diff --git a/notebooks/0_create-test-set/0_get-exact-answer.ipynb b/notebooks/0_create-test-set/0_get-exact-answer.ipynb
index f234bda..33021fa 100644
--- a/notebooks/0_create-test-set/0_get-exact-answer.ipynb
+++ b/notebooks/0_create-test-set/0_get-exact-answer.ipynb
@@ -23,7 +23,7 @@
     "from ase.vibrations import VibrationsData, Vibrations\n",
     "from ase.calculators.mopac import MOPAC\n",
     "from ase.calculators.psi4 import Psi4\n",
-    "from ase.optimize import QuasiNewton\n",
+    "from ase.optimize import BFGS\n",
     "from ase import Atoms, units\n",
     "from ase.io import write, read\n",
     "from jitterbug.utils import make_calculator\n",
@@ -58,7 +58,8 @@
    "outputs": [],
    "source": [
     "molecule_name = 'caffeine'\n",
-    "method = 'pm7'\n",
+    "relax_method = 'pm7/None'  # Method used to relax geometry \n",
+    "hess_method = None  # Method used to perform Hessian computation, None to use same\n",
     "basis = None  # Set to None for MOPAC methods\n",
     "threads = min(os.cpu_count(), 12)\n",
     "delta = 0.01"
@@ -72,18 +73,37 @@
     "Derived"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f543fd2-ca4c-4d68-a523-14515f351c4b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "relax_method, relax_basis = relax_method.split(\"/\")\n",
+    "if hess_method is None:\n",
+    "    hess_method, hess_basis = relax_method, relax_basis\n",
+    "else:\n",
+    "    hess_method, hess_basis = hess_method.split(\"/\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "aeebbc77-70e4-4709-90a0-b9aaf54d4cd9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "run_name = f'{molecule_name}_{method}_{basis}'\n",
+    "run_name = f'{molecule_name}_{hess_method}_{hess_basis}_at_{relax_method}_{relax_basis}'\n",
     "run_name_with_delta = f'{run_name}_d={delta:.3g}'\n",
     "out_dir = Path('data') / 'exact'\n",
     "if (out_dir / f'{run_name_with_delta}-times.json').exists():\n",
-    "    raise ValueError('Already done!')"
+    "    raise ValueError('Already done!')\n",
+    "print(f'Run name: {run_name_with_delta}')"
    ]
   },
   {
@@ -160,7 +180,7 @@
    },
    "outputs": [],
    "source": [
-    "calc = make_calculator(method, basis, num_threads=threads)"
+    "calc = make_calculator(relax_method, relax_basis, num_threads=threads)"
    ]
   },
   {
@@ -180,14 +200,17 @@
    },
    "outputs": [],
    "source": [
-    "geom_path = out_dir / f'{run_name}.xyz'"
+    "geom_path = out_dir / f'{molecule_name}_{relax_method}_{relax_basis}.xyz'\n",
+    "print(f'Geometry path: {geom_path}')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "ef903a43-5d6c-47fb-a500-837599c95f91",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "%%time\n",
@@ -196,7 +219,7 @@
     "    atoms.calc = calc\n",
     "else:\n",
     "    atoms.calc = calc\n",
-    "    dyn = QuasiNewton(atoms)\n",
+    "    dyn = BFGS(atoms)\n",
     "    with redirect_stderr(devnull):\n",
     "        dyn.run(fmax=0.01)"
    ]
@@ -242,6 +265,35 @@
     "ASE has a built-in method which uses finite displacements"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0e70265b-cefd-4d3c-925b-8b2cf13419e4",
+   "metadata": {},
+   "source": [
+    "Make the calculator for the hessian"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a144434b-e478-42e0-a2bd-5c43beab31d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "calc = make_calculator(hess_method, hess_basis, num_threads=threads)\n",
+    "atoms.calc = calc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71949047-df5f-47a5-883f-c329b7ca12bf",
+   "metadata": {},
+   "source": [
+    "Perform the computation"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -332,11 +384,11 @@
    "outputs": [],
    "source": [
     "psi4_path = out_dir / f'{run_name}-psi4.json'\n",
-    "if isinstance(calc, Psi4) and \"cc\" not in method and not psi4_path.exists():\n",
+    "if isinstance(calc, Psi4) and \"cc\" not in hess_method and not psi4_path.exists():\n",
     "    # Compute\n",
     "    analytic_time = perf_counter()\n",
     "    calc.set_psi4(atoms)\n",
-    "    hess = calc.psi4.hessian(f'{method}/{basis}')\n",
+    "    hess = calc.psi4.hessian(f'{hess_method}/{hess_basis}')\n",
     "    analytic_time = perf_counter() - analytic_time\n",
     "\n",
     "    # Convert to ASE format\n",
diff --git a/notebooks/0_create-test-set/1_compare-step-sizes-and-methods.ipynb b/notebooks/0_create-test-set/1_compare-step-sizes-and-methods.ipynb
index f1ed31c..bce4875 100644
--- a/notebooks/0_create-test-set/1_compare-step-sizes-and-methods.ipynb
+++ b/notebooks/0_create-test-set/1_compare-step-sizes-and-methods.ipynb
@@ -52,7 +52,8 @@
    },
    "outputs": [],
    "source": [
-    "molecule_name = 'water'  # Which water molecule to evaluate\n",
+    "molecule_name = 'water'\n",
+    "relax_level = 'b3lyp_cc-pvtz'  # Which water molecule to evaluate. Need both the molecule name and relaxation level\n",
     "target_method = ('ccsd(t)', 'cc-pvtz')"
    ]
   },
@@ -74,7 +75,7 @@
    },
    "outputs": [],
    "source": [
-    "hessian_paths = list(Path('data/exact/').glob(f'{molecule_name}_*-ase.json'))\n",
