{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18300b5e",
   "metadata": {},
   "source": [
    "# Fine-tuning universal models\n",
    "\n",
    "This notebook is the worked example from the\n",
    "[Fine-tuning universal models](https://aitomistic.com/mlatom/tutorial_finetuning.html)\n",
    "tutorial, with the data set bundled so it can be run as it stands.\n",
    "\n",
    "We fine-tune **AIQM2** on 100 nitromethane geometries with reference energies and\n",
    "forces, look at what training wrote, and check the result.\n",
    "\n",
    "**What you need installed:** MLatom, and the `xtb` and `dftd4` programs — AIQM2 is\n",
    "built from a GFN2-xTB\\* baseline, a neural-network correction and a D4 dispersion\n",
    "term, and fine-tuning has to evaluate the first and the third over your geometries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ca4d87d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:41.164053Z",
     "iopub.status.busy": "2026-08-11T07:37:41.163591Z",
     "iopub.status.idle": "2026-08-11T07:37:42.577383Z",
     "shell.execute_reply": "2026-08-11T07:37:42.576215Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLatom 3.23.5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import mlatom as ml\n",
    "\n",
    "print('MLatom', ml.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e952d8e",
   "metadata": {},
   "source": [
    "## The reference data\n",
    "\n",
    "`CH3NO2_100.json` holds 100 nitromethane geometries, each with an energy and\n",
    "forces. Your own data needs the same: a `molecular_database` whose molecules carry\n",
    "geometries and a reference energy, plus forces if you want the model to reproduce\n",
    "those too. Energies are in Hartree and gradients in Hartree/Å."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "968a7ecc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:42.582456Z",
     "iopub.status.busy": "2026-08-11T07:37:42.581819Z",
     "iopub.status.idle": "2026-08-11T07:37:42.603442Z",
     "shell.execute_reply": "2026-08-11T07:37:42.602204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 molecules, 7 atoms each\n",
      "elements: ['C', 'H', 'N', 'O']\n",
      "energies: -244.6433 .. -244.6432 Hartree\n",
      "gradients on the first molecule: (7, 3)\n"
     ]
    }
   ],
   "source": [
    "db = ml.data.molecular_database.load('CH3NO2_100.json', format='json')\n",
    "\n",
    "print(f'{len(db)} molecules, {len(db[0].atoms)} atoms each')\n",
    "print('elements:', sorted(set(db[0].element_symbols)))\n",
    "\n",
    "energies = np.array(db.get_properties('energy'))\n",
    "print(f'energies: {energies.min():.4f} .. {energies.max():.4f} Hartree')\n",
    "print('gradients on the first molecule:',\n",
    "      np.shape(db[0].get_xyz_vectorial_properties('energy_gradients')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af645fdc",
   "metadata": {},
   "source": [
    "## Fine-tuning\n",
    "\n",
    "One call. Two things are worth pointing at:\n",
    "\n",
    "- `dispersion_kwargs` names the dispersion term. It is **subtracted from your\n",
    "  reference energies** and **added back when the model predicts** — the same term\n",
    "  in both places, so your reference energies are reproduced whichever you choose.\n",
    "  There is no default, because any default would quietly do the wrong thing for\n",
    "  somebody. AIQM2's own term is D4 with the ωB97X parameters.\n",
    "- `model_index` is given when you set the model up, not to `train()`. It selects\n",
    "  which of the eight pretrained networks you start from; leaving it out fine-tunes\n",
    "  all eight, which is about eight times slower but gives you the spread between\n",
    "  members as an uncertainty estimate.\n",
    "\n",
    "`max_epochs` is set low here so the notebook runs quickly. It is not a\n",
    "recommendation — how long you need depends on your data, so watch the loss rather\n",
    "than trust a number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9a542e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:42.607185Z",
