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Tutorial 1: Thermochemical Property Estimation (Enthalpy, Entropy, and Gibbs Free Energy for Gases)

In this tutorial you will learn how to use ASE [7] and MACE-OFF [2] to estimate Thermohemical Properties of Atoms and Molecules using a Machine Learning Potential (MACE-OFF)

Code Overview:

This code will compute Chemical Properties of Propanol. First, the molecule is built using the SMILES identificiation. The code then runs a simulations for the molecule which allows the chemical properties to be calculated. Then the code will display the results.

The libraries / packages listed in cell 2 will have a brief explanation of their function in the code, but for more information, please use the links below

Library / PackageLink to Documentations
numpyNumPy Documentation [8]
pandasPandas Documentation [9]
chemrdkit.chem Documentation [10]
AllChemSame Link as above
mace_offMACE Calculator Documentation [11]
mace_offMACE Descriptors Documentation [12]
atomsAtoms Object Documentation [13]
moleculeMolecules Documentation [14]
QuasiNewtonStructure Optimization Documentation [15]
VibrationsVibrational Modes Documentation [16]
IdealGasThermoIdeal-gas limit Documentation [17]
unitsUnits Documentation [18]

Click the button below to open this code in Google Colab

Open in Colab

Outputs Should Appear Like This:

# Cell 1: Install Required Packages

!pip install ASE
!pip install mace-torch ase rdkit weas-widget
Expected output: Required packages installed

Collecting ASE
  Downloading ase-3.29.0-py3-none-any.whl.metadata (4.4 kB)
Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.12/dist-packages (from ASE) (2.0.2)
Requirement already satisfied: scipy>=1.8.1 in /usr/local/lib/python3.12/dist-packages (from ASE) (1.16.3)
Requirement already satisfied: matplotlib>=3.5.2 in /usr/local/lib/python3.12/dist-packages (from ASE) (3.10.0)
Requirement already satisfied: typing_extensions>=4.13.1 in /usr/local/lib/python3.12/dist-packages (from ASE) (4.15.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (4.63.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (1.5.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (26.2)
Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (11.3.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (3.3.2)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.5.2->ASE) (2.9.0.post0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3.5.2->ASE) (1.17.0)
Downloading ase-3.29.0-py3-none-any.whl (3.0 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.0/3.0 MB 32.3 MB/s eta 0:00:00
Installing collected packages: ASE
Successfully installed ASE-3.29.0
Collecting mace-torch
  Downloading mace_torch-0.3.16-py3-none-any.whl.metadata (27 kB)
Requirement already satisfied: ase in /usr/local/lib/python3.12/dist-packages (3.29.0)
Collecting rdkit
  Downloading rdkit-2026.3.3-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (3.8 kB)
Collecting weas-widget
  Downloading weas_widget-0.2.6-py3-none-any.whl.metadata (13 kB)
Requirement already satisfied: torch>=1.12 in /usr/local/lib/python3.12/dist-packages (from mace-torch) (2.11.0+cpu)
Collecting e3nn==0.4.4 (from mace-torch)
  Downloading e3nn-0.4.4-py3-none-any.whl.metadata (5.1 kB)
Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from mace-torch) (2.0.2)
Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.4.0)
Collecting torch-ema (from mace-torch)
  Downloading torch_ema-0.3-py3-none-any.whl.metadata (415 bytes)
Requirement already satisfied: prettytable in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.17.0)
Collecting matscipy (from mace-torch)
  Downloading matscipy-1.2.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (37 kB)
Requirement already satisfied: h5py in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.16.0)
Collecting torchmetrics (from mace-torch)
  Downloading torchmetrics-1.9.0-py3-none-any.whl.metadata (23 kB)
Collecting python-hostlist (from mace-torch)
  Downloading python_hostlist-2.3.0.tar.gz (37 kB)
  Preparing metadata (setup.py) ... done
Collecting configargparse (from mace-torch)
  Downloading configargparse-1.7.5-py3-none-any.whl.metadata (23 kB)
Requirement already satisfied: GitPython in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.1.50)
Requirement already satisfied: pyYAML in /usr/local/lib/python3.12/dist-packages (from mace-torch) (6.0.3)
Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from mace-torch) (4.67.3)
Collecting lmdb (from mace-torch)
  Downloading lmdb-2.2.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (1.2 kB)
Requirement already satisfied: orjson in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.11.9)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from mace-torch) (3.10.0)
Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from mace-torch) (2.2.2)
Requirement already satisfied: sympy in /usr/local/lib/python3.12/dist-packages (from e3nn==0.4.4->mace-torch) (1.14.0)
Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from e3nn==0.4.4->mace-torch) (1.16.3)
Collecting opt-einsum-fx>=0.1.4 (from e3nn==0.4.4->mace-torch)
  Downloading opt_einsum_fx-0.1.4-py3-none-any.whl.metadata (3.3 kB)
Requirement already satisfied: typing_extensions>=4.13.1 in /usr/local/lib/python3.12/dist-packages (from ase) (4.15.0)
Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from rdkit) (11.3.0)
Requirement already satisfied: anywidget>=0.9.11 in /usr/local/lib/python3.12/dist-packages (from weas-widget) (0.9.21)
Collecting appdirs>=1.4.4 (from weas-widget)
  Downloading appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)
Requirement already satisfied: click>=8.1.7 in /usr/local/lib/python3.12/dist-packages (from weas-widget) (8.4.1)
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Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=1.12->mace-torch) (75.2.0)
Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.12->mace-torch) (3.6.1)
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Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.12/dist-packages (from GitPython->mace-torch) (4.0.12)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->mace-torch) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->mace-torch) (2026.2)
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Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.12/dist-packages (from scikit-image->weas-widget) (0.5)
Collecting lightning-utilities>=0.15.3 (from torchmetrics->mace-torch)
  Downloading lightning_utilities-0.15.3-py3-none-any.whl.metadata (5.5 kB)
Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.12/dist-packages (from gitdb<5,>=4.0.1->GitPython->mace-torch) (5.0.3)
Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (6.17.1)
Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (0.2.0)
Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (5.7.1)
Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (3.6.10)
Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (7.34.0)
Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (3.0.16)
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Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget) (5.9.5)
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Collecting jedi>=0.16 (from ipython>=4.0.0->ipywidgets>=7.6.0->anywidget>=0.9.11->weas-widget)
  Downloading jedi-0.20.0-py2.py3-none-any.whl.metadata (23 kB)
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Cell 1 Purpose: Install packages for ASE and MACE-OFF.

