diff --git a/envs/environment-cpu.yml b/envs/environment-cpu.yml
index 7dc4eae..1bbbb18 100644
--- a/envs/environment-cpu.yml
+++ b/envs/environment-cpu.yml
@@ -18,7 +18,8 @@ dependencies:
   - scikit-learn
   
   # Quantum chemistry
-  - psi4==1.8.*
+  - psi4==1.8.*  # Remove for MacOS systems
+  - mopac
 
   # Interatomic forcefields
   - dscribe==2.1.*
@@ -29,4 +30,5 @@ dependencies:
 
   - pip
   - pip:
+    - git+https://gitlab.com/ase/ase.git  # Needed for MOPAC
     - -e ..[test]
diff --git a/notebooks/proof-of-concept/0_get-exact-answer.ipynb b/notebooks/proof-of-concept/0_get-exact-answer.ipynb
index 69f2d98..ecf7b28 100644
--- a/notebooks/proof-of-concept/0_get-exact-answer.ipynb
+++ b/notebooks/proof-of-concept/0_get-exact-answer.ipynb
@@ -21,13 +21,16 @@
    "source": [
     "from ase.thermochemistry import IdealGasThermo\n",
     "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 import Atoms, units\n",
     "from ase.io import write\n",
+    "from contextlib import redirect_stderr\n",
     "from time import perf_counter\n",
     "from platform import node\n",
     "from pathlib import Path\n",
+    "from os import devnull\n",
     "import numpy as np\n",
     "import shutil\n",
     "import json\n",
@@ -53,10 +56,12 @@
    },
    "outputs": [],
    "source": [
-    "molecule_name = 'caffeine'\n",
-    "method = 'hf'\n",
-    "basis = 'def2-svpd'\n",
-    "threads = min(os.cpu_count(), 12)"
+    "molecule_name = 'water'\n",
+    "method = 'pm7'\n",
+    "mopac_methods = ['pm7']  # Use MOPAC for these methods\n",
+    "basis = None  # Set to None for MOPAC methods\n",
+    "threads = min(os.cpu_count(), 12)\n",
+    "assert (method in mopac_methods) == (basis is None), 'Use a basis of None for MOPAC computations'"
    ]
   },
   {
@@ -149,19 +154,12 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  Memory set to   3.815 GiB by Python driver.\n",
-      "  Threads set to 12 by Python driver.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')"
+    "if method not in mopac_methods:\n",
+    "    calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')\n",
+    "else:\n",
+    "    calc = MOPAC(method=method, command='mopac PREFIX.mop > /dev/null')"
    ]
   },
   {
@@ -174,58 +172,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "  Memory set to   3.815 GiB by Python driver.\n",
-      "  Threads set to 12 by Python driver.\n",
       "                Step[ FC]     Time          Energy          fmax\n",
-      "BFGSLineSearch:    0[  0] 15:25:31   -18390.794139        4.1661\n",
-      "BFGSLineSearch:    1[  2] 15:26:05   -18391.020663        1.9419\n",
-      "BFGSLineSearch:    2[  4] 15:26:41   -18391.134456        1.3043\n",
-      "BFGSLineSearch:    3[  6] 15:27:16   -18391.190769        1.0587\n",
-      "BFGSLineSearch:    4[  8] 15:27:51   -18391.215384        0.5746\n",
-      "BFGSLineSearch:    5[ 10] 15:28:27   -18391.226240        0.3911\n",
-      "BFGSLineSearch:    6[ 12] 15:29:02   -18391.233268        0.3498\n",
-      "BFGSLineSearch:    7[ 14] 15:29:37   -18391.239126        0.2511\n",
-      "BFGSLineSearch:    8[ 16] 15:30:12   -18391.244270        0.2340\n",
-      "BFGSLineSearch:    9[ 18] 15:30:46   -18391.247254        0.1998\n",
-      "BFGSLineSearch:   10[ 20] 15:31:21   -18391.249083        0.1269\n",
-      "BFGSLineSearch:   11[ 22] 15:31:56   -18391.249935        0.1215\n",
-      "BFGSLineSearch:   12[ 24] 15:32:31   -18391.251072        0.1065\n",
-      "BFGSLineSearch:   13[ 26] 15:33:06   -18391.251900        0.1094\n",
-      "BFGSLineSearch:   14[ 28] 15:33:41   -18391.252777        0.0838\n",
