{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b22003d",
   "metadata": {},
   "source": [
    "# Loading datasets with `xnn`: the data hub & `load_dataset()`\n",
    "\n",
    "`xnn.common.data.hub` gives you a **HuggingFace `load_dataset()`-style one-liner**\n",
    "for pulling standard atomistic benchmark datasets into `xnn`. One call\n",
    "**downloads** the raw files (cached and MD5-verified), **preprocesses** them into\n",
    "`xnn`' native structure dicts, and, optionally, hands you a ready-to-train\n",
    "`AtomicDataset`.\n",
    "\n",
    "```python\n",
    "from xnn.common.data import load_dataset\n",
    "train = load_dataset(\"rmd17\", molecule=\"aspirin\", split=\"train\", cutoff=5.0)\n",
    "```\n",
    "\n",
    "This notebook walks through:\n",
    "\n",
    "1. Discovering the available datasets\n",
    "2. The one-liner and what it returns\n",
    "3. Where files are cached\n",
    "4. The options: splits, folds, units, subsetting\n",
    "5. A quick look at the data (EDA + a 3-D render)\n",
    "6. Going from structure dicts to a training `DataLoader`\n",
    "7. A second built-in dataset: molecular dimers (`lode_dimers`)\n",
    "8. Registering **your own** dataset with the hub\n",
    "\n",
    "The running example is **rMD17** (revised MD17): ten small molecules with\n",
    "PBE/def2-SVP energies and forces, and five official 1000-structure train/test\n",
    "splits. Section 7 switches to a very different dataset: biomolecular dimers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c95efb7",
   "metadata": {},
   "source": [
    "## 0. Setup\n",
    "\n",
    "`float64` so downstream neighbour-list / graph tensors are double precision, and\n",
    "we silence the expected `torch.load` `FutureWarning` from `e3nn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f14e2c1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:15.576552Z",
     "iopub.status.busy": "2026-07-20T04:33:15.576442Z",
     "iopub.status.idle": "2026-07-20T04:33:17.447931Z",
     "shell.execute_reply": "2026-07-20T04:33:17.446865Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "torch.set_default_dtype(torch.float64)\n",
    "\n",
    "from xnn.common.data import load_dataset, list_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8665db65",
   "metadata": {},
   "source": [
    "## 1. What datasets are available?\n",
    "\n",
    "Builders register themselves under a short name; `list_datasets()` enumerates\n",
    "them. (Section 8 shows how to add your own.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1eefc926",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:17.450152Z",
     "iopub.status.busy": "2026-07-20T04:33:17.449849Z",
     "iopub.status.idle": "2026-07-20T04:33:17.455692Z",
     "shell.execute_reply": "2026-07-20T04:33:17.454846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ani1', 'ani1ccx', 'ani1x', 'ani2x', 'argon_md', 'lode_dimers', 'rmd17']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_datasets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8621df58",
   "metadata": {},
   "source": [
    "## 2. The one-liner\n",
    "\n",
    "Call `load_dataset(name, ...)`. With no `split` argument you get **all splits**\n",
    "as a mapping: for rMD17 that's `{\"train\", \"test\"}` (fold 1 by default), each a\n",
    "plain list of *structure dicts*.\n",
    "\n",
    "rMD17 requires a `molecule` argument. Below we pull **every** molecule into a\n",
    "dict keyed by name; the first call for each downloads and caches it\n",
    "(per-molecule files are 60–175 MB, ~1.1 GB for all ten). To grab just one, pass\n",
    "a single `molecule=`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e96d0a22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:17.457468Z",
     "iopub.status.busy": "2026-07-20T04:33:17.457353Z",
     "iopub.status.idle": "2026-07-20T04:33:22.800902Z",
     "shell.execute_reply": "2026-07-20T04:33:22.800200Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
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      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
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      "text/plain": [
       "rmd17:azobenzene train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
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     "output_type": "display_data"
    },
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       "rmd17:azobenzene test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