     "iopub.status.busy": "2026-08-11T07:37:42.606869Z",
     "iopub.status.idle": "2026-08-11T07:37:52.070262Z",
     "shell.execute_reply": "2026-08-11T07:37:52.068245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start retraining on model 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIQM2 TL model saved in /home/dral/tlfigs/nb/my_tl_model\n"
     ]
    }
   ],
   "source": [
    "model = ml.models.methods(method='AIQM2', model_index=0)\n",
    "\n",
    "model.train(\n",
    "    molecular_database=db,\n",
    "    property_to_learn='energy',\n",
    "    xyz_derivative_property_to_learn='energy_gradients',   # omit to fit energies only\n",
    "    dispersion_kwargs={'method': 'd4', 'functional': 'wb97x'},\n",
    "    file_to_save_model='my_tl_model/',\n",
    "    hyperparameters={'max_epochs': 20},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6235370",
   "metadata": {},
   "source": [
    "The last line printed is what the fine-tuned model is made of. It should read\n",
    "\n",
    "```text\n",
    "Fine-tuned model composition: E = GFN2-xTB* + dNN + D4(wb97x)\n",
    "```\n",
    "\n",
    "which is the same decomposition as the equation at the top of the tutorial:\n",
    "a baseline, a neural network, and a dispersion term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0731abb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:52.075091Z",
     "iopub.status.busy": "2026-08-11T07:37:52.074644Z",
     "iopub.status.idle": "2026-08-11T07:37:52.081664Z",
     "shell.execute_reply": "2026-08-11T07:37:52.079503Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cv0.pt', 'delta_db.h5', 'tree.json']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(os.listdir('my_tl_model')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1f8ba5",
   "metadata": {},
   "source": [
    "## What training wrote\n",
    "\n",
    "`delta_db.h5` is one file holding your reference energies, every term that was\n",
    "subtracted from them, and the delta labels the network was actually fitted to.\n",
    "Each term is kept under its own name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99d916f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:52.086074Z",
     "iopub.status.busy": "2026-08-11T07:37:52.085640Z",
     "iopub.status.idle": "2026-08-11T07:37:52.116608Z",
     "shell.execute_reply": "2026-08-11T07:37:52.114995Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label sources in this database:\n",
      "  delta          {'method': 'aiqm2', 'baseline': 'GFN2-xTB*', 'dispersion': {'method': 'd4', 'functional': 'wb97x'}, 'dispersion_program_version': 'dftd4 version 4.2.0', 'gradients_subtracted': True, 'target_property': 'energy', 'role': 'delta'}\n",
      "  dispersion     {'method': 'd4', 'functional': 'wb97x', 'role': 'dispersion'}\n",
      "  gfn2xtbstar    {'role': 'baseline', 'method': 'GFN2-xTB*'}\n",
      "  target         {'role': 'reference', 'property': 'energy'}\n"
     ]
    }
   ],
   "source": [
    "prepared = ml.data.molecular_database.load('my_tl_model/delta_db.h5')\n",
    "\n",
    "print('label sources in this database:')\n",
    "for name, spec in sorted(prepared.label_sources.items()):\n",
    "    print(f'  {name:14s} {spec}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eaa2989e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:52.121140Z",
     "iopub.status.busy": "2026-08-11T07:37:52.120706Z",
     "iopub.status.idle": "2026-08-11T07:37:52.130213Z",
     "shell.execute_reply": "2026-08-11T07:37:52.128581Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reference   E          =    -244.643329 Hartree\n",
      "baseline    GFN2-xTB*  =     -14.618581\n",
      "dispersion  D4(wb97x)  =      -0.006004\n",
      "delta       what the NN learns =    -230.018744\n",
      "\n",
      "E - baseline - dispersion == delta: True\n"
     ]
    }
   ],
   "source": [
    "mol = prepared[0]\n",
    "\n",