# Cell 2: Import Required Libraries

import numpy as np                              # Computational library
import pandas as pd                             # Excel of python

from rdkit import Chem                          # Used to build molecules (basic package)
from rdkit.Chem import AllChem                  # Used to build molecules (advanced package)

from mace.calculators import mace_off           # MACE-OFF (Machine Learning Potential)

# Atomic Simulation Environment Libraries
from ase import Atoms                           # Represents a molecule object with information
from ase.build import molecule                  # Creates an atomic structure from the database
from ase.optimize import QuasiNewton            # Optimization / energy minimization
from ase.vibrations import Vibrations           # Used to calculate vibrational modes of the Atom object
from ase.thermochemistry import IdealGasThermo  # Allows you to calculate entropy, enthalpy, and gibbs free energy
from ase.units import kJ, mol                   # Conversion for units
Expected output: Libraries imported successfully
/usr/local/lib/python3.12/dist-packages/e3nn/o3/_wigner.py:10: UserWarning: Environment variable TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD detected, since the`weights_only` argument was not explicitly passed to `torch.load`, forcing weights_only=False.
  _Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(__file__), 'constants.pt'))
cuequivariance or cuequivariance_torch is not available. Cuequivariance acceleration will be disabled.

Cell 2 Purpose: Import required libraries to run codes in subsequent cells.

# Cell 3: Load MACE-OFF

print("Loading MACE-OFF (medium model)...")
calc_mol = mace_off(model="small", default_dtype="float64")
print("MACE-OFF loaded.")
Expected output: MACE-OFF model loaded

Loading MACE-OFF (medium model)...
Downloading MACE model from 'https://raw.githubusercontent.com/ACEsuit/mace-off/main/mace_off23/MACE-OFF23_small.model'
The model is distributed under the Academic Software License (ASL) license, see https://github.com/gabor1/ASL 
 To use the model you accept the terms of the license.
ASL is based on the Gnu Public License, but does not permit commercial use
Downloading: 100.0% (7.0 MB / 7.0 MB)
Cached MACE model to /root/.cache/mace/MACE-OFF23_small.model
Using MACE-OFF23 MODEL for MACECalculator with /root/.cache/mace/MACE-OFF23_small.model
Using float64 for MACECalculator, which is slower but more accurate. Recommended for geometry optimization.
/usr/local/lib/python3.12/dist-packages/mace/calculators/mace.py:226: UserWarning: Environment variable TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD detected, since the`weights_only` argument was not explicitly passed to `torch.load`, forcing weights_only=False.
  torch.load(f=model_path, map_location=device)
MACE-OFF loaded.

Cell 3 Purpose: Load MACE-OFF medium model to get the energy of molecules that are simulated.