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      "text/plain": [
       "rmd17:toluene train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
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     "output_type": "display_data"
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    {
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       "rmd17:toluene test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
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      "text/plain": [
       "rmd17:aspirin train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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       "rmd17:aspirin test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
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      "text/plain": [
       "rmd17:benzene train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
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     "output_type": "display_data"
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      "text/plain": [
       "rmd17:benzene test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01ae3e89628747a38d93543b455f3a62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:naphthalene train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "rmd17:naphthalene test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
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     },
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     "output_type": "display_data"
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    {
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       "model_id": "43cb061d7b67415b90d5ae9e5126be89",
       "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": "e34f619b7895457b89a8c9de3fb6a41e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:malonaldehyde test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8646803376b44cc867de8f799781631",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:paracetamol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9215a040c754cf39348620446109e54",
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       "version_minor": 0
      },
      "text/plain": [
       "rmd17:paracetamol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7002b807a4674a998323cde18a9c6e3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:salicylic train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8687e22a21f4425887649ab7a1b2dce1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:salicylic test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a5fc668c2314d9a9b45405d93dc194f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:uracil train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efdeca80070a436abb49a5f1f4117e09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:uracil test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the molecule can be one of the following: ethanol, azobenzene, toluene, aspirin, benzene, naphthalene, malonaldehyde, paracetamol, salicylic_acid, uracil\n",
    "lists = [\"ethanol\", \"azobenzene\", \"toluene\", \"aspirin\", \"benzene\", \"naphthalene\", \"malonaldehyde\", \"paracetamol\", \"salicylic_acid\", \"uracil\"]\n",
    "splits = {k: load_dataset(\"rmd17\", molecule=k) for k in lists}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "327d1d43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:22.802816Z",
     "iopub.status.busy": "2026-07-20T04:33:22.802685Z",
     "iopub.status.idle": "2026-07-20T04:33:22.805904Z",
     "shell.execute_reply": "2026-07-20T04:33:22.805237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(splits[\"aspirin\"][\"train\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd69ac7",
   "metadata": {},
   "source": [
    "### Anatomy of a structure dict\n",
    "\n",
    "Each entry is the plain-dict format the rest of `xnn` consumes\n",
    "(`structure_to_graph`, `AtomicDataset`): positions in Å, integer atomic numbers,\n",
    "and- when the dataset provides them- `energy` (eV) and `forces` (eV/Å). rMD17\n",
    "is **molecular**, so there is no `cell`/`pbc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd162469",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:22.807226Z",
     "iopub.status.busy": "2026-07-20T04:33:22.807114Z",
     "iopub.status.idle": "2026-07-20T04:33:22.810593Z",