+    "hessian_paths = list(Path('data/exact/').glob(f'{molecule_name}_*_at_{relax_level}_d=*-ase.json'))\n",
     "print(f'Found {len(hessian_paths)} hessians for {molecule_name}')"
    ]
   },
@@ -87,7 +88,7 @@
    },
    "outputs": [],
    "source": [
-    "exact_path = Path(f'data/exact/{molecule_name}_{\"_\".join(target_method)}_d=0.005-ase.json')\n",
+    "exact_path = Path(f'data/exact/{molecule_name}_{\"_\".join(target_method)}_at_{relax_level}_d=0.005-ase.json')\n",
     "assert exact_path.exists(), f'Missing reference calculation: {exact_path}'\n",
     "exact_hess = VibrationsData.read(exact_path)"
    ]
@@ -105,7 +106,7 @@
     "    \"\"\"Load the Hessian and parse the metadata from the filename\n",
     "    \n",
     "    Args:\n",
-    "        path: Path to the Hessia\n",
+    "        path: Path to the Hessian\n",
     "    Returns:\n",
     "        Dictionary the includes the metadata for the calculation and errors wrt true Hessian\n",
     "    \"\"\"\n",
@@ -113,7 +114,7 @@
     "    # Get some of the basic information\n",
     "    method_name, delta = path.name[:-9].rsplit(\"_d=\", 1)\n",
     "    delta = float(delta)\n",
-    "    _, method, basis = method_name.split(\"_\")\n",
+    "    _, method, basis = method_name.split(\"_\")[:3]\n",
     "    \n",
     "    # Compare to reference\n",
     "    approx_hess = VibrationsData.read(path)\n",
diff --git a/notebooks/0_create-test-set/2_evaluate-effect-of-rotations.ipynb b/notebooks/0_create-test-set/2_evaluate-effect-of-rotations.ipynb
index 41a3aeb..f67c692 100644
--- a/notebooks/0_create-test-set/2_evaluate-effect-of-rotations.ipynb
+++ b/notebooks/0_create-test-set/2_evaluate-effect-of-rotations.ipynb
@@ -52,7 +52,7 @@
    },
    "outputs": [],
    "source": [
-    "molecule_path = 'data/exact/water_pm7_None.xyz'\n",
+    "molecule_path = 'data/exact/caffeine_pm7_None.xyz'\n",
     "num_samples: int = 256"
    ]
   },
@@ -152,8 +152,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 22.3 ms, sys: 83.7 ms, total: 106 ms\n",
-      "Wall time: 492 ms\n"
+      "CPU times: user 151 ms, sys: 696 ms, total: 847 ms\n",
+      "Wall time: 5.57 s\n"
      ]
     }
    ],
@@ -185,7 +185,7 @@
      "text": [
       "  0%|                                                                                                                                                                                                                                                             | 0/256 [00:00<?, ?it/s]/home/lward/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pmutt/statmech/vib.py:87: RuntimeWarning: overflow encountered in sinh\n",
       "  (0.5 * vib_dimless)**2 * (1. / np.sinh(vib_dimless / 2.))**2\n",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [02:16<00:00,  1.87it/s]\n"
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [30:09<00:00,  7.07s/it]\n"
      ]
     }
    ],
@@ -258,38 +258,38 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.015256</td>\n",
-       "      <td>2.120676</td>\n",
+       "      <td>0.008504</td>\n",
+       "      <td>0.124474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.011612</td>\n",
-       "      <td>0.997429</td>\n",
+       "      <td>0.004583</td>\n",
+       "      <td>0.045956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>0.000040</td>\n",
-       "      <td>0.273227</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.016011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>0.006243</td>\n",
-       "      <td>1.387332</td>\n",
+       "      <td>0.004681</td>\n",
+       "      <td>0.088121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>0.012741</td>\n",
-       "      <td>1.964503</td>\n",
+       "      <td>0.008094</td>\n",
+       "      <td>0.124565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>0.022034</td>\n",
-       "      <td>2.795209</td>\n",
+       "      <td>0.013257</td>\n",
+       "      <td>0.168693</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>0.044429</td>\n",
-       "      <td>4.493064</td>\n",
+       "      <td>0.015370</td>\n",
+       "      <td>0.219113</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -298,13 +298,13 @@
       "text/plain": [
        "        zpe_error     vib_mae\n",
        "count  256.000000  256.000000\n",
-       "mean     0.015256    2.120676\n",
-       "std      0.011612    0.997429\n",
-       "min      0.000040    0.273227\n",
-       "25%      0.006243    1.387332\n",
-       "50%      0.012741    1.964503\n",
-       "75%      0.022034    2.795209\n",
-       "max      0.044429    4.493064"
+       "mean     0.008504    0.124474\n",
+       "std      0.004583    0.045956\n",
+       "min      0.000007    0.016011\n",
+       "25%      0.004681    0.088121\n",
+       "50%      0.008094    0.124565\n",
+       "75%      0.013257    0.168693\n",
+       "max      0.015370    0.219113"
       ]
      },
      "execution_count": 9,
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "3e4ffe3f-b8c3-4112-ba7b-7e22119591ca",
    "metadata": {
     "tags": []