-      "BFGSLineSearch:   15[ 30] 15:34:15   -18391.253605        0.1189\n",
-      "BFGSLineSearch:   16[ 32] 15:34:50   -18391.254853        0.0854\n",
-      "BFGSLineSearch:   17[ 34] 15:35:24   -18391.255313        0.0749\n",
-      "BFGSLineSearch:   18[ 36] 15:35:59   -18391.256046        0.0943\n",
-      "BFGSLineSearch:   19[ 38] 15:36:34   -18391.256326        0.0431\n",
-      "BFGSLineSearch:   20[ 40] 15:37:09   -18391.256438        0.0387\n",
-      "BFGSLineSearch:   21[ 42] 15:37:44   -18391.256503        0.0240\n",
-      "BFGSLineSearch:   22[ 44] 15:38:18   -18391.256571        0.0296\n",
-      "BFGSLineSearch:   23[ 46] 15:38:53   -18391.256651        0.0230\n",
-      "BFGSLineSearch:   24[ 48] 15:39:28   -18391.256700        0.0238\n",
-      "BFGSLineSearch:   25[ 50] 15:40:02   -18391.256791        0.0237\n",
-      "BFGSLineSearch:   26[ 52] 15:40:37   -18391.256819        0.0183\n",
-      "BFGSLineSearch:   27[ 54] 15:41:12   -18391.256859        0.0086\n",
-      "CPU times: user 2h 50min 12s, sys: 5min 56s, total: 2h 56min 8s\n",
-      "Wall time: 15min 59s\n"
+      "*Force-consistent energies used in optimization.\n",
+      "BFGSLineSearch:    0[  0] 13:34:20        3.054451*      39.6081\n",
+      "BFGSLineSearch:    1[  1] 13:34:20       -2.442459*       1.4363\n",
+      "BFGSLineSearch:    2[  3] 13:34:20       -2.504294*       0.3803\n",
+      "BFGSLineSearch:    3[  5] 13:34:20       -2.506296*       0.0708\n",
+      "BFGSLineSearch:    4[  6] 13:34:20       -2.506439*       0.0029\n",
+      "CPU times: user 35.5 ms, sys: 20.8 ms, total: 56.3 ms\n",
+      "Wall time: 342 ms\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
     "atoms.calc = calc\n",
     "dyn = QuasiNewton(atoms)\n",
-    "dyn.run(fmax=0.01)"
+    "with redirect_stderr(devnull):\n",
+    "    dyn.run(fmax=0.01)"
    ]
   },
   {
@@ -295,8 +259,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4h 37min 32s, sys: 9min 54s, total: 4h 47min 27s\n",
-      "Wall time: 26min 18s\n"
+      "CPU times: user 26.8 ms, sys: 42.5 ms, total: 69.4 ms\n",
+      "Wall time: 782 ms\n"
      ]
     }
    ],
@@ -349,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "5.5067174465850215"
+       "0.4429860245936006"
       ]
      },
      "execution_count": 13,
@@ -372,83 +336,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "577cd427-7829-4f7b-8457-a6c709c3ea80",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "analytic_time = perf_counter()\n",
-    "calc.set_psi4(atoms)\n",
-    "hess = calc.psi4.hessian(f'{method}/{basis}')\n",
-    "analytic_time = perf_counter() - analytic_time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d611176-e94d-44de-a57b-6750625463a1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "analytic_hess = hess.to_array() * units.Hartree / units.Bohr / units.Bohr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9567ec44-fda4-4052-87dc-c6c257adbc4e",
-   "metadata": {},
-   "source": [
-    "Convert it to an ASE object and save"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a767054-2b20-4a87-8f51-5e715d71d539",
+   "execution_count": 14,
+   "id": "da5a4aea-134d-4950-8dbd-df83338983a8",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "vib_data = VibrationsData.from_2d(atoms, analytic_hess)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5053fa4-e301-472a-8982-1b02c7727d90",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "vib_data = vib.get_vibrations()\n",
-    "with (out_dir / f'{run_name}-psi4.json').open('w') as fp:\n",
-    "    vib_data.write(fp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36d8ad22-2ad6-45c0-ba9d-0aeed39cb41f",
-   "metadata": {},
-   "source": [