     "shell.execute_reply": "2026-07-20T04:33:22.809968Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys:         ['atomic_numbers', 'energy', 'forces', 'pos']\n",
      "atomic_numbers: [6 6 8 1 1 1 1 1 1]\n",
      "n_atoms:      9\n",
      "pos shape:    (9, 3)\n",
      "forces shape: (9, 3)\n",
      "energy [eV]:  -4209.784941392146\n"
     ]
    }
   ],
   "source": [
    "eth = splits[\"ethanol\"]\n",
    "s = eth[\"train\"][0]\n",
    "print(\"keys:        \", sorted(s))\n",
    "print(\"atomic_numbers:\", s[\"atomic_numbers\"])\n",
    "print(\"n_atoms:     \", len(s[\"atomic_numbers\"]))\n",
    "print(\"pos shape:   \", s[\"pos\"].shape)\n",
    "print(\"forces shape:\", s[\"forces\"].shape)\n",
    "print(\"energy [eV]: \", s[\"energy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a52b03c",
   "metadata": {},
   "source": [
    "### Caching\n",
    "\n",
    "The download is verified by MD5 and reused, so a second load is essentially free\n",
    "and works offline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bdc7c7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:22.811932Z",
     "iopub.status.busy": "2026-07-20T04:33:22.811822Z",
     "iopub.status.idle": "2026-07-20T04:33:22.966231Z",
     "shell.execute_reply": "2026-07-20T04:33:22.965624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8e8801d5a924396a774ed0bb6464338",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebf4f3b98b32439ba4daae153ada3b7b",
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       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cached reload: 0.151 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "t = time.perf_counter()\n",
    "_ = load_dataset(\"rmd17\", molecule=\"ethanol\")\n",
    "print(f\"cached reload: {time.perf_counter() - t:.3f} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9cfb35",
   "metadata": {},
   "source": [
    "## 3. Where does the data live?\n",
    "\n",
    "By default everything caches under the repository's `datasets/` directory, one\n",
    "subfolder per dataset (`datasets/rmd17/raw/`). Override it per call with\n",
    "`cache_dir=...`, or globally with the `XNN_DATASETS` (or `XNN_CACHE`)\n",
    "environment variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d8c312c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:22.968008Z",
     "iopub.status.busy": "2026-07-20T04:33:22.967881Z",
     "iopub.status.idle": "2026-07-20T04:33:22.972263Z",
     "shell.execute_reply": "2026-07-20T04:33:22.971690Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache root: /D3/sina/xnn/datasets\n",
      "\n",
      "  rmd17_ethanol.npz              67.20 MB\n",
      "  index_test_01.csv               0.01 MB\n",
      "  index_test_02.csv               0.01 MB\n",
      "  index_test_03.csv               0.01 MB\n",
      "  index_test_04.csv               0.01 MB\n",
      "  index_train_01.csv              0.01 MB\n",
      "  index_train_02.csv              0.01 MB\n",
      "  index_train_03.csv              0.01 MB\n",
      "  index_train_04.csv              0.01 MB\n"
     ]
    }
   ],
   "source": [
    "from xnn.common.data.hub import default_cache_dir\n",
    "\n",
    "raw = default_cache_dir() / \"rmd17\" / \"raw\"\n",
    "print(\"cache root:\", default_cache_dir())\n",
    "print()\n",
    "for p in sorted(raw.glob(\"*ethanol*\")) + sorted(raw.glob(\"*.csv\")):\n",
    "    print(f\"  {p.name:28s} {p.stat().st_size / 1e6:7.2f} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a00a869",
   "metadata": {},
   "source": [
    "## 4. Options\n",
    "\n",
    "**Units.** Upstream rMD17 is in kcal/mol; the hub converts to **eV** by default\n",
    "(the convention used across `xnn` and by MACE/NequIP). Pass `units=\"kcal/mol\"`\n",
    "to keep the raw magnitudes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9dd85f6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:22.973833Z",
     "iopub.status.busy": "2026-07-20T04:33:22.973722Z",
     "iopub.status.idle": "2026-07-20T04:33:23.258721Z",
     "shell.execute_reply": "2026-07-20T04:33:23.258073Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1 [00:00<?, ? struct/s]"
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     },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ee6d26c882b4508b11341919b6b62cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cd8c99af19d4b64a847decec078f4e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3b8c563958745a89c96b0927ceffb27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eV:         -4209.7849\n",