@@ -331,13 +331,13 @@
        "Text(0, 0.5, 'Frequency')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 550x200 with 2 Axes>"
       ]
@@ -367,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "f2151e7b-79d4-4c3d-95f4-45d4f61006a5",
    "metadata": {},
    "outputs": [],
diff --git a/notebooks/data/README.md b/notebooks/0_create-test-set/data/README.md
similarity index 100%
rename from notebooks/data/README.md
rename to notebooks/0_create-test-set/data/README.md
diff --git a/notebooks/data/structures/README.md b/notebooks/0_create-test-set/data/structures/README.md
similarity index 100%
rename from notebooks/data/structures/README.md
rename to notebooks/0_create-test-set/data/structures/README.md
diff --git a/notebooks/data/structures/butanol.json b/notebooks/0_create-test-set/data/structures/butanol.json
similarity index 100%
rename from notebooks/data/structures/butanol.json
rename to notebooks/0_create-test-set/data/structures/butanol.json
diff --git a/notebooks/data/structures/caffeine.json b/notebooks/0_create-test-set/data/structures/caffeine.json
similarity index 100%
rename from notebooks/data/structures/caffeine.json
rename to notebooks/0_create-test-set/data/structures/caffeine.json
diff --git a/notebooks/data/structures/water.json b/notebooks/0_create-test-set/data/structures/water.json
similarity index 100%
rename from notebooks/data/structures/water.json
rename to notebooks/0_create-test-set/data/structures/water.json
diff --git a/notebooks/0_create-test-set/run-all-methods.sh b/notebooks/0_create-test-set/run-all-methods.sh
index 497ba86..1b2d6f5 100644
--- a/notebooks/0_create-test-set/run-all-methods.sh
+++ b/notebooks/0_create-test-set/run-all-methods.sh
@@ -1,18 +1,17 @@
 #! /bin/bash
 
-molecule=butanol
-methods="pm7//None xtb//None hf//cc-pvtz b3lyp//cc-pvtz wb97x-d//cc-pvtz m062x//cc-pvtz ccsd(t)//cc-pvdz"
+molecule=water
+relax_method="b3lyp/cc-pvtz"
+
+hess_methods="pm7/None xtb/None hf/cc-pvtz b3lyp/cc-pvtz wb97x-d/cc-pvtz m062x/cc-pvtz ccsd(t)/cc-pvdz"
 deltas="0.04 0.02 0.01 0.005 0.0025"
 
-#methods="ccsd(t)//cc-pvtz"
+#hess_methods="ccsd(t)/cc-pvtz"
 #deltas=0.005
 
 notebook=0_get-exact-answer.ipynb
-for name in $methods; do
-    echo $name
+for method in $hess_methods; do
     for delta in $deltas; do
-        method=$(echo $name | cut -d "/" -f 1)
-        basis=$(echo $name | cut -d "/" -f 3)
-        papermill -p method $method -p basis $basis -p delta $delta -p molecule_name $molecule $notebook live.ipynb
+        papermill -p hess_method $method -p relax_method $relax_method -p delta $delta -p molecule_name $molecule $notebook live.ipynb
     done
 done
diff --git a/notebooks/1_explore-sampling-methods/0_random-directions-same-distance.ipynb b/notebooks/1_explore-sampling-methods/0_random-directions-same-distance.ipynb
index ea8d7b8..5bbd580 100644
--- a/notebooks/1_explore-sampling-methods/0_random-directions-same-distance.ipynb
+++ b/notebooks/1_explore-sampling-methods/0_random-directions-same-distance.ipynb
@@ -47,9 +47,8 @@
    },
    "outputs": [],
    "source": [
-    "molecule_name = 'caffeine'\n",
-    "method = 'hf'\n",
-    "basis = 'def2-svpd'\n",
+    "starting_geometry = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "method = 'hf/def2-svpd'\n",
     "threads = min(os.cpu_count(), 12)\n",
     "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm"
    ]
@@ -69,8 +68,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "run_name = Path(starting_geometry).name[:-4]\n",
-    "name, method, basis = run_name.split(\"_\")"
+    "relax_name = Path(starting_geometry).name[:-4]\n",
+    "name, relax_method, relax_basis = relax_name.split(\"_\")\n",
+    "method, basis = method.split(\"/\")\n",
+    "run_name = f'{name}_{method}_{basis}_at_{relax_method}_{relax_basis}'\n",
+    "print(f'Run name: {run_name}')"
    ]
   },
   {
diff --git a/notebooks/1_explore-sampling-methods/1_random-directions-variable-distance.ipynb b/notebooks/1_explore-sampling-methods/1_random-directions-variable-distance.ipynb
index 4567a8c..d46d849 100644
--- a/notebooks/1_explore-sampling-methods/1_random-directions-variable-distance.ipynb
+++ b/notebooks/1_explore-sampling-methods/1_random-directions-variable-distance.ipynb
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "52252ee2-315c-48bb-8cba-07620e6e2faa",
+   "id": "c61794ce-ca24-470f-903f-4fc5118af1d3",
    "metadata": {
     "tags": [
      "parameters"
@@ -48,13 +48,14 @@
    "outputs": [],
    "source": [
     "starting_geometry = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "method = 'pm7/None'\n",
     "threads = min(os.cpu_count(), 12)\n",
-    "step_size: float = 0.005 # Lambda parameter for an expontential distribution for the Perturbation amount"
+    "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7010df09-73b2-4d58-be03-15a5f0d04b4c",
+   "id": "7ebb8a2a-b2f8-4647-9cd4-d9a05efc4790",
    "metadata": {},
    "source": [
     "Derived"
@@ -63,12 +64,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b6794cd-477f-45a1-b96f-2332804ddb20",
+   "id": "3177eaf6-7af7-4e7c-8440-e32c172f8669",
    "metadata": {},
