{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19375bf6",
   "metadata": {},
   "source": [
    "# The ANI-1 dataset in one line, and a paper-style correlation test\n",
    "\n",
    "The **ANI-1 dataset** (Smith, Isayev & Roitberg, *Sci. Data* **4**, 170193,\n",
    "2017) is the training set behind the ANI-1 potential: ~20 million\n",
    "off-equilibrium conformations of 57k small organic molecules (H, C, N, O) from\n",
    "GDB-11, each with a wB97X/6-31G(d) total energy, generated by Normal Mode\n",
    "Sampling. `xnn` serves it through the same one-liner hub as every other\n",
    "dataset:\n",
    "\n",
    "```python\n",
    "from xnn.common.data import load_dataset\n",
    "splits = load_dataset(\"ani1\", heavy_atoms=[2, 3, 4])   # pyanitools .h5 -> xnn dicts\n",
    "```\n",
    "\n",
    "The first call downloads the one 4.8 GB archive from figshare (cached and\n",
    "MD5-verified under `datasets/ani1/`); afterwards it is instant and offline. Here\n",
    "we load a **small subset**, train `xnn` ANI on it, and reproduce the paper's\n",
    "energy-correlation plot (Fig. 4A) at small scale.\n",
    "\n",
    "Run with the **`xnn`** kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf0f116",
   "metadata": {},
   "source": [
    "## 0. Load a subset of the ANI-1 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ee865e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:18:28.793869Z",
     "iopub.status.busy": "2026-07-20T04:18:28.793752Z",
     "iopub.status.idle": "2026-07-20T04:18:35.619554Z",
     "shell.execute_reply": "2026-07-20T04:18:35.612743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8e58df4f2ba48eda589a8510498d1da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ani1:ani_gdb_s02:   0%|          | 0/13 [00:00<?, ? mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10643c6c98ce45eda72289a079658b19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ani1:ani_gdb_s03:   0%|          | 0/20 [00:00<?, ? mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56a4ac226c7f439bb6b6e15d26643c91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ani1:ani_gdb_s04:   0%|          | 0/60 [00:00<?, ? mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 5,580 conformations\n",
      "elements [1, 6, 7, 8]  atoms/mol 2-14  example SMILES: [H]C([H])([H])C([H])([H])[H]\n"
     ]
    }
   ],
   "source": [
    "import time, warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "torch.set_default_dtype(torch.float64)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "from xnn.common.data import load_dataset\n",
    "\n",
    "# Molecules with 2-4 heavy atoms, capped so the demo trains in minutes.\n",
    "data = load_dataset(\"ani1\", heavy_atoms=[2, 3, 4],\n",
    "                    max_molecules=60, max_conformations=60, units=\"eV\")[\"all\"]\n",
    "print(f\"loaded {len(data):,} conformations\")\n",
    "sizes = [len(s['atomic_numbers']) for s in data]\n",
    "elems = sorted(set(int(z) for s in data for z in s['atomic_numbers']))\n",
    "print(f\"elements {elems}  atoms/mol {min(sizes)}-{max(sizes)}  example SMILES: {data[0]['smiles']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8207834f",
   "metadata": {},
   "source": [
    "## 1. Self atomic energies + train/test split\n",
    "\n",
    "As in the ANI-1x `EnergyShifter`, subtract per-element self energies (least\n",
    "squares) so the network learns the residual. Then a random 90/10 train/test\n",
    "split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "973bc935",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:18:35.627019Z",
     "iopub.status.busy": "2026-07-20T04:18:35.626243Z",
     "iopub.status.idle": "2026-07-20T04:18:36.083208Z",
     "shell.execute_reply": "2026-07-20T04:18:36.082635Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self energies (eV): {1: -16.48, 6: -1036.08, 7: -1488.66, 8: -2045.56}\n",
      "train 5022  test 558\n"
     ]
    }
   ],
   "source": [
    "SPECIES = [1, 6, 7, 8]\n",
    "counts = np.array([[np.sum(s[\"atomic_numbers\"] == z) for z in SPECIES] for s in data], float)\n",
    "E = np.array([s[\"energy\"] for s in data])\n",
    "sae, *_ = np.linalg.lstsq(counts, E, rcond=None)\n",
    "print(\"self energies (eV):\", {z: round(float(e), 2) for z, e in zip(SPECIES, sae)})\n",
    "for s in data:\n",
    "    n = np.array([np.sum(s[\"atomic_numbers\"] == z) for z in SPECIES])\n",
    "    s[\"energy\"] = float(s[\"energy\"] - n @ sae)\n",
    "\n",
    "rng = np.random.default_rng(0)\n",
    "idx = rng.permutation(len(data)); cut = int(0.9 * len(data))\n",
    "train_structs = [data[i] for i in idx[:cut]]\n",
    "test_structs = [data[i] for i in idx[cut:]]\n",
    "print(f\"train {len(train_structs)}  test {len(test_structs)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01801b26",
   "metadata": {},
   "source": [
    "## 2. Train ANI (energy only)\n",
    "\n",