      "kcal/mol:  -97079.9222\n",
      "ratio:       23.060542   (1 eV = 23.0605 kcal/mol)\n"
     ]
    }
   ],
   "source": [
    "ev = load_dataset(\"rmd17\", molecule=\"ethanol\", split=\"train\", n_train=1)[0][\"energy\"]\n",
    "kcal = load_dataset(\"rmd17\", molecule=\"ethanol\", split=\"train\",\n",
    "                    n_train=1, units=\"kcal/mol\")[0][\"energy\"]\n",
    "print(f\"eV:       {ev:12.4f}\")\n",
    "print(f\"kcal/mol: {kcal:12.4f}\")\n",
    "print(f\"ratio:    {kcal / ev:12.6f}   (1 eV = 23.0605 kcal/mol)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63512b47",
   "metadata": {},
   "source": [
    "**Splits and folds.** A `split` argument returns just that list. rMD17 ships\n",
    "five official train/test splits that can be picked one with `fold=1..5`.\n",
    "`split=\"all\"` returns every conformation (100k) with no index filtering, for\n",
    "custom splitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "314bea9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:23.260535Z",
     "iopub.status.busy": "2026-07-20T04:33:23.260338Z",
     "iopub.status.idle": "2026-07-20T04:33:24.248854Z",
     "shell.execute_reply": "2026-07-20T04:33:24.247868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "230fb029eb2f49479025f27e08fab52d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ffde3350a801489cad4a2f9a08d94f01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 1: 1000 train structures, first energy = -4209.7849 eV\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "092d18a8a7164d20b3b86b00f225a570",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a8d1755c6b845e0b6ea7cf806fd7772",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 2: 1000 train structures, first energy = -4209.5620 eV\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50715654809d4e7d806f51dd6933c676",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6bdd1684bd045eab16a95b6006ba3f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 3: 1000 train structures, first energy = -4209.4255 eV\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d117e5fe688e42dea58823504df43e9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol all:   0%|          | 0/100000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "split=\"all\": 100000 conformations\n"
     ]
    }
   ],
   "source": [
    "for fold in (1, 2, 3):\n",
    "    tr = load_dataset(\"rmd17\", molecule=\"ethanol\", split=\"train\", fold=fold)\n",
    "    print(f\"fold {fold}: {len(tr)} train structures, \"\n",
    "          f\"first energy = {tr[0]['energy']:.4f} eV\")\n",
    "\n",
    "allc = load_dataset(\"rmd17\", molecule=\"ethanol\", split=\"all\")\n",
    "print(f'\\nsplit=\"all\": {len(allc)} conformations')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200e6106",
   "metadata": {},
   "source": [
    "**Subsetting.** `n_train` / `n_test` truncate a split to the first *n*\n",
    "structures which can be handy for a quick smoke test. (The authors warn against\n",
    "training on **more** than 1000 correlated MD frames, which the official splits\n",
    "respect.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bcbbbcb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:24.250785Z",
     "iopub.status.busy": "2026-07-20T04:33:24.250665Z",
     "iopub.status.idle": "2026-07-20T04:33:24.390727Z",
     "shell.execute_reply": "2026-07-20T04:33:24.390068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42e45379833c472e9721414595ee1080",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/50 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "386ed1d07c4342ea869b81e22f757725",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/25 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'train': 50, 'test': 25}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small = load_dataset(\"rmd17\", molecule=\"ethanol\", n_train=50, n_test=25)\n",
    "{k: len(v) for k, v in small.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c54cced",
   "metadata": {},
   "source": [
    "## 5. A quick look at the data\n",
    "\n",
    "The structure dicts can be converted to NumPy arrays to help with exploratory\n",
    "data analysis (EDA) and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad3b15ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:24.392933Z",