    "outputs": [],
    "source": [
-    "run_name = Path(starting_geometry).name[:-4]\n",
-    "name, method, basis = run_name.split(\"_\")"
+    "relax_name = Path(starting_geometry).name[:-4]\n",
+    "name, relax_method, relax_basis = relax_name.split(\"_\")\n",
+    "method, basis = method.split(\"/\")\n",
+    "run_name = f'{name}_{method}_{basis}_at_{relax_method}_{relax_basis}'\n",
+    "print(f'Run name: {run_name}')"
    ]
   },
   {
@@ -226,7 +230,8 @@
     "    # Sample a perturbation\n",
     "    disp = np.random.normal(0, 1, size=(n_atoms * 3))\n",
     "    disp /= np.linalg.norm(disp)\n",
-    "    my_step_dist = np.random.exponential(scale=step_size)\n",
+    "    my_step_dist = np.random.uniform(0, step_size)\n",
+    "    pbar.set_description(f'd={my_step_dist:.3e}')\n",
     "    disp *= my_step_dist * len(atoms)\n",
     "    disp = disp.reshape((-1, 3))\n",
     "    \n",
diff --git a/notebooks/1_explore-sampling-methods/2_displace-along-axes.ipynb b/notebooks/1_explore-sampling-methods/2_displace-along-axes.ipynb
index ef96e40..1589071 100644
--- a/notebooks/1_explore-sampling-methods/2_displace-along-axes.ipynb
+++ b/notebooks/1_explore-sampling-methods/2_displace-along-axes.ipynb
@@ -52,6 +52,7 @@
    "outputs": [],
    "source": [
     "starting_geometry = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "method = 'hf/def2-svpd'\n",
     "threads = min(os.cpu_count(), 12)\n",
     "step_size: float = 0.005 # Lambda parameter for an expontential distribution for the Perturbation amount\n",
     "perturbs_per_evaluation: int = 2  # Number of perturbations to perform at once"
@@ -59,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7010df09-73b2-4d58-be03-15a5f0d04b4c",
+   "id": "134b0aa4-f7ef-415f-8334-7039bdf66152",
    "metadata": {},
    "source": [
     "Derived"
@@ -68,12 +69,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b6794cd-477f-45a1-b96f-2332804ddb20",
+   "id": "f47df53a-1b81-4504-a9db-2fcc583d7096",
    "metadata": {},
    "outputs": [],
    "source": [
-    "run_name = Path(starting_geometry).name[:-4]\n",
-    "name, method, basis = run_name.split(\"_\")"
+    "relax_name = Path(starting_geometry).name[:-4]\n",
+    "name, relax_method, relax_basis = relax_name.split(\"_\")\n",
+    "method, basis = method.split(\"/\")\n",
+    "run_name = f'{name}_{method}_{basis}_at_{relax_method}_{relax_basis}'\n",
+    "print(f'Run name: {run_name}')"
    ]
   },
   {
diff --git a/notebooks/1_explore-sampling-methods/3_displace-along-vibrational-modes.ipynb b/notebooks/1_explore-sampling-methods/3_displace-along-vibrational-modes.ipynb
index b327b4d..1739a1e 100644
--- a/notebooks/1_explore-sampling-methods/3_displace-along-vibrational-modes.ipynb
+++ b/notebooks/1_explore-sampling-methods/3_displace-along-vibrational-modes.ipynb
@@ -29,6 +29,7 @@
     "from pathlib import Path\n",
     "from tqdm import tqdm \n",
     "import numpy as np\n",
+    "import shutil\n",
     "import os"
    ]
   },
@@ -52,6 +53,7 @@
    "outputs": [],
    "source": [
     "starting_geometry = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "method = 'pm7/None'\n",
     "threads = min(os.cpu_count(), 12)\n",
     "step_size: float = 0.002 # Target energy increase (units: eV)\n",
     "perturbs_per_evaluation: int = 16  # Number of perturbations to perform at once\n",
@@ -70,14 +72,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b6794cd-477f-45a1-b96f-2332804ddb20",
+   "id": "91cc7cb8-a620-4395-84fc-533c041c652e",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "run_name = Path(starting_geometry).name[:-4]\n",
-    "name, method, basis = run_name.split(\"_\")"
+    "relax_name = Path(starting_geometry).name[:-4]\n",
+    "name, relax_method, relax_basis = relax_name.split(\"_\")\n",
+    "method, basis = method.split(\"/\")\n",
+    "run_name = f'{name}_{method}_{basis}_at_{relax_method}_{relax_basis}'\n",
+    "print(f'Run name: {run_name}')"
    ]
   },
   {
@@ -142,6 +147,8 @@
    "source": [
     "%%time\n",
     "atoms.calc = lower_calc\n",
+    "if Path('vib').exists():\n",
+    "    shutil.rmtree('vib')\n",
     "vib = Vibrations(atoms)\n",
     "vib.run()"
    ]
diff --git a/notebooks/1_explore-sampling-methods/run-all-methods.sh b/notebooks/1_explore-sampling-methods/run-all-methods.sh
index 42eea5d..8bcca29 100644
--- a/notebooks/1_explore-sampling-methods/run-all-methods.sh
+++ b/notebooks/1_explore-sampling-methods/run-all-methods.sh
@@ -1,23 +1,25 @@
 #! /bin/bash
 
 xyz=../data/exact/caffeine_pm7_None.xyz
+method='pm7/None'
+
 for step_size in 0.02; do
     # Do the randomized methods
-    for method in 0_random-directions-same-distance.ipynb 1_random-directions-variable-distance.ipynb; do
-        papermill -p starting_geometry $xyz -p step_size $step_size $method last.ipynb
+    for notebook in 0_random-directions-same-distance.ipynb 1_random-directions-variable-distance.ipynb; do
+        papermill -p starting_geometry $xyz -p method $method -p step_size $step_size $notebook last.ipynb
     done
     
     # Test with different reductions for "along axes"