    "The ANI-1 dataset has no forces, so we train on energies alone\n",
    "(`force_weight=0`), using the ANI-1x AEV grid and a compact per-element MLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba95ff9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:18:36.084920Z",
     "iopub.status.busy": "2026-07-20T04:18:36.084555Z",
     "iopub.status.idle": "2026-07-20T04:41:35.745602Z",
     "shell.execute_reply": "2026-07-20T04:41:35.744664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANI parameters: 296,452  device cuda\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss 4.9704e-04\n",
      "trained 120 epochs in 1378.5s\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Subset\n",
    "from xnn.common.data import AtomicDataset\n",
    "from xnn.common.config import Config, ModelConfig, DataConfig, OptimConfig\n",
    "from xnn.common.train import Trainer\n",
    "\n",
    "CUTOFF = 5.2\n",
    "EPOCHS, BS, LR = 120, 64, 1e-3\n",
    "train_ds = AtomicDataset(train_structs, CUTOFF)\n",
    "test_ds = AtomicDataset(test_structs, CUTOFF)\n",
    "n_val = max(1, len(train_ds) // 10)\n",
    "val_idx = list(range(n_val)); tr_idx = list(range(n_val, len(train_ds)))\n",
    "\n",
    "cfg = Config(\n",
    "    model=ModelConfig(name=\"ani\", cutoff=CUTOFF,\n",
    "                      extra={\"species\": SPECIES, \"hidden\": [128, 128, 64],\n",
    "                             \"activation\": \"celu\"}),\n",
    "    data=DataConfig(cutoff=CUTOFF, batch_size=BS),\n",
    "    optim=OptimConfig(lr=LR, epochs=EPOCHS, energy_weight=1.0,\n",
    "                      force_weight=0.0, scheduler=\"plateau\"),\n",
    "    output_dir=\"runs/ani1_subset\",\n",
    ")\n",
    "trainer = Trainer(cfg, Subset(train_ds, tr_idx), Subset(train_ds, val_idx), test_ds)\n",
    "print(f\"ANI parameters: {sum(p.numel() for p in trainer.module.parameters()):,}  device {trainer.device}\")\n",
    "\n",
    "hist = {\"train\": [], \"val\": []}\n",
    "trainer._log = lambda ep, tr, va: (hist[\"train\"].append(tr.get(\"loss\")),\n",
    "                                   hist[\"val\"].append(va.get(\"loss\")))\n",
    "t0 = time.time(); trainer.fit()\n",
    "print(f\"trained {EPOCHS} epochs in {time.time()-t0:.1f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b5abdd",
   "metadata": {},
   "source": [
    "## 3. Energy correlation vs DFT (paper Fig. 4A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ff84673",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:41:35.748103Z",
     "iopub.status.busy": "2026-07-20T04:41:35.747929Z",
     "iopub.status.idle": "2026-07-20T04:41:37.035192Z",
     "shell.execute_reply": "2026-07-20T04:41:37.034326Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test energy RMSE: 145.4 meV = 3.354 kcal/mol\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 460x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from xnn.common.data import collate\n",
    "\n",
    "model = trainer.model.eval(); device = trainer.device\n",
    "EV2KCAL = 23.060541945329334\n",
    "E_pred, E_ref = [], []\n",
    "for batch in DataLoader(test_ds, batch_size=64, collate_fn=collate):\n",
    "    batch = batch.to(device)\n",
    "    with torch.no_grad():\n",
    "        E_pred.append(model(batch)[\"energy\"].cpu().numpy())\n",
    "    E_ref.append(batch.energy.cpu().numpy())\n",
    "E_pred, E_ref = np.concatenate(E_pred), np.concatenate(E_ref)\n",
    "rmse = np.sqrt(np.mean((E_pred - E_ref) ** 2))\n",
    "print(f\"test energy RMSE: {rmse*1e3:.1f} meV = {rmse*EV2KCAL:.3f} kcal/mol\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n",
    "ax.scatter(E_ref, E_pred, s=8, alpha=0.5)\n",
    "lim = [min(E_ref.min(), E_pred.min()), max(E_ref.max(), E_pred.max())]\n",
    "ax.plot(lim, lim, \"k--\", lw=1)\n",
    "ax.set_xlabel(\"DFT residual energy (eV)\"); ax.set_ylabel(\"ANI residual energy (eV)\")\n",
    "ax.set_title(f\"ANI-1 subset  RMSE {rmse*EV2KCAL:.3f} kcal/mol\")\n",
    "fig.tight_layout(); fig.savefig(\"ani1_correlation.png\", dpi=110); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0ab6cc",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "`load_dataset(\"ani1\", ...)` brings the original 20 M-conformation ANI-1 training\n",
    "set into the same one-line hub as every other `xnn` dataset, with heavy-atom\n",
    "subset selection and conformation caps for tractable experiments. Trained on a\n",
    "small subset, `xnn` ANI already reproduces DFT energies to well within chemical\n",
    "accuracy, the paper's headline result. At full scale with the published\n",
    "`ANI.ani1x()` architecture this is the ANI-1x potential verified against\n",
    "torchani in `examples/fidelity_checks/ani_verification.ipynb`."
   ]
  }
 ],
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    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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