# Cell 4: Calculate Propanol (CCCO) Chemical Properties
# Build the Molecule using SMILES

smiles = 'CCCO' # SMILES for Propanol (CCCO)
seed = 42

mol = Chem.MolFromSmiles(smiles)
mol = Chem.AddHs(mol) # Adds explicit hydrogens to the molecule
AllChem.EmbedMolecule(mol, randomSeed=seed) # Randomly places atoms at correct distances from each other
AllChem.MMFFOptimizeMolecule(mol) # Optimizes the geometry using the MMFF94 classical force field (Starting Position for MACE-OFF optimization)
conf = mol.GetConformer()
symbols = [a.GetSymbol() for a in mol.GetAtoms()]
positions = conf.GetPositions()

atoms_CCCO = Atoms(symbols=symbols, positions=positions)

atoms_CCCO.calc = calc_mol # Calls to MACE-OFF to be used
dyn = QuasiNewton(atoms_CCCO, logfile=None)
dyn.run(fmax=0.01) # Finding energy minimum
potentialenergy_CCCO = atoms_CCCO.get_potential_energy() # Get potential energy

vib = Vibrations(atoms_CCCO, name='ccco_vib')
vib.clean()
vib.run()
vib_energies = vib.get_energies() # Gets vibrational energy based on molecule geometry
vib_energies = np.array([e.real for e in vib_energies if e.real > 0.01]) # Filters out imaginary numbers and very low frequencies

# Takes inputs of vibrational energies, potential energy, and geometry to compute chemical properties
thermo = IdealGasThermo(
    vib_energies=vib_energies,
    potentialenergy=potentialenergy_CCCO,
    atoms=atoms_CCCO,
    geometry='nonlinear', # Linear (Straight Line) or Nonlinear (Bent in any way)
    symmetrynumber=1, # How many times you can rotate the molecule and get the same configuration
    spin=0, # 0.5 for each unpaired electrons
)

Conv_eV_kJ = 96.485 # eV -> kJ

# Records Chemical Property Data at 6 Temperatures at 1 atmosphere and Displays it
temps = [298.15, 400, 500, 600, 700, 800]
P = 101325.

records = []
for T in temps:
    H = thermo.get_enthalpy(T, verbose=False) * Conv_eV_kJ
    S = thermo.get_entropy(T, P, verbose=False) * Conv_eV_kJ
    G = thermo.get_gibbs_energy(T, P, verbose=False) * Conv_eV_kJ
    records.append({"T (K)": T, "H (kJ)": H, "S (kJ/K)": S, "G (kJ)": G})

df1 = pd.DataFrame(records)
display(df1)
Expected output: Thermodynamic properties of propanol
      T(K)	   H (kJ/mol)	     S (kJ/mol K)	 G (kJ)
0	    298.15	-510303.027435	 0.301001	    -510392.770753
1	    400.00	-510293.228129	 0.329111	    -510424.872461
2	    500.00	-510281.390242	 0.355439	    -510459.109522
3	    600.00	-510267.574571	 0.380577	    -510495.920554
4	    700.00	-510252.060535	 0.404461	    -510535.182949
5	    800.00	-510235.093697	 0.427097	    -510576.771020

Cell 4 Purpose: Build the Propanol molecule, minimize the force on the molecule, obtain energies using MACE-OFF potential. Then you use this energy and ASE to compute the properties including enthalpy, entropy, and gibbs free energy at various temperatures.

Propanol

SMILES: CCCO

Chemical Formula: C3H8O

Click the power icon and then run the cell below to view an interactive model of a Propanol Molecule

import micropip
await micropip.install("py3Dmol")

from pyodide.http import pyfetch
import py3Dmol


response = await pyfetch("https://raw.githubusercontent.com/cacherowan/CACHE-Rowan/main/Reference_Files/Molecules_To_View/Propanol(C3H8O).xyz")
xyz = await response.string()

view = py3Dmol.view(width=800, height=400)
view.addModel(xyz, "xyz")
view.setStyle({"stick": {}, "sphere": {"scale": 0.3}})
view.zoomTo()
view.zoom(2)
view.show()

Discussion / Analysis

In this code, you were able to calculate the Enthalpy, Entropy, and Gibbs Free Energy of Propanol by explicitly defining the geometry and constructing the molecule.

Molecular Dynamics Simulation Background

Background information of Molecular Dynamics Simulations

Tutorial 2: Heat Capacity for Gases

Perform multiple molecular simulations using MACE-OFF, which will be used to calculate the heat capacity of multiple molecules. Also some of the molecules will be constructed using RDKit.

Module 1 Chemical Property Prediction
Molecular Dynamics Background
Module 1 Chemical Property Prediction
Tutorial 2: Thermochemical Property Estimation (Heat Capacity for Gases)