    "reference  = float(mol.energy)\n",
    "baseline   = float(mol.get_property('gfn2xtbstar.energy'))\n",
    "dispersion = float(mol.get_property('dispersion.energy'))\n",
    "delta      = float(mol.delta_energy)\n",
    "\n",
    "print(f'reference   E          = {reference:14.6f} Hartree')\n",
    "print(f'baseline    GFN2-xTB*  = {baseline:14.6f}')\n",
    "print(f'dispersion  D4(wb97x)  = {dispersion:14.6f}')\n",
    "print(f'delta       what the NN learns = {delta:14.6f}')\n",
    "print()\n",
    "print('E - baseline - dispersion == delta:',\n",
    "      np.isclose(reference - baseline - dispersion, delta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54cf408a",
   "metadata": {},
   "source": [
    "That last line is the point of keeping the parts and not only their difference:\n",
    "$\\Delta E = E - E_\\text{dispersion} - E_\\text{baseline}$ is checkable, molecule by\n",
    "molecule. It also means the baseline — the expensive part of preparing the data —\n",
    "is already computed if you want to try something else on the same geometries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3eff6a4",
   "metadata": {},
   "source": [
    "## Using the fine-tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7affa7cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:52.135246Z",
     "iopub.status.busy": "2026-08-11T07:37:52.134805Z",
     "iopub.status.idle": "2026-08-11T07:37:58.408428Z",
     "shell.execute_reply": "2026-08-11T07:37:58.406190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model loaded from /home/dral/tlfigs/nb/my_tl_model/cv0.pt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max  |E_predicted - E_reference| = 0.000066 Hartree\n",
      "mean |E_predicted - E_reference| = 0.000027 Hartree\n"
     ]
    }
   ],
   "source": [
    "tuned = ml.models.aiqm2.load('my_tl_model/')\n",
    "\n",
    "check = db.copy(atomic_labels=['xyz_coordinates'], molecular_labels=[])\n",
    "tuned.predict(molecular_database=check, calculate_energy=True)\n",
    "\n",
    "error = np.abs(np.array(check.get_properties('energy'))\n",
    "               - np.array(db.get_properties('energy')))\n",
    "print(f'max  |E_predicted - E_reference| = {error.max():.6f} Hartree')\n",
    "print(f'mean |E_predicted - E_reference| = {error.mean():.6f} Hartree')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2a074b",
   "metadata": {},
   "source": [
    "`molecular_labels=[]` matters here. Without it the copy carries your reference\n",
    "energies across, and a molecule the model failed on would keep the reference value\n",
    "and appear to have zero error.\n",
    "\n",
    "For comparison, the model before fine-tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16eaf3b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:37:58.413301Z",
     "iopub.status.busy": "2026-08-11T07:37:58.412788Z",
     "iopub.status.idle": "2026-08-11T07:38:04.737166Z",
     "shell.execute_reply": "2026-08-11T07:38:04.735053Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock AIQM2      mean |dE| = 0.166710 Hartree  ( 104.612 kcal/mol)\n",
      "fine-tuned       mean |dE| = 0.000027 Hartree  (   0.017 kcal/mol)\n"
     ]
    }
   ],
   "source": [
    "stock = ml.models.methods(method='AIQM2', model_index=0)\n",
    "\n",
    "before = db.copy(atomic_labels=['xyz_coordinates'], molecular_labels=[])\n",
    "stock.predict(molecular_database=before, calculate_energy=True)\n",
    "\n",
    "error_before = np.abs(np.array(before.get_properties('energy'))\n",
    "                      - np.array(db.get_properties('energy')))\n",
    "print(f'stock AIQM2      mean |dE| = {error_before.mean():.6f} Hartree'\n",
    "      f'  ({error_before.mean() * 627.5094740631:8.3f} kcal/mol)')\n",
    "print(f'fine-tuned       mean |dE| = {error.mean():.6f} Hartree'\n",
    "      f'  ({error.mean() * 627.5094740631:8.3f} kcal/mol)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ea2059",
   "metadata": {},
   "source": [
    "Plotted, with the constant offset taken out of both so the *shape* is what you\n",