     "iopub.status.busy": "2026-07-20T04:33:24.392815Z",
     "iopub.status.idle": "2026-07-20T04:33:24.726097Z",
     "shell.execute_reply": "2026-07-20T04:33:24.725265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x320 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = splits[\"ethanol\"][\"train\"]\n",
    "epa = np.array([s[\"energy\"] / len(s[\"atomic_numbers\"]) for s in train])\n",
    "fmax = np.array([np.abs(s[\"forces\"]).max() for s in train])\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(9, 3.2))\n",
    "ax[0].hist(epa, bins=40); ax[0].set(xlabel=\"energy / atom [eV]\", title=\"per-atom energy\")\n",
    "ax[1].hist(fmax, bins=40); ax[1].set(xlabel=\"max |force| [eV/Å]\", title=\"force magnitude\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0882034a",
   "metadata": {},
   "source": [
    "A single conformation, coloured by element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb910717",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:24.727929Z",
     "iopub.status.busy": "2026-07-20T04:33:24.727808Z",
     "iopub.status.idle": "2026-07-20T04:33:25.034000Z",
     "shell.execute_reply": "2026-07-20T04:33:25.032906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ELEM = {1: (\"H\", \"#c8c8c8\", 60), 6: (\"C\", \"#404040\", 140),\n",
    "         7: (\"N\", \"#3050f8\", 140), 8: (\"O\", \"#ff2010\", 140)}\n",
    "\n",
    "s = train[0]\n",
    "fig = plt.figure(figsize=(4.5, 4.5))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "for z in np.unique(s[\"atomic_numbers\"]):\n",
    "    mask = s[\"atomic_numbers\"] == z\n",
    "    sym, color, size = _ELEM[int(z)]\n",
    "    ax.scatter(*s[\"pos\"][mask].T, c=color, s=size, edgecolors=\"k\", label=sym)\n",
    "ax.set(title=\"ethanol — conformation 0\", xlabel=\"x [Å]\", ylabel=\"y [Å]\", zlabel=\"z [Å]\")\n",
    "ax.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35bdd49",
   "metadata": {},
   "source": [
    "## 6. From structure dicts to a training `DataLoader`\n",
    "\n",
    "Pass `cutoff=` and `load_dataset` wraps each split in an `AtomicDataset`, which\n",
    "builds the neighbour-list graph (`AtomicGraph`) for every structure on first\n",
    "access. That's the object every `xnn` model consumes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "263de2e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:25.035927Z",
     "iopub.status.busy": "2026-07-20T04:33:25.035800Z",
     "iopub.status.idle": "2026-07-20T04:33:25.214703Z",
     "shell.execute_reply": "2026-07-20T04:33:25.213597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "853c354ddcc94009afc826598751145e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol train:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac41cdc9f7494ac988f249d49f4c4de2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "rmd17:ethanol test:   0%|          | 0/1000 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtomicDataset | len: 1000\n",
      "graph 0: 9 atoms, 72 edges, cell=None\n"
     ]
    }
   ],
   "source": [
    "ds = load_dataset(\"rmd17\", molecule=\"ethanol\", cutoff=5.0)\n",
    "train_ds = ds[\"train\"]\n",
    "print(type(train_ds).__name__, \"| len:\", len(train_ds))\n",
    "\n",
    "g = train_ds[0]\n",
    "print(f\"graph 0: {g.num_nodes} atoms, {g.num_edges} edges, cell={g.cell}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf3b9ef",
   "metadata": {},
   "source": [
    "Batch several graphs into one with xnn' `collate`. A standard PyTorch `DataLoader`\n",
    "handles shuffling and batching (`batch_size=1` effectively disables batching)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ec33002",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:25.216607Z",
     "iopub.status.busy": "2026-07-20T04:33:25.216475Z",
     "iopub.status.idle": "2026-07-20T04:33:25.222782Z",
     "shell.execute_reply": "2026-07-20T04:33:25.222119Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batched graph: 8 structures, 72 atoms\n",
      "  energy: (8,)   forces: (72, 3)\n",
      "  batch vector maps each atom -> its structure: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1] ...\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from xnn.common.data import collate\n",
    "\n",
    "loader = DataLoader(train_ds, batch_size=8, shuffle=True, collate_fn=collate)\n",
    "batch = next(iter(loader))\n",
    "print(f\"batched graph: {batch.num_graphs} structures, {batch.num_nodes} atoms\")\n",
