2bt3Lxs3bmTmzJnMmjXL4UGKSuqu/t1odn3Ny73UoaDz/jzJ2dxiZ0YlhBCiDrMpkdi1axcdOnSwbG/cuJE777yT4GD1S2vkyJEcPXrUsRGKymvSE8LagbGI7mcXcnN0A0qMZmb+In8nQgghqoZNiYSLiwu5uZdWmNyyZQtdu3a12m8ymRwXnbCNRgO91L4Smq2zeaWLOo/E4u2nSc4sdGZkQggh6iibEolOnToxbdo0DAYDq1ev5uDBg/Tr18+yPzExkVatWjk8SGGDm+6EuL5gNtAh6V1ujQvCaFb418okZ0cmhBCiDrIpkXjzzTf56quvcHd3Z+DAgYwaNcoqcZg3bx5jx451dIzCFhoN9J8OGi0cWcs/O5da1uBYs/+Ms6MTQghRx9iUSHTp0oX9+/fzxRdfsGLFChYuXGi1/7bbbuOhhx5yaIDCDkFx0GYkAE0SP+HxnjEAvPbDfnKKZJIqIYQQjmPTFNm12fHjx/Hy8iIkJMSm42rFFNkVOX8IZnUBFErGrmPA8iKOZxRwf+dIpg1v6+zohBBC1GC2fPfZtGjXggULKlVv9OjRtpy2ShkMBoYMGUJSUhL5+fk89dRTvP76684Oq+oFN1NXBt2zGP3Kp5h+1/+49/MEFm9PZmi7RnSLDXR2hEIIIeoAm1okNBpNperVpEaOxYsXM2fOHDZs2EB2djatW7dm69atREVFVer4WtsiAVCYqbZKFJyD7pOYkj+ShdtO0zjQkzX/6IG7q87ZEQohhKiBqmzRroiICKKjo3njjTc4deoUiqJU+HIkg8HA8uXLmTp1KqdOnaqwztmzZ5kzZw4ffPAB27Zts9q3fft2hg0bhlarJSAggJ49e7Jz506HxlhjeQbA4A/Vn//4iJebnyHEV8/JC4V8uP6wc2MTQghRJ9iUSJw6dYqPP/6YPXv2cNNNN3HnnXfyzTffUFJSUiXBLV68mNjYWD777DNeffVVTpwovwjVzp07adasGcuXLycpKYm+ffsyZcoUy/68vDy8vb0t297e3uTl5VVJvDVSi8HQaRwAXiufZPrAxgB8tuk4209kOjEwIYQQdYFNiYRWq2XgwIF89913nD59mj59+vDaa6/RqFEj/vGPfzg8uKioKLZt28YXX3xx1TqPP/44/fv3Z+3atcyZM4dFixYxbdo09u3bB0CjRo2sFho7duwYERERDo+1RrvzbQiIhfwz3J46m5E3R6Ao8NTiBBLTcq9/vBBCCHEVdi8j3rBhQ5577jkWLVpETEwMH330kSPjAqB79+6EhYVddf+JEyfYtWsX48ePt5QNGjSIsLAwvv32W0CdtnvOnDksX76cjz76iKNHj3Lbbbdd9ZwlJSXk5uZavWo9V49Ljzh2fM4bHQuIDfbiTG4xI2b/yTtrDsqwUCGEEHaxK5HIyspi5syZdOjQgdtvv52OHTuW65tQHQ4ePAjATTfdZCnTaDTExcVZ9rVu3Zq5c+cyf/58tm7dypo1a3Bzc7vqOadNm4afn5/lFRkZWbVvorrE9IR2DwAKXmsn891jnbmtaRBFBhOzNx5jwlc7a1QnWSGEELWDTYnE+vXruf/++wkPD2fZsmU8/fTTpKen88knn9C5c+eqivGq8vPzAfDz87Mq9/f3t+wDGDJkCD/++COLFy+mZcuW1zznSy+9RE5OjuWVnJzs+MCdpd9U8AiAcwfwS5jF/Ec689lDN+PhqmP7iUz+l5Dq7AiFEELUMjbNI9GvXz+io6N54YUXiIuLA+C7774rV6+65pHw8vIC1GEq/v7+lvKcnBybJ54qo9fr0ev1jgiv5vEKhP7T4H+Pw6/T0DbpSb9WnXnqjqa8s+Yg/1qZRLtIf2KDva9/LiGEEAIbEwmdTkdKSgpTp069Zr3qSiSaNWsGwNGjR63mhTh27Bg9evSolhhqnbb3wZH1sH85LL4fBr3P+FuH8uOeNBLTc7nv060smtCFm0J8nB2pEEKIWsCmRxtGo7FSr+oSGxtL27Zt+fLLLy1la9euJSUlhbvvvvuGzj1r1ixatmxJfHz8jYZZs2g0asfLkNZQmAHLHsZt41t8PS6eFmG+ZOSXMOqzrexJznZ2pEIIIWqBGr3Wxt69e1mxYgW5ubm8++67jBs3jiZNmtCjRw9Li8OWLVvo168fvXv3JioqigULFjBu3Djef/99h8RQq2e2vBZDMfw2HTZfHM0Rczt5nSfx4AZX9qbkoNHAPR0jeOuu1ni4yQyYQghRn9jy3VejE4mEhATLMM7L9e7dm969e1u2U1JS+O6778jLy6N79+706tXLYTHU2USizI7PYdVkUMyg0VLw4I88v92TlXvTAWgf6c8XY+MJ8Lr6SBchhBB1S51JJGqCOp9IAFw4BmunwOHV4B8Fj/7C1nNaHv/6L3KKDDQJ8mL+I52JCvR0dqRCCCGqQZWttSHqqMBYGP6ZmkRkn4YZHemavZJv/9aNRv4enMgoYMjMzZZWCiGEEKKMJBJXUWc7W16Nuy/cvwTC2kFJLqz4P+KOfMm3T3SjbYQfOUUGJi7axZs/HsBgMjs7WiGEEDWETYnE9OnTrbY3btxYrs4TTzxxQwHVFBMnTiQxMZEdO3Y4O5TqE9IKJmyEW59Rt9e/Sui6x/n2gSj+3isWgC//OMnoudvIyK+ahdqEEELULjb1kdBoNFbTKF+5fbWy2qxe9JG4kqLAnzPg5zfBbAStC7R/kF8inuCpH5LJLzES5ufOrAc70jGqgbOjFUII4WDSR0LcGI0Guj8F49dD9K1qMrFrPr3X9OPPFt9yS2A+6TnF3PfpFr7acrJOJY5CCCFsI4mEuLpGHeGRlTBuLYS2gdI8fA8uZaHxOSbHnMJgUnjthwP8Y+luCkurbyIyIYQQNYckEuL6orrCY7/D2FUQEY+mJIeJaS/zbcvNuGoVftidxrBZf5CcWejsSIUQQlQzm9baAJg7d+41t+uKWbNmMWvWLEwmk7NDqRm0WmjcHcauhDUvotn5BTcf/5hdEdt5OHMsu87CXbP+4NOHbia+cYCzoxVCCFFNbOps6eJSubyjOtfbqGr1srNlZSQsgNUvQGk+iosHn7qNYXpmD9x0Oj68rz2D2oY5O0IhhBB2qrLOljVt0S7hRB1Gw9/+hCY90RiLeKLwU/4XOBtXUwGTl+3h+Pl8Z0cohBCiGkgfCWG/BtEw5gcY8G/QudGhYDMrfN5Bb8jm6aW7MZllNIcQQtR1kkiIG6PRQJfH4ZE14BFArOEwX7h/wL6ULH5OOuvs6IQQQlQxSSSEY0TcDI+sAjcfOnKIx3Q/8eUfJ50dlRBCiComicRV1Lu1NhyhYQsY8A4Az7gs4/TxgySl5zo5KCGEEFVJEomrqJdrbThC+weg8W24aUxMcFnJ97tTnR2REEKIKnRDiURxcTFnzpwp9xL1mEYDPSYDMEr3KwcOH3NyQEIIIaqSXYnE3r17iY+Px9PTk7CwsHIvUc816YkhpD3uGgMtz68kp9Dg7IiEEEJUEZtntgR45JFHaNasGR9++CH+/v4ODknUehoNrq3vgrO7aa05wZbjF+jfOtTZUQkhhKgCdiUSiYmJ/PrrrzLTo7i60DYAtNCcZsGxDEkkhBCijrLr0UZsbCwpKSmOjkXUJSGtAYjRpLHrWLqTgxFCCFFV7GqRmDZtGmPHjuWtt94iNjYWjUZjtT8uLs4hwYlazCcUs0cguqILaDMOUlTaGw83nbOjEkII4WB2JRI6nY6kpCQGDBhQ4X4b1gGrsWT1zxuk0aAJbQ0nfqOZ5hQHz+TSIaqBs6MSQgjhYDat/lkmLi6OXr168eSTT1bY2bJx48YOCK1mkNU/b8DaKbBlJl8a78R18LuM7hrt7IiEEEJUgi3ffXa1SKSlpfHhhx/i4+NjV4CinghpBUAL7WlWyAyXQghRJ9nV2bJ58+acOHHC0bGIuuZih8sWmlMcSM1xcjBCCCGqgl0tEiNHjuT+++9n6tSpxMXFlets2bp1a4cEJ2q54GYoWhf8zIXknDmB0XQLLrqLuauiQOZxaNAYtNIJUwghaiu7EomXX34ZgOHDh1e4vy50thQO4KKHoJvgXCKx5hMcO19As9CLj8MO/gRLR8Ntk+GOV50bpxBCCLvZlUikp8u8AKJyNKFt4FwiLTSn2ZOcfSmROHtA/fP8QecFJ4QQ4obZlUiEhsoshaKSQloDS2mhPcXm5GzujY9Uy4uy1D9LpBOmEELUZnYlEmV27txJUlISiqLQsmVLOnXq5Ki4RF0RWtbh8jQzk7MvlZclEsWSSAghRG1mVyJx/vx5Ro0axS+//IK3tzcA+fn59O7dmyVLlhAcHOzQIJ1BJqRykIsjNxprznL6zDkKS414urlAYaa6vyTPicEJIYS4UXYN/5w0aRJFRUXs27ePvLw88vLy2LdvH4WFhUyaNMnRMTrFxIkTSUxMZMeOHc4OpXbzbgjeIWg1Ci04yb6Ui8NA5dGGEELUCXYlEitXruTrr7+2GubZunVrFixYwKpVqxwWnKgjIrsA0Fl7kISyxxuWREJaJIQQojazK5EwGAyWRxqX8/LyorS09IaDEnVM41sB6KpN4o+jGWpZ0cVHG8ZiMMpnRgghaiu7EokePXrwzDPPkJd36bfJ3Nxc/vGPf9CjRw+HBSfqiOjuANysPczO42fJKyqBouxL+6VVQgghai27Olt+9NFHDBgwgPDwcFq0aAFAUlISwcHBrF692qEBijqgYUvwaIBXURbNzCfYmniCvlw2aVlJDngFOi8+IYQQdrMrkWjWrBlJSUksW7aMAwcOoNFoeOqppxg5ciR6vd7RMYraTquFqFvg0Eq6ahPZkdSUvpfvlxYJIYSotexKJDp16sTOnTsZPXr0VfcJYSWmJxxaye263cw63s56n8wlIYQQtZZdfST++uuvCstNJhO7d+++kXhEXdVsAACdtIcIKE623ictEkIIUWvZ1CJxeUvDla0OZrOZLVu2EBUV5ZjIRN3iHwUhbdCd3cdw3SarXSUFWcgDMSGEqJ1sSiTi4+Mr/LlMUFAQM2bMuPGoRN3UfCCc3cet2v1WxZ+s3c3/dXgArVZzlQOFEELUVDYlEllZ6iRCQUFBZGRkWO1zdXXFy8vLcZGJuqfZQPjtHbQa62XmSwqyOZNbTLi/h5MCE0IIYS+bEgl/f38AjEZjVcRSo8haG1UgrB34RkBuilWxj6aIY+fzJZEQQohayK7OlvWBrLVRBTQaS6dLAFzVFiwfCjl6Lt9JQQkhhLgRdiUSJSUl/POf/6Rt27b4+/vj7e1t9RLiqpoPvPRzg2gAvC+2SAghhKh97EokXn/9dZYsWcIzzzxDTk4Oc+fO5bHHHsNkMtWZ1T9FFYm+FfS+6s/+aiLhQxHHzhU4MSghhBD2siuRWLJkCQsXLmTs2LEA3HfffXzwwQd88cUXbNq06doHi/rNxQ36vA4xt0OLwYDaInH0YotEcmYhx6V1Qgghag27Eonk5GTatGkDqCt+5uTkADBkyBDpUyCuL/5RGPM9+IQB4Esh5/NKOJdXzLBZf3DXzD/IKTI4N0YhhBCVYlciYTab0el0AMTGxvLLL78AkJCQgI+Pj+OiE3XbxUccftpiAL7ecooLBaXklRhJSr80bbbZrHD0XD5ms1LhaYQQQjiPXYnE5fNFTJo0idGjR9O1a1f69+/Po48+6rDgRB3nriYSvtoiAL7846Rl18HLEokv/jhB/w9+ZuG2U9UanhBCiOuzK5HIz7/0DHvcuHGsXr2aYcOGsXDhQt5++22HBSfqOL3aeuVlLsCXfPJLLs1PcujspfU3lH3LOaAfT9HeH6o9RCGEENdm1+qfV+revTs9e/Z0xKlEfeITBsHN0Zw/yDu+3/K33Ictu5LSLyUSkZlb0GsMhF7YCsioICGEqEkcMiGVq6urI04j6huNBgZ/CMCA0rV0d0mi503BABw6k4fZrFBsMOFbehYA9+LzGExmp4UrhBCiPJnZUjhX9C1w81gAvm7wOXPvjcXNRUuRwcTpzEJOXigghEwAgsji1AWZb0IIIWoSSSSE8/X7FwTEos1Lw3XdS9wUos6OuiclmyNn8gjTqIlEQ002h8/KHBNCCFGT2JVIrFmzxmp7xIgRDglG1FN6bxgxB9DA3qXcFZQOwEvf7WPx73vx1JQAEEw2h8/kXuNEQgghqptdicTgwYNRlEtj+pcvX+6wgEQ91ehmaP8gAI/kzOSuxmYKS01kpp+0VNFrjKSmpzspQCGEEBWxK5GIiYkhKSnJ0bGI+q73K+DmjcuZPfzn/Hgej0ojVHPBqkpG+imrJFYIIYRz2ZVIvPjii4wZM4aNGzdy7tw5srOzrV5C2MU3DB5ZBZFd0JhKeJ55dPTNs6piyElnX2qOkwIUQghxJY1ix693Go3mmvvrwm+Ms2bNYtasWZhMJg4fPkxOTg6+vr7ODqt+KMyEj9pBSS7moGZoMw5Zdj1T+gRuNz/I9BFtnRigEELUbbm5ufj5+VXqu8+uRGLnzp3X3N+pUydbT1lj2XIzhQNtfAc2lp8ldbphFPO1d/PL5J6E+Xk4ITAhhKj7bPnus2tmy7qUKIgaqtvfYfunUHixj4RPGOSl08yrkKJcE0Nn/sHsBzvSqXGAc+MUQoh6zu55JEpKSvj555+ZM2eOpSw1NdUhQQmB3gduffrSdnhHAPpFQYeGOu4tXMqkz1bx2e/HKDaYnBSkEEIIuxKJEydO0K5dO+6++24ee+wxS/nTTz/N//73P4cFJ+q5+EfBPxpc3CGmFwBepRl803QDz7l+w8u6+by96iC3v7eR/dIBUwghnMKuROLpp5+mb9++ZGVlWZU/++yzvPPOOw4JTAhcPWDCrzBxG4S2VssuHMX1wDcA3Om2jya+kJ5TzANztrLx0Lk60dFXCCFqE7s6WwYGBnLkyBECAgLQaDSW/7zz8/MJCgqiuLjY4YE6i3S2rCFKC+Cj9lBwzqq46O4veejPUHaeUpPazo0DmD6iDTHB3k4IUggh6gZbvvvsapEoLS3FbFZXYbx8KGhycjLe3vIfuKgCbl6WlUIB8FA7WXocWcn8cZ15pHtj9C5atp/MZOB/N/HF5hOYzdI6IYQQVc2uRKJ3797MmDEDuJRI5Obm8swzz9C3b1/HRSfE5VoMhm5PQlh7GDZbLUv6Ea8L+3l9SCt+mdyLW+OCKDaYeeunROL/tYHnl++hoMTo1LCFEKIus+vRxtGjR+nRowfh4eHs2rWLQYMGsWXLFtzd3fnjjz+Ijo6uilidQh5t1FCKAovugyNrwc0HtDro8RxKt4ks2Haad1YfJP9iAtG6kS8R/p50iw1kTLfo606oJoQQ9V2VT0gFkJmZyeeff87OnTsxm8107NiRxx57jMDAQLuCrqkkkajBinNgTm+4cFTddvWESXvAuyElRhN/HrvApMUJ5BZfapEY0i6c5/o1IyrQ00lBCyFEzVctiUR9IYlEDVeQAcnb4fd/Q1oCdHwY7ngdvNSE9vDZPBZtO43eRcvczScwmRU0Guh5UzBP3h4nE1oJIUQFqiWRyM3NZf78+ZZVQFu2bMmYMWPq3JetJBK1xJENsHDEpW3vUOj1InR6xFK042QmM345yu+HzwPgotUwfURbRnRsJI87hBDiMlWeSGzZsoXBgwfj5uZG+/btAdi9ezcGg4FVq1bRuXNnuwKviSSRqCUUBX57B/YshqyTaplGCw