    "see. The page shows the same comparison on a set with a wider energy range;\n",
    "this one is your training data, so it is the easy case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f016c73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:04.742543Z",
     "iopub.status.busy": "2026-08-11T07:38:04.741820Z",
     "iopub.status.idle": "2026-08-11T07:38:05.329159Z",
     "shell.execute_reply": "2026-08-11T07:38:05.328344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 460x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "K = 627.5094740631\n",
    "reference = np.array(db.get_properties('energy'))\n",
    "tuned_e   = np.array(check.get_properties('energy'))\n",
    "stock_e   = np.array(before.get_properties('energy'))\n",
    "\n",
    "# energies relative to each set's own mean: the constant offset between your\n",
    "# reference level and the model is not the interesting part, the shape is\n",
    "rel = lambda e: (e - e.mean()) * K\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n",
    "lo = min(rel(reference).min(), rel(tuned_e).min()) - 0.02\n",
    "hi = max(rel(reference).max(), rel(tuned_e).max()) + 0.02\n",
    "ax.plot([lo, hi], [lo, hi], color='0.75', lw=0.9, zorder=0)\n",
    "ax.scatter(rel(reference), rel(stock_e), s=26, marker='s', alpha=0.75,\n",
    "           color='#d62728', label='AIQM2 as shipped')\n",
    "ax.scatter(rel(reference), rel(tuned_e), s=26, alpha=0.85,\n",
    "           color='#1f77b4', label='fine-tuned')\n",
    "ax.set_xlabel('reference energy / kcal mol$^{-1}$')\n",
    "ax.set_ylabel('predicted / kcal mol$^{-1}$')\n",
    "ax.set_xlim(lo, hi); ax.set_ylim(lo, hi)\n",
    "ax.legend(frameon=False, fontsize=9, loc='upper left')\n",
    "ax.set_title('energies relative to each set mean', fontsize=10)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335529a0",
   "metadata": {},
   "source": [
    "Most of the difference is a constant offset: your reference level's atomic energies\n",
    "are not AIQM2's. Fine-tuning refits that per-element shift from your data, which is\n",
    "why it should not be left for the network to absorb."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcd6750",
   "metadata": {},
   "source": [
    "## Reusing the prepared data\n",
    "\n",
    "Rather than recomputing the baseline every time, hand the prepared database over.\n",
    "`train()` reads what was subtracted from it — the baseline, the dispersion term,\n",
    "the program that computed it, and whether forces were handled too — instead of\n",
    "asking you to say it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2aa7fb8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:05.333672Z",
     "iopub.status.busy": "2026-08-11T07:38:05.333359Z",
     "iopub.status.idle": "2026-08-11T07:38:12.149036Z",
     "shell.execute_reply": "2026-08-11T07:38:12.147970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing baseline (GFN2-xTB*) for delta preparation ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing dispersion (D4(wb97x)) for delta preparation ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start retraining on model 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIQM2 TL model saved in /home/dral/tlfigs/nb/reuse_model\n"
     ]
    }
   ],
   "source": [
    "delta_db = ml.delta_learning.prepare_delta_database(\n",
    "    db, method='AIQM2',\n",
    "    dispersion_kwargs={'method': 'd4', 'functional': 'wb97x'},\n",
    "    xyz_derivative_property_to_learn='energy_gradients')\n",
    "\n",
    "delta_db.dump('prepared_labels.h5', format='h5')\n",
    "\n",
    "again = ml.models.methods(method='AIQM2', model_index=0)\n",
    "again.train(delta_db='prepared_labels.h5',\n",
    "            file_to_save_model='reuse_model/',\n",
    "            hyperparameters={'max_epochs': 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec180522",
   "metadata": {},
   "source": [
    "To try a different dispersion choice on the same geometries, point `baseline_db=`\n",