    "print(f\"  energy: {tuple(batch.energy.shape)}   forces: {tuple(batch.forces.shape)}\")\n",
    "print(f\"  batch vector maps each atom -> its structure: {batch.batch[:12].tolist()} ...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68acc015",
   "metadata": {},
   "source": [
    "This `loader` is exactly what you'd hand to an xnn `Trainer`. See the per-model\n",
    "`*_train_test.ipynb` notebooks for the full training loop."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d068f70",
   "metadata": {},
   "source": [
    "## 7. A second built-in dataset: molecular dimers (`lode_dimers`)\n",
    "\n",
    "The same one-liner pulls a very different dataset. **`lode_dimers`** is the\n",
    "Materials Cloud set behind the LODE non-bonded-interactions work\n",
    "([10.24435/materialscloud:23-99](https://doi.org/10.24435/materialscloud:23-99)):\n",
    "biomolecular sidechain **dimers** with DFT energies and forces. Each pair is\n",
    "tagged by the polarity of its two fragments: `CC` (charged–charged), `CP`\n",
    "(charged–polar), `PP` (polar–polar), plus apolar variants. Every dimer is\n",
    "scanned over a range of separations, making it a natural testbed for\n",
    "**long-range** interactions.\n",
    "\n",
    "Pick a subset with `subset=`, filter the biomolecular dimers by class with\n",
    "`label=`, and attach each frame's metadata with `return_info=True`.\n",
    "\n",
    "> **Heads-up:** the `bio` subset (`bio_dimers.xyz`) is ~66 MB; the first call\n",
    "> downloads it (cached thereafter). Reading extxyz needs the `ase` extra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "837344dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:25.225041Z",
     "iopub.status.busy": "2026-07-20T04:33:25.224855Z",
     "iopub.status.idle": "2026-07-20T04:33:35.598192Z",
     "shell.execute_reply": "2026-07-20T04:33:35.597302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e021d50c41a47a6a16ec2178a9c0dc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "lode_dimers:bio [CC]:   0%|          | 0/2392 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "charged-charged (CC) dimers: 2392\n",
      "structure keys: ['atomic_numbers', 'cell', 'energy', 'forces', 'info', 'pbc', 'pos']\n",
      "n_atoms: 23 | box: 30 Å (large --> effectively isolated)\n",
      "info: {'label': 'CC', 'dimer_id': np.int64(0), 'distance': np.float64(6.630174209406314), 'energyA': np.float64(-7741.80993019298), 'energyB': np.float64(-6221.39955938734), 'chargeA': np.int64(1), 'chargeB': np.int64(-1)}\n"
     ]
    }
   ],
   "source": [
    "cc = load_dataset(\"lode_dimers\", subset=\"bio\", label=\"CC\",\n",
    "                  split=\"all\", return_info=True)\n",
    "print(f\"charged-charged (CC) dimers: {len(cc)}\")\n",
    "\n",
    "s = cc[0]\n",
    "print(\"structure keys:\", sorted(s))\n",
    "print(\"n_atoms:\", len(s[\"atomic_numbers\"]),\n",
    "      \"| box:\", f'{s[\"cell\"][0, 0]:.0f} Å (large --> effectively isolated)')\n",
    "meta = (\"label\", \"dimer_id\", \"distance\", \"energyA\", \"energyB\", \"chargeA\", \"chargeB\")\n",
    "print(\"info:\", {k: s[\"info\"][k] for k in meta})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583a9522",
   "metadata": {},
   "source": [
    "### Binding-energy curves\n",
    "\n",
    "`return_info=True` attaches each frame's metadata, including the\n",
    "isolated-monomer energies `energyA` / `energyB`. The **binding energy** \n",
    "$E_{AB} - E_A - E_B$ plotted against `distance` traces the attractive tail a long-range\n",
    "model must capture which would be the steepest for the charged–charged pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff40f4a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:35.600226Z",
     "iopub.status.busy": "2026-07-20T04:33:35.599882Z",
     "iopub.status.idle": "2026-07-20T04:33:51.023945Z",
     "shell.execute_reply": "2026-07-20T04:33:51.022962Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total biomolecular dimer frames: 29783\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x330 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "# read the biomolecular dimers once, with per-frame metadata\n",
    "bio = load_dataset(\"lode_dimers\", subset=\"bio\", split=\"all\",\n",
    "                   return_info=True, quiet=True)\n",
    "print(f\"total biomolecular dimer frames: {len(bio)}\")\n",
    "\n",