     notebook=2_displace-along-axes.ipynb
     for n in 2 4 8; do
-        papermill -p starting_geometry $xyz -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
+        papermill -p starting_geometry $xyz -p method $method -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
     done
 done
 
 # Test with the vibrational modes
 notebook=3_displace-along-vibrational-modes.ipynb
-for step_size in 0.001 0.002; do
+for step_size in 0.001 0.002; do  # These step sizes are energy scales in eV, not distances in Angstrom as above
     for n in 16 32 64; do
-        papermill -p starting_geometry $xyz -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
+        papermill -p starting_geometry $xyz -p method $method -p perturbs_per_evaluation $n -p step_size $step_size $notebook last.ipynb
     done
 done
diff --git a/notebooks/2_testing-fitting-strategies/1_fit-forcefield-using-mbtr.ipynb b/notebooks/2_testing-fitting-strategies/1_fit-forcefield-using-mbtr.ipynb
index cb29ff0..11fd5c6 100644
--- a/notebooks/2_testing-fitting-strategies/1_fit-forcefield-using-mbtr.ipynb
+++ b/notebooks/2_testing-fitting-strategies/1_fit-forcefield-using-mbtr.ipynb
@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "db_path: str = '../1_explore-sampling-methods/data/along-axes/caffeine_pm7_None_d=5.00e-03-N=2.db'\n",
+    "db_path: str = '../1_explore-sampling-methods/data/along-axes/caffeine_pm7_None_at_pm7_None_d=2.00e-02-N=4.db'\n",
     "overwrite: bool = False\n",
     "max_size: int = 10000"
    ]
@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "run_name, sampling_options = Path(db_path).name[:-3].rsplit(\"_\", 1)\n",
-    "exact_path = Path('../data/exact/') / f'{run_name}-ase.json'\n",
+    "exact_path = Path('../0_create-test-set/data/exact/') / f'{run_name}_d=0.01-ase.json'\n",
     "sampling_name = Path(db_path).parent.name\n",
     "out_name = '_'.join([run_name, sampling_name, sampling_options])\n",
     "out_dir = Path('data/mbtr/')"
diff --git a/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb b/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
index c864dfc..7776ede 100644
--- a/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
+++ b/notebooks/3_consolidate-results/0_compare-sampling-strategies-with-mbtr.ipynb
@@ -50,32 +50,38 @@
    },
    "outputs": [],
    "source": [
-    "target_mol = '../data/exact/caffeine_pm7_None.xyz'\n",
+    "target_result = '../0_create-test-set/data/exact/caffeine_pm7_None_at_pm7_None_d=0.01-ase.json'\n",
     "target_method = '../2_testing-fitting-strategies/data/mbtr/'\n",
     "target_size: int = 1500"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8874ea91-b4f3-432a-bd28-0d33b50e24ee",
+   "id": "5af51ec8-7a05-4000-8194-998dd08ce315",
    "metadata": {},
    "source": [
-    "## Load the Exact Result\n",
-    "The target molecule filename determines which molecule we'll look for. The name includes both the molecule name and method used to evaluate the hessian"
+    "Derived"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "db22a33d-e70a-4e7b-aad7-39aa4e552804",
+   "id": "7094c318-9f4c-448c-b1cb-bf61b3678851",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "target_mol = Path(target_mol)\n",
-    "mol_name = target_mol.name[:-4]\n",
-    "atoms = read(target_mol)"
+    "mol_name, _ = Path(target_result).name.rsplit(\"_\", 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8874ea91-b4f3-432a-bd28-0d33b50e24ee",
+   "metadata": {},
+   "source": [
+    "## Load the Exact Result\n",
+    "The target molecule filename determines which molecule we'll look for. The name includes both the molecule name and method used to evaluate the hessian"
    ]
   },
   {
@@ -89,7 +95,7 @@
     {
      "data": {
       "text/plain": [
-       "<ase.vibrations.data.VibrationsData at 0x7ff3ec197340>"
+       "<ase.vibrations.data.VibrationsData at 0x7f5d24a5f0a0>"
       ]
      },
      "execution_count": 4,
@@ -98,7 +104,7 @@
     }
    ],
    "source": [
-    "exact_hess = VibrationsData.read(target_mol.parent / f'{mol_name}-ase.json')\n",
+    "exact_hess = VibrationsData.read(target_result)\n",
     "exact_hess"
    ]
   },
@@ -118,9 +124,18 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 11 approximate Hessians\n"
+     ]
+    }
+   ],
    "source": [
-    "all_hessians = list(Path(target_method).glob(f\"{mol_name}_*-increment.json\"))"
+    "all_hessians = list(Path(target_method).glob(f\"{mol_name}_*-increment.json\"))\n",
+    "print(f'Found {len(all_hessians)} approximate Hessians')"
    ]
   },
   {
@@ -135,16 +150,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                                                                                                                                                                                              | 0/21 [00:00<?, ?it/s]/home/lward/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pmutt/statmech/vib.py:87: RuntimeWarning: overflow encountered in sinh\n",