    "at the database you already have. Watch the output: it computes the new dispersion\n",
    "term and **not** the baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "925296f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:12.153904Z",
     "iopub.status.busy": "2026-08-11T07:38:12.153437Z",
     "iopub.status.idle": "2026-08-11T07:38:13.302004Z",
     "shell.execute_reply": "2026-08-11T07:38:13.300964Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing dispersion (D3BJ(b3lyp)) for delta preparation ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delta with D4(wb97x)     = -230.018744 Hartree\n",
      "delta with D3(BJ)/b3lyp  = -230.018879 Hartree\n"
     ]
    }
   ],
   "source": [
    "delta_b = ml.delta_learning.prepare_delta_database(\n",
    "    db, method='AIQM2',\n",
    "    dispersion_kwargs={'method': 'd3bj', 'functional': 'b3lyp'},\n",
    "    baseline_db='my_tl_model/delta_db.h5')\n",
    "\n",
    "a = np.array(delta_db.get_properties('delta_energy'))\n",
    "b = np.array(delta_b.get_properties('delta_energy'))\n",
    "print(f'delta with D4(wb97x)     = {a[0]:.6f} Hartree')\n",
    "print(f'delta with D3(BJ)/b3lyp  = {b[0]:.6f} Hartree')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c552c0be",
   "metadata": {},
   "source": [
    "## Behaviour far from your data\n",
    "\n",
    "The check above says the model reproduces what you trained it on. It says nothing\n",
    "about what happens elsewhere, and that is where a dispersion term earns its place.\n",
    "\n",
    "A neural network sees an atom's neighbours only out to a fixed cutoff — 5.2 Å for\n",
    "most of these models. Beyond that it contributes nothing, so a fine-tuned model\n",
    "with no dispersion term has **no long-range attraction at all**.\n",
    "\n",
    "Here is that difference, measured the only way that settles it: the *same* model\n",
    "fine-tuned on the *same* data twice, differing only in whether `dispersion_kwargs`\n",
    "named a term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57e16bc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:13.305980Z",
     "iopub.status.busy": "2026-08-11T07:38:13.305794Z",
     "iopub.status.idle": "2026-08-11T07:38:13.313174Z",
     "shell.execute_reply": "2026-08-11T07:38:13.311933Z"
    }
   },
   "outputs": [],
   "source": [
    "CH4 = np.array([[ 0.0000,  0.0000,  0.0000],\n",
    "                [ 0.6276,  0.6276,  0.6276],\n",
    "                [ 0.6276, -0.6276, -0.6276],\n",
    "                [-0.6276,  0.6276, -0.6276],\n",
    "                [-0.6276, -0.6276,  0.6276]])\n",
    "Z = np.array([6, 1, 1, 1, 1])\n",
    "\n",
    "separations = np.concatenate([np.arange(3.0, 6.01, 0.1),\n",
    "                              np.arange(6.2, 12.01, 0.2)])\n",
    "\n",
    "def dimer_curve(model):\n",
    "    dimers = ml.data.molecular_database()\n",
    "    for r in separations:\n",
    "        dimers.molecules.append(ml.data.molecule.from_numpy(\n",
    "            coordinates=np.vstack([CH4, CH4 + np.array([r, 0, 0])]),\n",
    "            species=np.concatenate([Z, Z])))\n",
    "    monomer = ml.data.molecular_database(\n",
    "        [ml.data.molecule.from_numpy(coordinates=CH4, species=Z)])\n",
    "    model.predict(molecular_database=dimers, calculate_energy=True)\n",
    "    model.predict(molecular_database=monomer, calculate_energy=True)\n",
    "    return (np.array(dimers.get_properties('energy'))\n",
    "            - 2 * monomer[0].energy) * K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac4cc09d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:13.317020Z",
     "iopub.status.busy": "2026-08-11T07:38:13.316754Z",
     "iopub.status.idle": "2026-08-11T07:38:30.719241Z",