    "curves = {c: defaultdict(list) for c in (\"CC\", \"CP\", \"PP\")}\n",
    "for s in bio:\n",
    "    i = s[\"info\"]\n",
    "    if i[\"label\"] in curves:\n",
    "        binding = s[\"energy\"] - i[\"energyA\"] - i[\"energyB\"]\n",
    "        curves[i[\"label\"]][i[\"dimer_id\"]].append((i[\"distance\"], binding))\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(11, 3.3))\n",
    "for ax, cls in zip(axes, (\"CC\", \"CP\", \"PP\")):\n",
    "    for did in sorted(curves[cls])[:4]:\n",
    "        d, be = np.array(sorted(curves[cls][did])).T\n",
    "        ax.plot(d, be, \"o-\", ms=3, lw=1, label=f\"dimer {did}\")\n",
    "    ax.axhline(0, color=\"k\", lw=0.6, ls=\":\")\n",
    "    ax.set(title=f\"{cls} dimers\", xlabel=\"separation [Å]\",\n",
    "           ylabel=\"binding energy [eV]\")\n",
    "    ax.legend(fontsize=7)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f36860",
   "metadata": {},
   "source": [
    "Just like rMD17, add `cutoff=` for a ready-to-train `AtomicDataset`. Unlike\n",
    "rMD17 these frames are **periodic** (a large cubic box), so the graphs carry a\n",
    "`cell` and `pbc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9c2bce1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-20T04:33:51.025942Z",
     "iopub.status.busy": "2026-07-20T04:33:51.025809Z",
     "iopub.status.idle": "2026-07-20T04:34:00.631304Z",
     "shell.execute_reply": "2026-07-20T04:34:00.630695Z"
    }
   },
   "outputs": [
    {
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       "lode_dimers:bio [CC]:   0%|          | 0/2392 [00:00<?, ? struct/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2392 CC dimer graphs\n",
      "graph 0: 23 atoms, 294 edges, periodic=True\n"
     ]
    }
   ],
   "source": [
    "cc_ds = load_dataset(\"lode_dimers\", subset=\"bio\", label=\"CC\", cutoff=5.0)[\"all\"]\n",
    "g = cc_ds[0]\n",
    "print(f\"{len(cc_ds)} CC dimer graphs\")\n",
    "print(f\"graph 0: {g.num_nodes} atoms, {g.num_edges} edges, \"\n",
    "      f\"periodic={bool(g.pbc.all())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3820cac8",
   "metadata": {},
   "source": [
    "Other subsets need no `label`: `monomers`, the point-charge toy systems\n",
    "`point_charges_coulomb` (pure $1/r$) and `point_charges_dispersion` (pure\n",
    "$1/r^6$), and `xenon` (Xe dimers & trimers). For example:"
   ]
  },
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       "lode_dimers:xenon:   0%|          | 0/100 [00:00<?, ? struct/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xenon subset: 100 structures; sizes = [2, 3] atoms (dimers & trimers)\n"
     ]
    }
   ],
   "source": [
    "xe = load_dataset(\"lode_dimers\", subset=\"xenon\", split=\"all\")\n",
    "sizes = sorted(set(len(s[\"atomic_numbers\"]) for s in xe))\n",
    "print(f\"xenon subset: {len(xe)} structures; sizes = {sizes} atoms (dimers & trimers)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f29caf7",
   "metadata": {},
   "source": [
    "## 8. Registering your own dataset\n",
    "\n",
    "The hub is extensible: subclass `DatasetBuilder`, implement `load(...)` to return\n",
    "`{split: [structure_dict, ...]}` (or a single list), and call\n",
    "`register_dataset(...)`. It's then reachable through the same `load_dataset()`\n",
    "entry point.\n",
    "\n",
    "Here's a tiny in-memory example (a stretched H–H dimer) so you can see the\n",
    "whole contract without a download:"
   ]
  },
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   "execution_count": 19,
   "id": "d4f8479e",
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    {
     "data": {
      "text/plain": [
       "['ani1',\n",
       " 'ani1ccx',\n",
       " 'ani1x',\n",
       " 'ani2x',\n",
       " 'argon_md',\n",
       " 'lode_dimers',\n",
       " 'rmd17',\n",
       " 'toy_dimer']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xnn.common.data.hub import DatasetBuilder, register_dataset\n",
    "\n",
    "\n",
    "class ToyDimer(DatasetBuilder):\n",
    "    \"\"\"A two-atom H2 dimer scanned over a few bond lengths (no download).\"\"\"\n",
    "\n",
    "    name = \"toy_dimer\"\n",
    "    description = \"H2 dimer bond-length scan (in-memory demo).\"\n",
    "\n",
    "    def load(self, *, split=None, cache_dir, n=5, **kwargs):\n",
    "        structures = []\n",
    "        for d in np.linspace(0.5, 1.5, n):\n",
    "            structures.append({\n",