//CI1vtap6MqOAd9cdYuXedABigrwY0i6c/q1DaRbig1YrSYUQon6r8kSiQ4cO3HrrrXzwwQe4uroCYDAYeOaZZ/jjjz/YtWuXfZHXQJJI1EIlebDqOTWpcPOBJrfB7VMuTWoFmM0K768/xNxNJygxmi3lfh6uxDduQCN/D5qH+XLPzRG46uyeSV4IIWqlKk8kPDw8SElJKdex8sKFC0RGRlJYWGjrKWssSSRqqZJ8+HIAnNmrbrt5w73zIa6PVbW8YgPrE8/y4540th7PpOiKdTuah/rwSPfG9LgpmFBfd3kEIoSoF6p89c+4uLgKE4nk5GTi4uLsOaUQjqX3hgm/QNpu+PlNOLkJvhkLwz+Dw6uhwxiIjMfH3ZXhHSMY3jECg8nMgbRcdp3K4mxeMd/sSObgmTxe+HYfANGBnrw6qCV3tGgoCYUQQlxkV4vEnDlzePfdd5k+fTrx8fEoisLOnTt58cUXeeGFFxgx4lKnN39/f0fGW+2kRaIOMJbC/MGQvO1SmU4P/adB+wfB1b3CwzILSlmy4zSr9qWTlJ6H6eJMmY38PWgR5gNoyCs28ECXKO5q36ga3ogQQlSPKn+0YctvY7V9dKkkEnXEhWMwuzsYi8ArGArUkRt4BkHvKeqwUa3uqofnlxiZ8csRvvzjJKWX9ako82CXKAa2CeOvU1lEB3pye/OG+Lq7VtW7EUKIKlXlicTOnTsrXbdTp062nr5GkUSiDkneAWf3Q/sHYNunsP0zyElW9zXtByPnqWt6XENhqZEdJ7NIyy7CaFY4lVHA3M0nytXz0bswqU9Tbm/ekCBvPd56F3QyGkQIUUvIhFQOJIlEHWYywo65sOENtaUiuLm6lke7UbBnCZTkQuPbYMtMiO2tlldg85EMZv56hMNn84lv3IAjZ/M5nlFQrp6P3oX4JgHc1T6cwW3DJbEQQtRYVZJITJgwgddff52IiIhr1ktOTuatt95izpw5lY+4BpNEoh5I3q6u21GUqW57NICirCsqaWD08nKjPipiMiss3n6aZTuTOXw2v9xIEIBQX3eahniTX2IkLtib0V2j8XF3Qbm4z0tvVz9oIYRwiCoZtREaGkqLFi3o06cPQ4YM4eabbyYkJARFUThz5gw7duxgxYoV/Pbbb0yaNOmG34SjlJSUWFYqbdasGUOGDHFyRKLGiewM//cXJHwNm/+jJhQ6N/BqCLkp6iyZ+Wdg+Tjo/araWtEgGvpPB++G5U6n02oY3TWa0V2jURSFUpOZghITadlFbEg6y7w/T3Imt5gzucUAJJzOZtlfKZbjvfUuPNAlilbhvrQK96NxoCfJWUWsO3CGTo0bcHO0TOsthKg5bHq0kZyczIwZM1iyZAnJyclW+6Kiorj//vuZOHEikZGRDg/UXsXFxbzyyiscP34crVbL8uXLbTpeWiTqmcJMdSKrJj0gsClkHIagm+CrodajPgA8A2HMiksTXZlN1+ywablEqZHdydmkZBbh7qbjxz1pbDt+AQV1oqyC0vItGGV0Wg0v9m9O35YhFJQaCfByI9TXnfwSIx6uOlxk8iwhhANUSx+J5ORkkpOT0Wg0RERE1KjkoSI//fQT8+bNk0RC2MdQBKufh4QF0Go4nD+odtz0CYP7FsAfH8GJ3+Duz6BZf+tjFQUUc6WSDLNZYV3iWdYdOMPpzEL2peZQYjSj1UBcQ28On80vd4yLVoPx4tDUthF+3BcfSanRTGQDT9pH+RPkrXfILRBC1B+1qrNlUlISn376KQcPHuTf//43bdu2LVdnw4YNfP311+Tl5dG9e3eefPJJ9Hr1P8fU1FQWL15c4bmfffZZy1BVSSSEQxiK1XknirLgiwFwPsl6v04PvV6A5oMhuJm61PmCEZB3FkZ/C8E32XY5k5n8YiOeeh1uOi1f/nGS7xJSOHw2Hx+9C9lFBsv8FlcT7udOkI+e7EIDHq46BrQJpXW4HwpgMpvx9XAl4XQ2B9JyMJkVJtwWI6uiClHPVfnMlo7yr3/9iwULFjB8+HDWrl3Liy++WK7O4sWLGTNmDK+++irR0dFMmzaN9evXs2bNGkBd4+PMmTMVnl9RFJmBUDhW2eRVHg3gof/B6ucg6Sd1O7Q1nPgdfn5LfTUfrC5znrJDPWbBcLh/idWaH9e9nE5LAy83y/a4W5sw7tYmlu3CUiOZBaUEeLmRU2Rg8bbT7DiZha+HC8fPF3D0fD5pOcWk5RRbjjl0Nu+a11x74CwxwV60i/CnXYQfbSL8CfbWo3fVcianmBV70mgb4cfQduHy70sI4dwWiTNnzhAaGkpKSgqRkZH8+uuv9OrVy7LfbDYTFRXFQw89xLRp0wA4cOAArVu3Zt26dfTt27fS15IWCVFlctPVBMPVE3Z9BYfXwtH1l/a7eIBPyKVVSQNi1EciTXpCSEuIiAef0KoJrdjAkbN5ZBUY8PVw5XRmIb8ePMepzAJ0Gg0ajYYLBSW0DPOlU3QAx87n883OZK7TyAFAq3BfGvroaR/ZgMZBnvi6u3JTqA/uLlrSc4rJyC+hSZAXUQGeknAIUcvUqkcbwFUTib1799KuXTu2bt1Kly5dLOXNmzdn0KBBvP/++5U6/8cff8xff/3Fjh07GDNmDPfccw+NGzeusG5JSQklJSWW7dzcXCIjIyWRELY5sx8O/qS2SLS8S00e1k2BA/8rX1ejVZMJF73anyK4OXSfBP7O6XeUWVDKnpRs9ibnsDclm/1pOeQVGykxmtFpNdwSG8ifRy9Qaio/w2dFGvroaRfpTwNPV/w93fDzUGf8zC0y0DTEh9hgLzzdXCg2mGgc6IWfp8wIKoSzOeXRRkZGBv7+/ri4OO5pycmTJwHKdeSMiIiw7KuMs2fP0qBBA/r168eZM2esEoUrTZs2jTfffNOecIW4JLR1+UcYI+dB339CbiqcS4KTm+H8ITi7z3pEyMlNsPNzCGkFfpFqEtKkh1q3YUu178XpLRDVFfyj1GMKMiDjCGhdIKgpePjbHXqAlxu3N2vI7c3KD20te1yYnFnIrtNZ5BYZ2HYikwv5pWTkl3A8owCTWaGBpytB3npOZRZyLq+E9YlnK339Rv4exAR74aLV4Ovhit5Fi06rpXUjX2KDvdX5NhQI9tET5K2Xib2EcDK7WiT27dvHvHnzLC0CTzzxBJ9++imBgYGsWbPG5mmxr9YisXz5ckaOHElmZiYNGjSwlA8dOhRFUfjxxx9tDf26pEVCVLuMo5CWABoNmI2we6Ha1+K6NNDoZnVESNquS8VaF+g0Xm3p8AyA255VR4yU5Knn92hw9VPeIPPFZyLai1/uxQYTf53K4kRGATlFBrILS8kuNKAAXm46EtNzScsuprDUiKtOy7m8qyf5FXHRagj0dsNL74K3Xk0w3Fy03BTiTXJmEXoXLW0j/HHRqfE08HSjeZgPiqLg4+5KiK87vu4u8uhFiCtUeYvEs88+y0svvQSooy4WLlzIhg0b2LBhAy+88AI///yzPactp2zl0CsTiQsXLhAbG+uQa1xJr9dbRoQIUS2C4tRXmXajIDcNUv9SFxc7sUlNFAKbwqk/wFCozm2RcRhSy9a90aiPQswmtcVj+6eXzpe8DfS+cGgVmEqhxVDoNhH8ItRhrQ2agLaS808oChxcqR4b3r7cbu0VrQPurjq6xwXRPS6oUqfPKTKQmJZLanYRZkUhu7AUg0mhsNTI3pQckjMLKSw1odFARn4pRrPC2dwSwDoB+evUpZlJfz547prX9HDVEernTkMfPcE+evw8XPH1cCW7sJTcIiMxwV4Ulppwc9HSPNSHYB89DTzd0Gig2GCmVbgvrjJ/h6jH7EokLu+zsG7dOoYNG8Ydd