+      "  0%|                                                                                                                                                                                                                                                              | 0/11 [00:00<?, ?it/s]/home/lward/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pmutt/statmech/vib.py:87: RuntimeWarning: overflow encountered in sinh\n",
       "  (0.5 * vib_dimless)**2 * (1. / np.sinh(vib_dimless / 2.))**2\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:07<00:00,  2.64it/s]"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:02<00:00,  3.80it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded 336 approximate hessians\n"
+      "Loaded 176 approximate hessians\n"
      ]
     },
     {
@@ -194,6 +209,48 @@
     "print(f'Loaded {len(all_results)} approximate hessians')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c3aa94ce-1f23-4007-8766-3da998db628e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "path               ../2_testing-fitting-strategies/data/mbtr/caff...\n",
+       "sampling_method                                           along-axes\n",
+       "options                                               d=2.00e-02-N=2\n",
+       "size                                                               5\n",
+       "d                                                           2.00e-02\n",
+       "N                                                                  2\n",
+       "scale_factor                                                     1.0\n",
+       "zpe                                                         5.058148\n",
+       "zpe_error                                                -104.407702\n",
+       "cp                 [0.014146793803042134, 0.01771395512622287, 0....\n",
+       "cp_error           [-0.010065824804868617, -0.01212553988407947, ...\n",
+       "h                  [5.072098696119492, 5.11863818857305, 5.175908...\n",
+       "h_error            [104.39707523324437, 104.36530161384731, 104.3...\n",
+       "temps              [1.0, 3.9291338582677167, 6.858267716535433, 9...\n",
+       "vib_freqs          [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
+       "vib_errors         [218.79974021444124, 187.1848443090143, 148.53...\n",
+       "vib_mae                                                  1119.440878\n",
+       "maxstep                                                          NaN\n",
+       "lower                                                            NaN\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results.iloc[0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0f243a23-ed89-4f5e-aeac-f23722ef10af",
@@ -204,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "2d315d44-7564-468f-bd03-8bbedf8b424c",
    "metadata": {
     "tags": []
@@ -215,246 +272,6 @@
     "    all_results[col] = pd.to_numeric(all_results[col])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "eee03c3a-4a53-4e84-995a-c2191e6f6332",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>path</th>\n",
-       "      <th>sampling_method</th>\n",
-       "      <th>options</th>\n",
-       "      <th>size</th>\n",
-       "      <th>d</th>\n",
-       "      <th>N</th>\n",
-       "      <th>zpe</th>\n",
-       "      <th>zpe_error</th>\n",
-       "      <th>cp</th>\n",
-       "      <th>cp_error</th>\n",
-       "      <th>h</th>\n",
-       "      <th>h_error</th>\n",
-       "      <th>temps</th>\n",
-       "      <th>vib_freqs</th>\n",
-       "      <th>vib_errors</th>\n",
-       "      <th>vib_mae</th>\n",
-       "      <th>maxstep</th>\n",
-       "      <th>lower</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>../2_testing-fitting-strategies/data/mbtr/caff...</td>\n",
-       "      <td>along-axes</td>\n",
-       "      <td>d=1.00e-02-N=8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>14.200987</td>\n",
-       "      <td>-95.264864</td>\n",
-       "      <td>[0.000397593092606875, 0.002386964975551445, 0...</td>\n",
-       "      <td>[0.0036833759055666425, 0.0032014502665919545,...</td>\n",
-       "      <td>[14.201069696784895, 14.20544123215772, 14.216...</td>\n",
-       "      <td>[95.26810423257896, 95.27849857026263, 95.2846...</td>\n",
-       "      <td>[1.0, 3.9291338582677167, 6.858267716535433, 9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[365.26521782818406, 345.1085529599917, 194.17...</td>\n",
-       "      <td>1027.154434</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>../2_testing-fitting-strategies/data/mbtr/caff...</td>\n",
-       "      <td>along-axes</td>\n",
-       "      <td>d=1.00e-02-N=8</td>\n",
-       "      <td>91</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>5.163324</td>\n",
-       "      <td>-104.302527</td>\n",
-       "      <td>[0.0019348995359107907, 0.008929018823987386, ...</td>\n",
-       "      <td>[0.002146069462262727, -0.0033406035818439863,...</td>\n",
-       "      <td>[5.163950489872683, 5.179473698114745, 5.21578...</td>\n",
-       "      <td>[104.30522343949119, 104.3044661043056, 104.28...</td>\n",
-       "      <td>[1.0, 3.9291338582677167, 6.858267716535433, 9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[136.3752974454976, 116.1372867519512, 97.3719...</td>\n",