-    "Print the ZPE for reference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e9422b7-a62d-49b7-af4a-b433d24804ae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "vib_data.get_zero_point_energy()"
+    "if isinstance(calc, Psi4):\n",
+    "    # Compute\n",
+    "    analytic_time = perf_counter()\n",
+    "    calc.set_psi4(atoms)\n",
+    "    hess = calc.psi4.hessian(f'{method}/{basis}')\n",
+    "    analytic_time = perf_counter() - analytic_time\n",
+    "\n",
+    "    # Convert to ASE format\n",
+    "    analytic_hess = hess.to_array() * units.Hartree / units.Bohr / units.Bohr\n",
+    "    vib_data = VibrationsData.from_2d(atoms, analytic_hess)\n",
+    "    with (out_dir / f'{run_name}-psi4.json').open('w') as fp:\n",
+    "        vib_data.write(fp)\n",
+    "else:\n",
+    "    analytic_time = None"
    ]
   },
   {
@@ -461,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "273b5116-06ec-4098-8b20-537d1f9d7e84",
    "metadata": {
     "tags": []
@@ -501,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/proof-of-concept/1_compute-random-offsets.ipynb b/notebooks/proof-of-concept/1_compute-random-offsets.ipynb
index 10ae373..d6ac780 100644
--- a/notebooks/proof-of-concept/1_compute-random-offsets.ipynb
+++ b/notebooks/proof-of-concept/1_compute-random-offsets.ipynb
@@ -18,6 +18,7 @@
    },
    "outputs": [],
    "source": [
+    "from ase.calculators.mopac import MOPAC\n",
     "from ase.calculators.psi4 import Psi4\n",
     "from ase.io import write, read\n",
     "from ase.db import connect\n",
@@ -48,10 +49,12 @@
    "outputs": [],
    "source": [
     "molecule_name = 'caffeine'\n",
-    "method = 'hf'\n",
-    "basis = 'def2-svpd'\n",
+    "method = 'pm7'\n",
+    "mopac_methods = ['pm7']  # Use MOPAC for these methods\n",
+    "basis = None  # Set to None for MOPAC methods\n",
     "threads = min(os.cpu_count(), 12)\n",
-    "step_size: float = 0.01 # Perturbation amount, used as maximum L2 norm"
+    "step_size: float = 0.01 # Perturbation amount, used as maximum L2 norm\n",
+    "assert (method in mopac_methods) == (basis is None), 'Use a basis of None for MOPAC computations'"
    ]
   },
   {
@@ -184,19 +187,12 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  Memory set to   3.815 GiB by Python driver.\n",
-      "  Threads set to 12 by Python driver.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')"
+    "if method not in mopac_methods:\n",
+    "    calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')\n",
+    "else:\n",
+    "    calc = MOPAC(method=method, command='mopac PREFIX.mop > /dev/null')"
    ]
   },
   {
@@ -241,7 +237,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Already done 427. 2274 left to do.\n"
+      "Already done 1. 2700 left to do.\n"
      ]
     }
    ],
@@ -253,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "a6fa1b33-defc-4b35-895d-052eb64453fb",
    "metadata": {},
    "outputs": [
@@ -261,23 +257,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                                                                                                                                                                                                            | 0/2701 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  Memory set to   3.815 GiB by Python driver.\n",
-      "  Threads set to 12 by Python driver.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                                                                                                | 1600/2701 [2:06:01<2:33:24,  8.36s/it]"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2701/2701 [03:10<00:00, 19.27it/s]"
      ]
     }
    ],