9xBfHw8UVFRDguubdu2aDQaEhISLIlDaWkpBw4cYPjw4Q67jhA1jm+4+gLoNO5SeXEOlBao+7JOqSNCjMUQ11ft0AlwdANsnwt6H0j8Xt2+XOL36quMuz+0HArGEjWBaT0CGt8Kabsh+yS0HXWpv8bv78GvU9WfWw6DYbPBzdNhb9vPw5VusYGVqmsyK5zPKyEjv4SCEiMFpUY0aMgtNnDoTB4RDTwpLDVy+OIoFQ0aUrOLOHY+HxedhpxCA7nFRooMJk5kFHCigvVRKsPf05XmoT54613wuvjy83AlwNMNf09XArzc8Pd0I8DLjQBPN3zcXawSLrNZwaQokoyIWsuuRMLT05PU1FSaNm3KypUruf/++wEoLCzEw8PDYcE1bNiQAQMG8OGHHzJkyBD0ej2zZ8+mpKSE++67z2HXqcisWbOYNWsWJtPVZxkUotq5+6kvUKfpbhBdvk5cn0trgsSPV6f09glVy1zcYets2PfNxZk4XaA4Wx1tUubkJuvzbfoAmvZVH5VYOotq1GREo4Hhc0HnorZWmAzgcnG4ammB2qoS3AK8gx14E1Q6rYZQP3dC/dztPkdRqYmzucWcvThleUZ+KblFBnKLDXjrXfB1d+V4Rj4+7q7kFRs5ei6PzIJSsgoNABdbTQxsPZ5Z6WtqNeojlgAvN0J83UlMzyW7sJTGgV6M7BTJ3R0a0dBHX651R4iayq4+Eo8//jibNm2iffv2rFy5kmPHjhEUFMTXX3/Nhg0bmD9/fqXOs379et5//31KSkrYuHEj8fHxBAQEMHr0aEaPHg2oE04NGDCAjIwMQkJCOHr0KHPmzGHUqIpXYnQ0Gf4p6iRDEWh0aiJwegvsWw6uHmrCsf9buHAcvILUV+pf1sfGT1BbML4eDmaDmoxoXdUVVEFNdDwC1Mcypfng6gVdHlfn1dBqoUFj9VHL+YPq6JaQlhDaptpvgSMYTWZ2nc7mTG6x2ipSYiS/xEhOkYGsiwlHVmGp+iowkF9irNR53Vy0RPh7qB1KffR4uOrILjTQIsyHLk0CiWjggU6roYGXG96ywJuoAlU+/LO4uJj333+fU6dOMXbsWG655RYAJk2axKRJk4iJianUeVJTU9m3b1+58ri4OOLiLj0zNpvN7N69m7y8PNq3b4+fn5+tIdtNEglRrymK2vHz7H61BSO0NcTcriYge7+Bn56B0mtMcKX3g5Ic6zKNVm0ZMRReKitrLfEMgMA4tWPonqUQ1xt6vnDpMU8tV2o0k12oJhjn80pIyy4iMsCTJkFebDpyns83n+Dw2bxKzeNRxs/DlUb+HjT01RPsrSYewd5qf4+ykS3BPnrpVCps4pR5JKpi+GdNIImEENdgNqsro5oM6oRcWh3kn4XiXLWFI6Q1HPwRdi+ClJ1qElFwsfOjm7eaNKTvvv519L7qsT5h4NdIHXkS2FRdfTXjiDrqJa63Gs+FI+p5Y++AiE5q0lOLGExmzuQUk5JVxPn8EjLySigymPBy07HjVBZJabmcyS3GrCgUGyo3lweorRwVJRrB3m4Eeetx0Wlx0WkI9FIfu7i5aNG76Czzfoj6pcoTCUcP/6zJJJEQwsFy09VHHgExauKRlgDJ29WfCzLg7AF1f7OBsHepmoBg5+87Za0fLnp1hlG/RhDZRU1yfBupI1xObIKAJtC0H3iHwIHvoNXdaofTMqWF6iMcFze1U6rWtfIjXapQXrGBtOxi0rLVpON83sXXxQSkrCyvuHKPVCri6+6CwaTg4aYj1NcdL70Od1cdnm46PFx1eLi5EOrrTrCPHhedBledBhetFi+9jhBfd7zcXHB31aF30eLuqsNVp0Gn1ZRrHVEUhRKjGb2LVlpOaoAqTyT69evHSy+9xO23305SUhKdO3fm+++/Z8OGDWzfvt1hwz+d6fLOlocPH5ZEQghnKS1QR5OYTZCTrLZ4FGSow1+LstU+GaFtIOlHNUGI7gZnE+HIOjUhsVdg3MVVW13hwlFw84JGHdXEw8MfbuoPbUaqiU5AEzX50OrUBdr03mr9yjCbLrbo2N9p9HqKDSYyyiUapZzPL7448qUUk1nBYDKTWVDKhYJSjCazTY9YbKHRgKtWqyYdOi1ms0JBqRGzAj56F6ICPfHzcMXH3QUPVx2uOi1uLlrLn2469WdXFw1ul+27tF9jdYyrTove8rPminOU/Vw+uanPqjyR8PX15cyZM3h6evLRRx+xc+dOvv76a3Jzc4mKiiI7O9ve2GscaZEQopYylkJRpjo81liidjA9ewDO7FW/vDOPq60MzQdC5gl1ifiiLGjcXU0WbG0F8Q5VWz6yT6mPYtqMVLfT96h9TUJaqrOVhrQG/2hw91U7pC4cqSZKA9+DdlU7Gs1WBSVGUrOLcHfRkV9i5GxeMcWlJgpLTRQZTBQbTBSUmEjLLlKTD7PZkpDkFRs5l1dCUalaz1hVWYkDuerUxMS1LOm42HKi0ajJj1ajQQOXyrhYdnEfV2yrx5Udc+l47cWDtRp1WLJWq/55eX3txZ/VwTuai9vlz6u9LJZW4X5M6FG5PorXU+UTUlXX8E8hhLCbi1v5xdDC2wMPVly/9ytqguHqrs42mpemtkYYCtQJwM4fVqczj+ujzuexdbY6VDYiXp3PI/+yVYhLctVpzi93+s/y19S6qiNfAP73mDpipusT6norPmFO79/hpXfhphAfy3ZL7P9lymgyU2I0YzCZMZjUZMNoUig1mdFqwFvvgrubjvTsYlKzC8krNpJbbKTEYKLUZKb0smNLjWZKTWYMF8vU/eo5L9UzU2pSKDWaLNezHHfxPKYrkhu1nglKa+ew/9xio8MSCVvYlUjcdddd3HXXXbRv355t27axaNEiQB3O2a9fP4cGKIQQ1ULnqr6g/GyjoK5r0rTPpe3L+1AU58K5RLXlI7SNOqT21MXEoWEL0LmpI1/OHlBfeenqPrNBTRqaDYQ//wtH1qovADcfdd2U4GZqUmEshvCO0OQ28Ay8FGstoXbmvH6/Et9QV5qF+ly3niOUtZ5YJSpGxbJtNJtRFLVtyqwo6s+Kom6bFUs5CpgVUFDUP8vqov5ZVqbmLWV1Lp6TS/XNl/9pVX7p3CiK1fkurxcZ4Jxf5J06/LM2kEcbQgiHM5vVYbF5Z9W+GDoXdfTJ7++q83ZkngDlOr8V6/3AswEExEJUNzWR8YuAFkPUfiPnEtWOpmHtwTesWt6WqDtq3TLiNZkkEkKIamcsUftwZBxWH6nkn1U7ch77RU04bO2/0bCV2qLi5qn+KpyTonYQDWkFOr3auuEVpA6rVRT1/IqiTrMe0krtxGoyqJ1X9X7XHrFy/pA6LNeWxeGO/Qp/fQm9XoaGzW17b6JKVNsy4mlpaRw6dAhFUWjevDnh4XVj0hiQKbKFEE7kolcfiTRsUX6f2aT20Si8oI5eOb1FnYujYSu1I2nqX+qEXkFN1YTk/EE4d0B9XS5leyWD0ahzhBgKAUXt1xHcTE0uCs6rS9v7hKlJSsZROLxaffTS500IjFVjAXXeEFd3dVvvqx6n0ajJ0pIH1b4o6XvhsY3qqBhRa9jVIpGfn8/EiRP5+uuvKTtco9Hw0EMP8fHHH+PlVclhT7WAtEgIIWq1ggvqqrFFWepQWsWsPuo4m6i2TJhK1YSj4LyaoGg0wMUhAgUZlyYQqy4a3aU1Zdz91KTC3U9NRAovqImU3lttLXHzUdeKyT+nJl0BMWo9s0F9zOPmjdq6gvq+UdT+Kn4RarKGRp1rRKNVt40l6vt19VSTnbJ1Y+qhKn+08fjjj/P777/z4Ycf0q1bNwC2bNnC008/Tc+ePfnkk0/si7wGkkRCCFFvKYqaTJTmqWumuPupj1nO7FO/rD0C1L4YZUmKRqsOez34Exxee7H1wUdNSkry1SG4em/1HEWXlnontC3c8Rr8MFHd5wwa3aVko4yLu5pQ6H3Un7VatZ5Wpw4dLvtZo73458X1a7KToTCj7MSXjb7RXErU4GISc1niZrXvyp+vPFcFom+BIR/d+L2gGhKJoKAgfv31V9q0sV5oZ+/evfTu3ZuMjIyrHFn7SCIhhBAOpihqIlH2hezqqX5BGkvVVofiHLWloThHnXSsOEdNZjwC1P4cJflqklKW4HgFqQlNTsrFBem0kH1aHemi0WL1RW0ounayonNTW2lqo6b94MFlDjlVlfeRyM/Pr7A/RHh4OPn5NzCTnBBCiLpPo1EXaLuSi5v62KWqR5mYDGA2XupYajapHUm1FzudKmZ1LpDiXDVhKclVH3soJnXEjdl48WfTpT8v/9k3XO03AlhaOC7vxFpWfnlZ2c+X17X8zBXHXoUtHVwdyK5EIj4+nqlTp/Lee++h0+kAMJlM/POf/6Rz584ODVAIIYRwqMvnDCnjftlv3Rqd+qXspC/m2sauROKDDz6gX79+fPfdd9x8880A/PXXX+Tl5bF27VqHBugsMmpDCCGEuD6755G4cOECc+bM4cCBA2g0Glq2bMljjz1GQEAFzVW1mPSREEIIUd/IhFQOJImEEEKI+qZKOlumpKRUOoCIiIhK1xVCCCFE7VXpRCIyMrLSJ5VGDiGEEKJ+qHQikZSUVJVxCCGEEKIWqnQi0by5LKQihBBCCGs3tGhXfVD2mCY3N9fJkQghhBDVo+w7rzJdFSSRuIqyeSRKS9WpUm3pIyKEEELUBXl5efj5+V2zjgz/vA6z2UxaWho+Pj5orrVYSiXl5uYSGRlJcnKyDCdF7seV5H5Yk/thTe6HNbkf1hx5PxRFIS8vj/DwcLRa7TXrSovEdWi12ioZzurr6ysf/MvI/bAm98Oa3A9rcj+syf2w5qj7cb2WiDLXTjOEEEIIIa5BEgkhhBBC2E0SiWqm1+t5/fXX0ev1zg6lRpD7YU3uhzW5H9bkfliT+2HNWfdDOlsKIYQQwm7SIiGEEEIIu0kiIYQQQgi7SSIhhBBCCLvJPBLVyGAwcODAAfR6Pc2bN3fIBFe1wenTpzl9+rRVmZubG507dy5XNz09nZSUFGJjYwkICKiuEKtFQkICAB06dKhwv9FoZP/+/bi5udGiRYsKPx+VqVNbJCQkoCgKHTt2LLdv8+bN5cpiY2MJCwuzKjMajRw4cAAXFxdatmxZa+9HXl4eR48eJTw8nJCQkArrVOa91rX7ERYWRmhoaLn9f/75J2az2aqsSZMmNGrUyKrMZDKxf/9+dDodLVu2vO7ESjVVSUkJhw4dwsvLiyZNmlT4PkwmEwcOHECj0dCqVSu769hFEdXit99+U0JDQ5Xo6GglKChIad26tXL8+HFnh1UtpkyZovj6+irdu3e3vIYOHWpVx2g0KmPHjlXc3d2Vli1bKnq9XnnzzTedFLFjffjhh0qzZs0Uf39/pVWrVhXW2bx5sxIWFqZERUUpwcHBSsuWLZWjR4/aXKc2+O9//6u0aNFC8ff3V5o1a1ZhHUBp3bq11Wfm22+/tarz559/Ko0aNVIiIyOVhg0bKs2bN1cOHTpUHW/BYY4dO6aMGDFC8ff3V9q3b6/4+Pgo/fv3V86fP29Vb9u2bUpERIQSGRmphISEKDfddJOSlJRkc52a7sSJE8rIkSMVPz8/pX379oqvr6/St29f5ezZs1b19Hq90rJlS6vPx+LFi63q7NixQ4mKilIiIiKUkJAQJS4uTjlw4EB1vp0bVlJSojz//PNKUFCQ0qFDByU0NFSJiYlRNm7caFVv165dSnR0tNKoUSMlNDRUiY2NVfbt22dzHXtJIlEN8vLylODgYOWZZ55RFEVRDAaD0qdPH6Vbt25Ojqx6TJkyRenZs+c167z33ntKQECA5Ytx48aNik6nU1auXFkNEVatSZMmKYmJicoLL7xQYSKRn5+vhIaGKk899ZSiKGpSdeeddyrx8fE21aktnn76aeXAgQPKlClTrplIrF+//qrnKCwsVMLDw5W///3viqKo92PgwIFKx44dqyTmqrJu3Tpl+fLlitlsVhRFUS5cuKC0adNGGTlypKVOcXGxEhERoTz22GOKoiiKyWRShg4dqrRt29amOrXBhg0blG+++cZyPzIzM5X27dsrd999t1U9vV6v/Pjjj1c9T0lJiRIdHa2MHz9eURT1ftx9991Kq1atLOeuDTIzM5WZM2cqxcXFiqKo7+PRRx9VQkJCLHVKS0uVmJgY5eGHH1YURVHMZrMycuRIpXnz5orJZKp0nRshiUQ1WLRokaLT6ZSMjAxL2YYNGxSg1v3GYI8pU6Yot9xyi5KQkKAcOnRIMRgM5eq0bNlSefLJJ63KevXqpYwYMaK6wqxyV0skvvnmG0Wr1Vr91rVx40YFsPzGUJk6tc31EomvvvpK2blzp3LhwoVy+7/77jtFo9EoaWlplrLNmzcrgJKQkFBVIVeLqVOnKmFhYZbtFStWKICSnJxsKdu6dasCKDt27Kh0ndpq+vTpSnBwsFWZXq9X5syZo+zYsaNc642iKMqqVasUQDlx4oSlbOfOnQqgbNmypapDrlJfffWVotPpLMnFunXrFMCqdXL37t0KoGzatKnSdW5E7XxgVMskJCTQuHFjAgMDLWVl/QPKnpvXdVu3bmX06NH06tWL8PBwli5datlXXFxMUlISN998s9UxnTt3rhf3JyEhgcjISBo2bGgpu/LzUZk6dc3TTz/NuHHjCA8PZ9iwYZw/f96yLyEhgfDwcKs+E3XlfuzYsYO4uDjLdkJCAiEhIVZr/nTq1AmNRmP1+bhendrqyvtR5vnnn2f8+PFERkYyaNAgzpw5Y9mXkJBAYGAgjRs3tpR17NgRnU5XK+/H4cOH2bRpE19//TVvvvkmr732mmXSqYSEBPz8/IiNjbXUb9euHW5ublafj+vVuRGSSFSDzMxMqyQCwMfHB1dXVzIzM50UVfW55ZZbOHXqFPv37yc1NZVnnnmG0aNHWz7A2dnZKIpS7h4FBgbWi/tT0efDw8MDDw8Py/uvTJ265LPPPuP8+fPs2bOHw4cPc+jQIR599FHL/oruh6urKz4+PrX6fixZsoQVK1bwyiuvWMoqeq86nQ5/f/9rfj6urFMbLVu2jO+++87qfgDMnDmTjIwM9uzZw9GjR0lOTubhhx+27K/ofmg0GgICAmrl/Vi6dCnPP/88kydPJigoiOHDh1v2VfRewfr/z8rUuRGSSFQDV1dXiouLrcqMRiNGoxE3NzcnRVV9Bg4caPlNSaPR8OKLLxIREcHy5csB9f4A5e5RUVFRvbg/FX0+FEWhtLTU8v4rU6cumTBhgmXEQVRUFFOmTOHHH3+koKAAqPh+gPoZqq33Y926dYwdO5b333+ffv36Wcor817r4v34+eefGTNmDO+88w4DBw602vfoo49aRhw0atSI1157jXXr1lm+FK92P2rr/ymvvvoqW7ZsITU1la5du9KrVy9ycnKAyr3Xqr4fkkhUg+joaNLS0lAum428bDsqKsqJkTlPw4YNSU1NBSAgIAAfHx/LdpnU1NR6cX+io6NJT0+3+nykp6djMpks778ydeqykJAQFEUhPT0dUO/HmTNnrIYAnjt3DoPBUCvvx/r16xk2bBhTp07l6aefttoXHR3N2bNnMZlMlrLMzEyKioqsPh/Xq1Ob/PLLLwwdOpTXX3+d55577rr1y4bMpqWlAer9KPs8lMnNzSU/P79W3o8yLi4uPP3001y4cIEdO3YA6nvNyMigtLTUUq+goICcnByrz8f16twISSSqQd++fcnIyGDLli2Wsh9++AFPT0+6d+/uxMiqR9lvkWXS09PZv38/rVu3BtRWijvuuIMVK1ZY6hgMBlatWkXfvn2rNVZn6Nu3L1lZWWzatMlS9sMPP+Du7s5tt91W6Tp1xZWfF1B/W/fx8bH8p9e3b19yc3PZuHGjpc4PP/yAm5sbPXr0qK5QHeLnn3/mrrvu4o033mDy5Mnl9vfp04eCggJ+/vlnS9kPP/yAq6srPXv2rHSd2mLjxo0MGTKEV155hRdffLHc/qt9Pjw8PIiJiQHU+1FSUsL69estdX744QdcXFy4/fbbqy54B6vovR49ehTA8qjijjvuwGAwsGbNGkudFStWoNVq6d27d6Xr3JAb7q4pKmXkyJFKTEyMsnjxYuWTTz5RvL29lbffftvZYVWL9u3bK9OmTVNWrVqlzJs3T2nRooXSvHlzJTs721InISFB8fDwUCZOnKisWLFCGTZsmBIaGlpu/HhttHv3bmXTpk3Kgw8+qDRp0kTZtGmTsmnTJqvRK/fff7/SuHFjZdGiRcpnn32m+Pj4KG+99ZbVeSpTpzbYs2ePsmnTJmXMmDFKVFSU5X6UlpYqiqIoc+fOVYYNG6Z89dVXyqpVq5Snn35acXFxUWbOnGl1noceekiJiopSFi5cqMyZM0fx8/NTXnvtNWe8Jbtt2bJF8fT0VEaMGGG5D2Wvyz3yyCNKRESEsmDBAmXu3LmKv7+/8vLLL9tcp6bbvn274uXlpQwbNuyq92PBggXK4MGDlfnz5yurV69WnnvuOcXV1VV57733rM41YcIEJTw8XPn666+VL774QgkICFCef/756n5LN2TBggXK0KFDlfnz5yvr169XPvroIyUsLEy56667rOr97W9/U0JDQ5WvvvpK+fLLL5XAwEDLdAO21LGXrP5ZTUpLS/noo4/YsGEDer2ekSNH8tBDDzk7rGpx4cIFZsyYwY4dO/D29qZr16787W9/w93d3apeQkIC//nPf0hJSaFZs2a88MILREdHOylqx3n00Uc5ePBgufKVK1fi5+cHqJ+PGTNmsG7dOtzc3BgxYgRjx461ql+ZOrXB448/zoEDB8qVr1ixwjKb6dq1a1m8eDFnzpyhSZMmjBs3jvj4eKv6BoOBmTNnsnbtWlxcXCz3ozbN5rhw4UJmz55d4b7LZ/c0Go3MnDmTNWvW4OLiwrBhwxg/frzVe61MnZpuyZIlzJw5s8J9v/32GzqdDlAffSxYsIDU1FSaNGnCww8/TLdu3azqG41GZs+ezapVq9Bqtdx1111WfStqi19//ZUFCxaQnJxMWFgYgwcPZsSIEVbvw2QyMXv2bFauXIlGo2Ho0KE89thjNtexlyQSQgghhLBb7UrNhBBCCFGjSCIhhBBCCLtJIiGEEEIIu0kiIYQQQgi7SSIhhBBCCLtJIiGEEEIIu0kiIYQQQgi7SSIhhKhzjhw5wqpVq5wdhhD1giQSQog6Z+3atTzzzDPODkOIekESCSGEEELYzcXZAQgh6pZTp06xe/duAgMD6dixI56enpZ9R44c4ciRI/Tt25c9e/aQmppKly5dCA0NLXeevXv3cvToUcLCwujSpUuFawIkJyeTkJBASEgInTp1sqzFUMZoNLJ3717S09Np3749jRo1cvwbFqKek0RCCOEQiqLwj3/8gwULFtC1a1cyMjJIT0/n22+/tSy4tXbtWqZOnUqjRo3w8PDAbDaze/dulixZwtChQwH1y//ee+9l48aNdO3alb179xIaGsrq1asJDg62XGvSpEnMnTuXLl26UFxcjIuLCytXrsTX1xeAnJwcbrvtNvR6PQDbtm1j6dKllusIIRzEIWuICiHqvc8++0yJjY1VMjIyLGVTp05VmjdvbtmeMWOGAijvvvuupez1119XQkNDlcLCQkVRFGXmzJlKQECAcurUKUVRFCU3N1dp27atMmHCBMsxs2bNUjw9PZU9e/ZYyv7880/l3LlzVtdZvny5Zf/kyZOVtm3bOvhdCyGkj4QQwiG+/PJL2rZty6+//sqyZcv45ptv8Pb25uDBg6Snp1vq6fV6/u///s+yPXnyZM6dO8fvv/8OqEtJjx49mqioKAB8fHx46qmnWLp0qeWY+fPnM2bMGNq2bWsp69atm6XFAiA4OJgRI0ZYtnv16sWhQ4cc/8aFqOfk0YYQwiFOnjxJaWkpy5cvtyq/7777KC0ttWyHhYVZHjcAeHt7ExwczKlTpwC1j8U999xjdY7Y2Fhyc3PJysqiQYMGnD59mlGjRl0znoCAAKttvV5PSUmJXe9NCHF1kkgIIRzC19eX3r178+9///ua9bKzs622FUUhOzuboKAgAIKCgsjMzLSqk5mZiaurq6X/g7+/PxcuXHBc8EIIu8mjDSGEQ/Tv35+FCxeSl5dnVZ6ammq1nZ2dzcaNGy3bq1atwmw2Wzpk3nrrrXz//feYzWZLnWXLltGtWzfLqIx+/fqxdOlSq5aOgoICCgsLHf22hBDXIS0SQgiHeO211/j555+Jj49nwoQJeHp6sm3bNo4fP27p/wDg5eXFmDFjeOqppzCbzUyfPp2nnnqKyMhIAF555RWWLVvGwIEDGT58OFu2bOGHH36wSj5effVV1qxZQ7du3Xj44YcpLi5m8eLFrFq1ymq4qRCi6kmLhBDCIQICAti+fTvPPvssSUlJHDhwgD59+vDLL79Y1YuIiGDlypVkZWWRlJTEBx98wLvvvmvZ37BhQ3bv3s0tt9zC5s2bCQ4OZteuXXTu3NlSJygoiJ07dzJmzBgSEhLIyclh+fLlhIWFAXDTTTcxaNAgq+uGhoZy3333VeEdEKJ+0iiKojg7CCFE/TBz5kxmzpzJwYMHnR2KEMJBpEVCCCGEEHaTREIIUW0qeuQghKjd5NGGEEIIIewmLRJCCCGEsJskEkIIIYSwmyQSQgghhLCbJBJCCCGEsJskEkIIIYSwmyQSQgghhLCbJBJCCCGEsJskEkIIIYSwmyQSQgghhLDb/wPtHLrUvVaM5gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 540x340 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# learning curve, parsed from the trainer's per-epoch log\n",
    "epochs, tr_loss, va_loss = [], [], []\n",
    "for line in log.getvalue().splitlines():\n",
    "    m = re.match(r\"epoch\\s+(\\d+) \\| train loss ([\\d.e+-]+|nan)\"\n",
    "                 r\" \\| val loss ([\\d.e+-]+|nan)\", line)\n",
    "    if m:\n",
    "        epochs.append(int(m.group(1)))\n",
    "        tr_loss.append(float(m.group(2)))\n",
    "        va_loss.append(float(m.group(3)))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5.4, 3.4))\n",
    "ax.semilogy(epochs, tr_loss, label=\"train\")\n",
    "ax.semilogy(epochs, va_loss, label=\"validation\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "ax.set_ylabel(\"loss (per-atom E MSE + 0.3 F MSE)\")\n",