    "                \"pos\": np.array([[0.0, 0.0, 0.0], [0.0, 0.0, d]]),\n",
    "                \"atomic_numbers\": np.array([1, 1]),\n",
    "                \"energy\": float((d - 0.74) ** 2),   # toy harmonic well at r_eq\n",
    "            })\n",
    "        splits = {\"train\": structures}\n",
    "        return splits if split is None else splits[split]\n",
    "\n",
    "\n",
    "register_dataset(ToyDimer())\n",
    "list_datasets()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "id": "5d5470fa",
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    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bond = 0.50 Å   energy = 0.0576\n",
      "bond = 1.00 Å   energy = 0.0676\n",
      "bond = 1.50 Å   energy = 0.5776\n",
      "\n",
      "AtomicDataset: 3 graphs; 2 atoms each\n"
     ]
    }
   ],
   "source": [
    "toy = load_dataset(\"toy_dimer\", n=3)\n",
    "for s in toy[\"train\"]:\n",
    "    print(f\"bond = {s['pos'][1, 2]:.2f} Å   energy = {s['energy']:.4f}\")\n",
    "\n",
    "# and it composes with cutoff= just like the built-ins:\n",
    "toy_ds = load_dataset(\"toy_dimer\", n=3, cutoff=2.0)[\"train\"]\n",
    "print(\"\\nAtomicDataset:\", len(toy_ds), \"graphs;\", toy_ds[0].num_nodes, \"atoms each\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511d658f",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- **`load_dataset(name, ...)`**: download + preprocess in one call; returns\n",
    "  structure-dict lists, or `AtomicDataset`s when you pass `cutoff=`.\n",
    "- **`list_datasets()`**: what's registered.\n",
    "- Data caches under **`datasets/<name>/`** (override via `cache_dir=` or\n",
    "  `XNN_DATASETS`); downloads are MD5-verified and reused.\n",
    "- **rMD17** knobs: `molecule`, `split` (`train`/`test`/`all`), `fold` (1–5),\n",
    "  `units` (`eV`/`kcal/mol`), `n_train`/`n_test`.\n",
    "- **lode_dimers** knobs: `subset` (`bio` / `monomers` / `point_charges_coulomb`\n",
    "  / `point_charges_dispersion` / `xenon`), `label` (`CC`/`CP`/`PP`/…),\n",
    "  `return_info` (attach per-frame metadata), `split` (`all`).\n",
    "- Add datasets by subclassing **`DatasetBuilder`** + **`register_dataset`**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ac5c65",
   "metadata": {},
   "source": [
    "## Bundled and large datasets: `argon_md`, `bio_scan`, and `ani1`\n",
    "\n",
    "Besides `rmd17` and `lode_dimers`, the hub also serves:\n",
    "\n",
    "- **`argon_md`**: periodic argon configurations with energies, forces and stress, *bundled* with the repository (no download); the dataset behind the `*_argon_*` example notebooks.\n",
    "- **`lode_dimers` `subset=\"bio_scan\"`**: a curated charged/polar dimer distance scan (bundled), used by the long-range (LES / BAMBOO) examples.\n",
    "- **`ani1`**: the original 20 M-conformation ANI-1 training set (Smith *et al.* 2017); one 4.8 GB archive downloaded once, with heavy-atom subset selection and conformation caps for tractable experiments."
   ]
  },
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     "output_type": "stream",
     "text": [
      "argon_md: {'train': 200, 'test': 50} | keys: ['atomic_numbers', 'cell', 'energy', 'forces', 'pbc', 'pos', 'stress']\n"
     ]
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     "text": [
      "bio_scan: 39 frames; ['CC', 'CP', 'PP']\n",
      "registered datasets: ['ani1', 'ani1ccx', 'ani1x', 'ani2x', 'argon_md', 'lode_dimers', 'rmd17', 'toy_dimer']\n"
     ]
    }
   ],
   "source": [
    "# argon_md: bundled, loads offline\n",
    "argon = load_dataset(\"argon_md\")\n",
    "print(\"argon_md:\", {k: len(v) for k, v in argon.items()},\n",
    "      \"| keys:\", sorted(argon[\"train\"][0]))\n",
    "\n",
    "# lode_dimers bio_scan: bundled charged/polar dimer distance scan\n",
    "scan = load_dataset(\"lode_dimers\", subset=\"bio_scan\", split=\"all\",\n",
    "                    return_info=True)\n",
    "print(\"bio_scan:\", len(scan), \"frames;\",\n",
    "      sorted({d['info']['label'] for d in scan}))\n",
    "\n",
    "# ani1: the full ANI-1 training set (downloads 4.8 GB on first call);\n",
    "# uncomment to fetch a small subset:\n",
    "# ani = load_dataset(\"ani1\", heavy_atoms=[2, 3], max_molecules=20,\n",
    "#                    max_conformations=20)\n",
    "print(\"registered datasets:\", list_datasets())"
   ]
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