-       "      <td>1113.227006</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>../2_testing-fitting-strategies/data/mbtr/caff...</td>\n",
-       "      <td>along-axes</td>\n",
-       "      <td>d=1.00e-02-N=8</td>\n",
-       "      <td>177</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>6.876866</td>\n",
-       "      <td>-102.588985</td>\n",
-       "      <td>[0.0020382429986622493, 0.008341448274375083, ...</td>\n",
-       "      <td>[0.0020427259995112685, -0.0027530330322316837...</td>\n",
-       "      <td>[6.878002515584569, 6.8926516980488675, 6.9274...</td>\n",
-       "      <td>[102.59117141377929, 102.59128810437149, 102.5...</td>\n",
-       "      <td>[1.0, 3.9291338582677167, 6.858267716535433, 9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[96.75265542338498, 90.41924803705203, 74.0875...</td>\n",
-       "      <td>1092.500033</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>../2_testing-fitting-strategies/data/mbtr/caff...</td>\n",
-       "      <td>along-axes</td>\n",
-       "      <td>d=1.00e-02-N=8</td>\n",
-       "      <td>263</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>89.958157</td>\n",
-       "      <td>-19.507693</td>\n",
-       "      <td>[0.0014020270591578593, 0.0023843729911753324,...</td>\n",
-       "      <td>[0.0026789419390156584, 0.003204042250968067, ...</td>\n",
-       "      <td>[89.9587466410539, 89.96445676642922, 89.97267...</td>\n",
-       "      <td>[19.510427288309955, 19.51948303599113, 19.528...</td>\n",
-       "      <td>[1.0, 3.9291338582677167, 6.858267716535433, 9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.446...</td>\n",
-       "      <td>[111.37319602907411, 108.7601947486991, 66.937...</td>\n",
-       "      <td>263.944079</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>../2_testing-fitting-strategies/data/mbtr/caff...</td>\n",
-       "      <td>along-axes</td>\n",
-       "      <td>d=1.00e-02-N=8</td>\n",
-       "      <td>350</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>93.839129</td>\n",
-       "      <td>-15.626722</td>\n",
-       "      <td>[1.6382310852832182e-08, 0.0004830509673205082...</td>\n",
-       "      <td>[0.004080952615862665, 0.005105364274822891, 0...</td>\n",
-       "      <td>[93.83912881523764, 93.83954779804499, 93.8423...</td>\n",
-       "      <td>[15.630045114126219, 15.644392004375362, 15.65...</td>\n",
-       "      <td>[1.0, 3.9291338582677167, 6.858267716535433, 9...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[118.00864162563397, 110.49254554846664, 100.8...</td>\n",
-       "      <td>237.597413</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                path sampling_method  \\\n",
-       "0  ../2_testing-fitting-strategies/data/mbtr/caff...      along-axes   \n",
-       "1  ../2_testing-fitting-strategies/data/mbtr/caff...      along-axes   \n",
-       "2  ../2_testing-fitting-strategies/data/mbtr/caff...      along-axes   \n",
-       "3  ../2_testing-fitting-strategies/data/mbtr/caff...      along-axes   \n",
-       "4  ../2_testing-fitting-strategies/data/mbtr/caff...      along-axes   \n",
-       "\n",
-       "          options  size     d    N        zpe   zpe_error  \\\n",
-       "0  d=1.00e-02-N=8     5  0.01  8.0  14.200987  -95.264864   \n",
-       "1  d=1.00e-02-N=8    91  0.01  8.0   5.163324 -104.302527   \n",
-       "2  d=1.00e-02-N=8   177  0.01  8.0   6.876866 -102.588985   \n",
-       "3  d=1.00e-02-N=8   263  0.01  8.0  89.958157  -19.507693   \n",
-       "4  d=1.00e-02-N=8   350  0.01  8.0  93.839129  -15.626722   \n",
-       "\n",
-       "                                                  cp  \\\n",
-       "0  [0.000397593092606875, 0.002386964975551445, 0...   \n",
-       "1  [0.0019348995359107907, 0.008929018823987386, ...   \n",
-       "2  [0.0020382429986622493, 0.008341448274375083, ...   \n",
-       "3  [0.0014020270591578593, 0.0023843729911753324,...   \n",
-       "4  [1.6382310852832182e-08, 0.0004830509673205082...   \n",
-       "\n",
-       "                                            cp_error  \\\n",
-       "0  [0.0036833759055666425, 0.0032014502665919545,...   \n",
-       "1  [0.002146069462262727, -0.0033406035818439863,...   \n",
-       "2  [0.0020427259995112685, -0.0027530330322316837...   \n",
-       "3  [0.0026789419390156584, 0.003204042250968067, ...   \n",
-       "4  [0.004080952615862665, 0.005105364274822891, 0...   \n",
-       "\n",
-       "                                                   h  \\\n",
-       "0  [14.201069696784895, 14.20544123215772, 14.216...   \n",
-       "1  [5.163950489872683, 5.179473698114745, 5.21578...   \n",
-       "2  [6.878002515584569, 6.8926516980488675, 6.9274...   \n",
-       "3  [89.9587466410539, 89.96445676642922, 89.97267...   \n",
-       "4  [93.83912881523764, 93.83954779804499, 93.8423...   \n",
-       "\n",
-       "                                             h_error  \\\n",
-       "0  [95.26810423257896, 95.27849857026263, 95.2846...   \n",