     "shell.execute_reply": "2026-08-11T07:38:30.718418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dral/.local/share/mamba/envs/mlatom-tl/lib/python3.11/site-packages/torchani/resources/\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dral/.local/share/mamba/envs/mlatom-tl/lib/python3.11/site-packages/torchani/resources/\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start retraining on model 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANI TL model saved in /home/dral/tlfigs/nb/ft_True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fine-tuned, D4 term declared     largest |E| beyond 6.5 A: 0.0238 kcal/mol\n",
      "/home/dral/.local/share/mamba/envs/mlatom-tl/lib/python3.11/site-packages/torchani/resources/\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dral/.local/share/mamba/envs/mlatom-tl/lib/python3.11/site-packages/torchani/resources/\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Start retraining on model 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANI TL model saved in /home/dral/tlfigs/nb/ft_False\n",
      "fine-tuned, no dispersion term   largest |E| beyond 6.5 A: 0.0000 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "curves = {}\n",
    "for label, dispersion in (('fine-tuned, D4 term declared',\n",
    "                           {'method': 'd4', 'functional': 'wb97x'}),\n",
    "                          ('fine-tuned, no dispersion term', False)):\n",
    "    m = ml.models.methods(method='ANI-1x', model_index=0)\n",
    "    m.train(molecular_database=db, property_to_learn='energy',\n",
    "            dispersion_kwargs=dispersion,\n",
    "            file_to_save_model=f'ft_{bool(dispersion)}',\n",
    "            hyperparameters={'max_epochs': 100}, verbose=0)\n",
    "    curves[label] = dimer_curve(m)\n",
    "    beyond = np.abs(curves[label][separations >= 6.5]).max()\n",
    "    print(f'{label:32s} largest |E| beyond 6.5 A: {beyond:.4f} kcal/mol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "05c78b81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-11T07:38:30.722958Z",
     "iopub.status.busy": "2026-08-11T07:38:30.722758Z",
     "iopub.status.idle": "2026-08-11T07:38:31.000335Z",
     "shell.execute_reply": "2026-08-11T07:38:30.999353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x390 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (full, tail) = plt.subplots(1, 2, figsize=(9.6, 3.9))\n",
    "for ax in (full, tail):\n",
    "    ax.axhline(0, color='0.8', lw=0.8, zorder=0)\n",
    "    ax.axvline(5.2, color='0.55', lw=1.0, ls=':', zorder=0)\n",
    "\n",
    "for (label, values), style, colour in zip(curves.items(), ('-', '--'),\n",
    "                                          ('#1f77b4', '#d62728')):\n",
    "    full.plot(separations, values, style, lw=1.9, color=colour, label=label)\n",
    "    keep = separations >= 5.0\n",
    "    tail.plot(separations[keep], values[keep], style, lw=1.9, color=colour)\n",
    "\n",
    "full.set_xlim(3, 12); full.set_ylim(-1.0, 0.6)\n",
    "full.set_title('Full range', fontsize=10)\n",
    "full.legend(frameon=False, fontsize=8.5, loc='upper right')\n",
    "tail.set_xlim(5, 12); tail.set_ylim(-0.045, 0.045)\n",
    "tail.set_title('Beyond the cutoff', fontsize=10)\n",
    "for ax in (full, tail):\n",
    "    ax.set_xlabel('CH$_4$–CH$_4$ separation / Å')\n",
    "    ax.set_ylabel('interaction energy / kcal mol$^{-1}$')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253a7c06",
   "metadata": {},
   "source": [
    "Past the cutoff the difference is absolute. The model with no dispersion term is\n",
    "**exactly flat** — the printout above gives 0.0000 kcal/mol beyond 6.5 Å — while\n",
    "the one with D4 keeps a real $-C_6/r^6$ tail. Nothing in a training set of small\n",
    "molecules would have told you which of those you had.\n",
    "\n",
    "The oscillations below 5 Å are worth seeing too, and they are not the dispersion\n",
    "term's doing: this model was fine-tuned on nitromethane and is being asked about\n",
    "two separated methanes, which is outside anything it was shown. That is the same\n",
    "warning as the paragraph above, made visible."
   ]
  }
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