    "ax.set_title(\"ReaxFF-nn on rMD17 malonaldehyde\")\n",
    "ax.legend(frameon=False)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"reaxff_loss_curves.png\", dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73efe9ba",
   "metadata": {},
   "source": [
    "## 3. Test-set accuracy: original vs seed vs trained\n",
    "\n",
    "Energy and force parity on held-out configurations. Besides the seed and the\n",
    "trained libraries, the same test set is evaluated with the **original\n",
    "classical ReaxFF**: the published C/H/O combustion parameterization of\n",
    "Chenoweth, van Duin and Goddard (*J. Phys. Chem. A* 112, 1040, 2008), read\n",
    "directly from the SEAMM `.frc` force-field format (`CHO_cho_2008` (shipped with `xnn` in the SEAMM `.frc` format), shipped\n",
    "alongside this notebook). Nothing about it is fitted to this data — it uses\n",
    "its own absolute energy reference, so a constant offset (fitted on the\n",
    "training split) is removed before comparing energies. It anchors the\n",
    "comparison: transferable published parameters cover all of C/H/O combustion\n",
    "chemistry at once, but malonaldehyde's conjugated dialdehyde backbone is far\n",
    "from that parameterization target, and both metrics show it.\n",
    "\n",
    "A classical functional form with a small message network will not reach the\n",
    "meV accuracy of large descriptor/GNN models on this benchmark — the point of\n",
    "a learnable force field is different: a **physically interpretable,\n",
    "reactive, portable** parameterization (every fitted number is still a ReaxFF\n",
    "parameter) whose accuracy improves systematically as more parameter groups,\n",
    "data and epochs are allowed. Note also what the fit cannot know: the\n",
    "training data samples thermal (≤ 500 K) configurations only, so regions far\n",
    "off that manifold — stretched bonds, close contacts — inherit the seed's\n",
    "generic parameters rather than data (the companion notebook makes this\n",
    "concrete, including where the *original* field in turn beats the trained\n",
    "one)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "191366d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-15T19:31:30.104214Z",
     "iopub.status.busy": "2026-09-15T19:31:30.104019Z",
     "iopub.status.idle": "2026-09-15T19:31:35.278819Z",
     "shell.execute_reply": "2026-09-15T19:31:35.278139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original E MAE    811.4 meV | F MAE  5.917 eV/A\n",
      "seed     E MAE    905.0 meV | F MAE  4.943 eV/A\n",
      "trained  E MAE     82.7 meV | F MAE  0.350 eV/A\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xnn.common.models import ForceStressOutput\n",
    "\n",
    "model = trainer.module.model          # the trained ReaxFF inside the wrapper\n",
    "fs = trainer.module\n",
    "model.eval()\n",
    "\n",
    "\n",
    "def collect(wrapped):\n",
    "    es, fs_ = [], []\n",
    "    for i in range(len(test_set)):\n",
    "        out = wrapped(test_set[i])\n",
    "        es.append(float(out[\"energy\"][0]))\n",
    "        fs_.append(out[\"forces\"].detach().numpy())\n",
    "    return np.array(es), np.concatenate([f.ravel() for f in fs_])\n",
    "\n",
    "\n",
    "seed_model2 = ReaxFF(seed_path)           # untouched seed, for comparison\n",
    "e_seed, f_seed = collect(ForceStressOutput(seed_model2))\n",
    "e_pred, f_pred = collect(fs)\n",
    "e_ref = np.array([float(test_set[i].energy[0]) for i in range(len(test_set))])\n",
    "f_ref = np.concatenate([test_set[i].forces.numpy().ravel()\n",
    "                        for i in range(len(test_set))])\n",
    "\n",
    "# the original published field, read from the standard ffield text format;\n",