-       "1  [104.30522343949119, 104.3044661043056, 104.28...   \n",
-       "2  [102.59117141377929, 102.59128810437149, 102.5...   \n",
-       "3  [19.510427288309955, 19.51948303599113, 19.528...   \n",
-       "4  [15.630045114126219, 15.644392004375362, 15.65...   \n",
-       "\n",
-       "                                               temps  \\\n",
-       "0  [1.0, 3.9291338582677167, 6.858267716535433, 9...   \n",
-       "1  [1.0, 3.9291338582677167, 6.858267716535433, 9...   \n",
-       "2  [1.0, 3.9291338582677167, 6.858267716535433, 9...   \n",
-       "3  [1.0, 3.9291338582677167, 6.858267716535433, 9...   \n",
-       "4  [1.0, 3.9291338582677167, 6.858267716535433, 9...   \n",
-       "\n",
-       "                                           vib_freqs  \\\n",
-       "0  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.446...   \n",
-       "4  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
-       "\n",
-       "                                          vib_errors      vib_mae maxstep  \\\n",
-       "0  [365.26521782818406, 345.1085529599917, 194.17...  1027.154434     NaN   \n",
-       "1  [136.3752974454976, 116.1372867519512, 97.3719...  1113.227006     NaN   \n",
-       "2  [96.75265542338498, 90.41924803705203, 74.0875...  1092.500033     NaN   \n",
-       "3  [111.37319602907411, 108.7601947486991, 66.937...   263.944079     NaN   \n",
-       "4  [118.00864162563397, 110.49254554846664, 100.8...   237.597413     NaN   \n",
-       "\n",
-       "  lower  \n",
-       "0   NaN  \n",
-       "1   NaN  \n",
-       "2   NaN  \n",
-       "3   NaN  \n",
-       "4   NaN  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_results.head()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -466,10 +283,10 @@
     {
      "data": {
       "text/plain": [
-       "along-axes                  9\n",
        "along-vibrational-modes     6\n",
-       "random-dir-same-dist        3\n",
-       "random-dir-variable-dist    3\n",
+       "along-axes                  3\n",
+       "random-dir-same-dist        1\n",
+       "random-dir-variable-dist    1\n",
        "Name: sampling_method, dtype: int64"
       ]
      },
@@ -538,10 +355,10 @@
     {
      "data": {
       "text/plain": [
-       "{PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_along-axes_d=2.00e-02-N=4-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_along-vibrational-modes_d=1.00e-03-N=64-maxstep=6.00e-02-lower=xtb+None-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_random-dir-same-dist_d=1.00e-02-increment.json'),\n",
-       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_random-dir-variable-dist_d=1.00e-02-increment.json')}"
+       "{PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_along-axes_d=2.00e-02-N=4-increment.json'),\n",
+       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_along-vibrational-modes_d=1.00e-03-N=32-maxstep=6.00e-02-lower=xtb+None-increment.json'),\n",
+       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_random-dir-same-dist_d=2.00e-02-increment.json'),\n",
+       " PosixPath('../2_testing-fitting-strategies/data/mbtr/caffeine_pm7_None_at_pm7_None_random-dir-variable-dist_d=2.00e-02-increment.json')}"
       ]
      },
      "execution_count": 12,
@@ -583,7 +400,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x250 with 1 Axes>"
       ]
@@ -618,7 +435,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x250 with 1 Axes>"
       ]
@@ -686,7 +503,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x380 with 3 Axes>"
       ]
@@ -733,40 +550,34 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "single positional indexer is out-of-bounds",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m best_model \u001b[38;5;241m=\u001b[39m \u001b[43mbest_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43md==0.01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msort_values\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msize\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtail\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1625\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1622\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1624\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m-> 1625\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1627\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.9/site-packages/pandas/core/indexing.py:1557\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1555\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[1;32m   1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[0;32m-> 1557\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
+     ]
+    }
+   ],
    "source": [
     "best_model = best_strategy.query('d==0.01').sort_values('size').tail().iloc[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "137b0999-98fe-414b-ae6c-c7c39f37314a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '$H$ (kcal/mol)')"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 350x250 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(3.5, 2.5))\n",
     "\n",