diff --git a/notebooks/proof-of-concept/2_approximate-hessians.ipynb b/notebooks/proof-of-concept/2_approximate-hessians.ipynb
index 505d56e..948b05e 100644
--- a/notebooks/proof-of-concept/2_approximate-hessians.ipynb
+++ b/notebooks/proof-of-concept/2_approximate-hessians.ipynb
@@ -50,8 +50,8 @@
    "outputs": [],
    "source": [
     "molecule_name = 'caffeine'\n",
-    "method = 'hf'\n",
-    "basis = 'def2-svpd'\n",
+    "method = 'pm7'\n",
+    "basis = None\n",
     "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm\n",
     "regressor = LinearRegression"
    ]
diff --git a/notebooks/proof-of-concept/3_approximate-hessians-with-mbtr.ipynb b/notebooks/proof-of-concept/3_approximate-hessians-with-mbtr.ipynb
index 5fd8f8c..11c646e 100644
--- a/notebooks/proof-of-concept/3_approximate-hessians-with-mbtr.ipynb
+++ b/notebooks/proof-of-concept/3_approximate-hessians-with-mbtr.ipynb
@@ -101,7 +101,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded 1457 structures\n"
+      "Loaded 2701 structures\n"
      ]
     }
    ],
@@ -174,8 +174,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4min 28s, sys: 7.75 s, total: 4min 35s\n",
-      "Wall time: 23.4 s\n"
+      "CPU times: user 6min 33s, sys: 22.4 s, total: 6min 56s\n",
+      "Wall time: 35.2 s\n"
      ]
     }
    ],
@@ -241,7 +241,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -296,7 +296,7 @@
     {
      "data": {
       "text/plain": [
-       "{'alpha': 1e-10, 'gamma': 2.1017480113324872e-05}"
+       "{'alpha': 1e-10, 'gamma': 9.284145445194744e-05}"
       ]
      },
      "execution_count": 11,
@@ -352,7 +352,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Maximum force: 8.96e-03 eV/Angstrom\n"
+      "Maximum force: 9.25e-03 eV/Angstrom\n"
      ]
     }
    ],
@@ -370,7 +370,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 400x200 with 2 Axes>"
       ]
@@ -411,8 +411,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 47.7 s, sys: 33.7 ms, total: 47.7 s\n",
-      "Wall time: 47.7 s\n"
+      "CPU times: user 40.6 s, sys: 15.1 ms, total: 40.6 s\n",
+      "Wall time: 40.6 s\n"
      ]
     }
    ],
@@ -465,9 +465,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[1.90039475e+01, 2.26533979e+01, 2.14746706e-02],\n",
-       "       [2.26533979e+01, 8.32193023e+01, 1.32040896e-02],\n",
-       "       [2.14746706e-02, 1.32040896e-02, 3.78143259e+00]])"
+       "array([[1.99647797e+01, 2.29521229e+01, 6.85504347e-03],\n",
+       "       [2.29521229e+01, 8.39212309e+01, 1.31434717e-02],\n",
+       "       [6.85504347e-03, 1.31434717e-02, 4.35586953e+00]])"
       ]
      },
      "execution_count": 18,
@@ -489,7 +489,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x200 with 2 Axes>"
       ]
@@ -541,7 +541,7 @@
     {
      "data": {
       "text/plain": [
-       "5.132369908274389"
+       "5.41561627027977"
       ]
      },
      "execution_count": 21,
@@ -626,7 +626,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Plotting at 16 steps: 5, 101, 198, 295, 392, ...\n"
+      "Plotting at 16 steps: 5, 184, 364, 544, 723, ...\n"
      ]
     }
    ],
@@ -647,7 +647,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [15:54<00:00, 59.66s/it]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [19:55<00:00, 74.72s/it]\n"
      ]
     }
    ],
@@ -681,7 +681,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x200 with 1 Axes>"
       ]
diff --git a/pyproject.toml b/pyproject.toml
index 03604e5..428d413 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,7 @@ classifiers = [
 dependencies = [
     "colmena==0.5.*",
     "parsl>=2023.04",
-    "ase==3.22.*",
+    "ase>3.22",
     "tqdm"
 ]