    "# its constant energy reference is fitted on (a sample of) the training split\n",
    "orig_model = ForceStressOutput(ReaxFF(\"CHO_cho_2008\"))\n",
    "off = np.mean([float(orig_model(train_set[i])[\"energy\"][0])\n",
    "               - float(train_set[i].energy[0]) for i in range(0, n_train, 10)])\n",
    "e_orig, f_orig = collect(orig_model)\n",
    "e_orig -= off\n",
    "\n",
    "for name, e, f in ((\"original\", e_orig, f_orig), (\"seed\", e_seed, f_seed),\n",
    "                   (\"trained\", e_pred, f_pred)):\n",
    "    print(f\"{name:8s} E MAE {1000 * np.abs(e - e_ref).mean():8.1f} meV | \"\n",
    "          f\"F MAE {np.abs(f - f_ref).mean():6.3f} eV/A\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(9, 4))\n",
    "lo, hi = e_ref.min(), e_ref.max()\n",
    "axes[0].plot([lo, hi], [lo, hi], \"k--\", lw=1)\n",
    "axes[0].scatter(e_ref, e_orig, s=12, alpha=0.5, color=\"C2\",\n",
    "                label=\"original (offset-shifted)\")\n",
    "axes[0].scatter(e_ref, e_seed - e_seed.mean() + e_ref.mean(), s=12,\n",
    "                alpha=0.5, label=\"seed (mean-shifted)\")\n",
    "axes[0].scatter(e_ref, e_pred, s=12, alpha=0.7, label=\"trained\")\n",
    "axes[0].set_xlabel(\"DFT energy (eV)\")\n",
    "axes[0].set_ylabel(\"ReaxFF-nn energy (eV)\")\n",
    "axes[0].legend(frameon=False)\n",
    "lim = np.abs(f_ref).max()\n",
    "axes[1].plot([-lim, lim], [-lim, lim], \"k--\", lw=1)\n",
    "axes[1].scatter(f_ref, f_pred, s=4, alpha=0.3)\n",
    "axes[1].set_xlabel(\"DFT force component (eV/Å)\")\n",
    "axes[1].set_ylabel(\"trained ReaxFF-nn force (eV/Å)\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"reaxff_parity.png\", dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95b8a97",
   "metadata": {},
   "source": [
    "## 4. Export the trained force field\n",
    "\n",
    "`export_library()` unpacks the trained tensors back into a flat parameter\n",
    "library (energies in kcal/mol, networks included) and `save` writes it in\n",
    "the ReaxFF-nn JSON format — a portable artifact: reload it here with\n",
    "`ReaxFF(\"runs/reaxff_rmd17/ffield_trained.json\")`, or hand it to other\n",
    "ReaxFF-nn-aware codes. And because every fitted number is still a ReaxFF\n",
    "parameter, the result can be *read*: the refit sigma-bond dissociation\n",
    "energies below are the force field's own statement of how strong it now\n",
    "thinks each bond is."
   ]
  },
  {
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     "text": [
      "saved runs/reaxff_rmd17/ffield_trained.json; reload round-trip |dE| = 0.00e+00 eV\n",
      "refit sigma-bond dissociation energies (seed value: 120 kcal/mol):\n",
      "  C-O:   160.9 kcal/mol\n",
      "  C-C:    48.5 kcal/mol\n",
      "  C-H:   231.4 kcal/mol\n",
      "  H-O:    91.3 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "trained_path = \"runs/reaxff_rmd17/ffield_trained.json\"\n",
    "model.export_library().save(trained_path)\n",
    "\n",
    "# round-trip check: the reloaded library reproduces the trained model\n",
    "# (to float32 round-off of the kcal/mol<->eV conversions; exact in float64)\n",
    "reloaded = ReaxFF(trained_path)\n",
    "g = test_set[0]\n",
    "diff = abs(float(reloaded(g)[\"energy\"][0]) - float(model(g)[\"energy\"][0]))\n",
    "print(f\"saved {trained_path}; reload round-trip |dE| = {diff:.2e} eV\")\n",
    "\n",
    "lib_out = reloaded.export_library()\n",
    "print(\"refit sigma-bond dissociation energies (seed value: 120 kcal/mol):\")\n",
    "for bd in (\"C-O\", \"C-C\", \"C-H\", \"H-O\"):\n",
    "    key = f\"Desi_{bd}\" if f\"Desi_{bd}\" in lib_out.p else \\\n",
    "        f\"Desi_{'-'.join(reversed(bd.split('-')))}\"\n",
    "    print(f\"  {bd}: {lib_out.p[key]:7.1f} kcal/mol\")"
   ]
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