Tutorial 4: Model Validation Metrics - MOL2LIFE: MOLecular-to-Life-cycle Intelligence Framework for Early process design

The Standard Enthalpy of Formation is the energy required to turn 1 mole of base elements in their natural state into 1 mole of the molecule you are computing this for. When using MACE-OFF to get these values, you need to calculate the enthalpy of the desired molecule and subtract the enthalpy of the same amount of atoms in their base elements in their natural state. For example to compute the Standard Enthalpy of Formation for water (H₂O), you would compute the enthalpy of a water molecule and subtract the base elements in their natural state (O₂ and H₂). The equation ends up being:

Standard Enthalpy of Formation of H₂O = H(H₂O) - H(H₂) - $\frac{1}{2}$ H(O₂)

Code Overview:¶

The Code below will go through using Atomic Simulation Environment (ASE) and Multi Atomic Cluster Expansion - Organic Force Field (MACE-OFF) to calculate the Standard Enthalpy of Formation of Molecules including, Ammonia, Propanol, Methanol, Propane, and Thiophene. Then you will see how to use the Database of Chemical Properties to obtain the Standard Enthalpy of Formation of the same molecules, and then finally compare the values from MACE-OFF and ASE against the Database values which are based on the National Institute of Standards and Technologies (NIST) Database.

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 / Package	Link to Documentations
numpy	NumPy Documentation [8]
matplotlib.pyplot	Matplotlib.pyplot [19]
pandas	Pandas Documentation [9]
chem	rdkit.chem Documentation [10]
AllChem	Same link as above [10]
mace_off	MACE Calculator Documentation [11]
mace_off	MACE Descriptors Documentation [12]
atoms	Atoms Object Documentation [13]
molecule	Molecules Documentation [14]
QuasiNewton	Structure Optimization Documentation [15]
Vibrations	Vibrational Modes Documentation [16]
IdealGasThermo	Ideal-gas limit Documentation [17]
kJ, mol	Units Documentation [18]

Click the link below to open the Colab notebook:

Outputs Should Appear Like This:¶

# Cell 1: Install Required Packages

!pip install ASE
!pip install mace-torch ase rdkit weas-widget

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Successfully installed appdirs-1.4.4 configargparse-1.7.5 e3nn-0.4.4 jedi-0.20.0 lightning-utilities-0.15.3 lmdb-2.3.0 mace-torch-0.3.16 matscipy-1.2.0 opt-einsum-fx-0.1.4 python-hostlist-2.3.0 rdkit-2026.3.4 torch-ema-0.3 torchmetrics-1.9.0 weas-widget-0.2.6

Cell 1 Purpose: Install packages for ASE and MACE-OFF.

# Cell 2: Import Required Libraries

# General Libraries
import numpy as np                              # Stores numbers in arrays and runs fast calculations on them
import matplotlib.pyplot as plt                 # Plot Graphs
import pandas as pd                             # Opens the data file as a table and find rows by their SMILES string

# Building Geometry of Molecules / Atoms
from rdkit import Chem                          # Used to build molecules (basic package)
from rdkit.Chem import AllChem                  # Used to build molecules (advanced package)

# MACE Library
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

/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="medium", default_dtype="float64")
print("MACE-OFF loaded.")

Loading MACE-OFF (medium model)...
Downloading MACE model from 'https://raw.githubusercontent.com/ACEsuit/mace-off/main/mace_off23/MACE-OFF23_medium.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% (17.5 MB / 17.5 MB)
Cached MACE model to /root/.cache/mace/MACE-OFF23_medium.model
Using MACE-OFF23 MODEL for MACECalculator with /root/.cache/mace/MACE-OFF23_medium.model
Using float64 for MACECalculator, which is slower but more accurate. Recommended for geometry optimization.
MACE-OFF loaded.
/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)

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

# Cell 4: Define Function to Compute Chemical Properties

# Geometry, Symmetry, and Spin are used to get accurate properties
def compute_thermo(
    atoms,                      # Attach atoms / molecule
    calc,                       # Attach calculator (MACE-OFF)
    geometry,                   # (Linear or nonlinear)
    symmetrynumber,             # Symmetry of molecule
    spin,                       # 0.5 for each unpaired electron
    temperature=298.15,
    vib_name='molecule_vib',
    fmax=0.01,                  # Force threshold
    vib_energy_threshold=0.01,
    verbose=False
):
    """
    Compute potential energy and enthalpy for a molecule.

    Parameters
    ----------
    atoms : ase.Atoms
        The molecule to compute thermodynamics for.
    calc : ASE calculator
        The calculator to use (e.g. EMT, GPAW, etc.).
    geometry : str
        'linear' or 'nonlinear' — molecular geometry type.
    symmetrynumber : int
        Rotational symmetry number (e.g. 3 for NH3, 2 for H2).
    spin : float
        Total spin (0 for closed-shell, 0.5 per unpaired electron).
    temperature : float
        Temperature in Kelvin. Default: 298.15.
    vib_name : str
        Prefix for vibrational frequency cache files.
    fmax : float
        Force convergence criterion for geometry optimization (eV/Å).
    vib_energy_threshold : float
        Minimum vibrational energy (eV) to include; filters near-zero/imaginary modes.
    verbose : bool
        Whether to print thermochemistry details.

    Returns
    -------
    dict with keys:
        'potential_energy'  : float, eV
        'enthalpy_eV'       : float, eV
        'enthalpy_kJ_mol'   : float, kJ/mol
    """
    # Attach Calculator and Optimize Geometry
    atoms.calc = calc
    dyn = QuasiNewton(atoms, logfile=None)
    dyn.run(fmax=fmax)

    potential_energy = atoms.get_potential_energy()

    # Compute Vibrational Frequencies
    vib = Vibrations(atoms, name=vib_name)
    vib.clean()
    vib.run()
    vib_energies = vib.get_energies()

    # Filter Out Imaginary and Near-zero Modes
    vib_energies = np.array([
        e.real for e in vib_energies
        if e.real > vib_energy_threshold
    ])

    # Compute Chemical Properties
    thermo = IdealGasThermo(
        vib_energies=vib_energies,
        potentialenergy=potential_energy,
        atoms=atoms,
        geometry=geometry,
        symmetrynumber=symmetrynumber,
        spin=spin,
    )

    enthalpy_eV = thermo.get_enthalpy(temperature=temperature, verbose=verbose)
    enthalpy_kJ_mol = enthalpy_eV * (1 / (kJ / mol))

    return {
        'potential_energy': potential_energy,
        'enthalpy_eV': enthalpy_eV,
        'enthalpy_kJ_mol': enthalpy_kJ_mol,
    }

Cell 4 Purpose: Create a function to pass MACE-OFF energy data from simulations and compute chemical properties using that data, ASE, and IdealGasThermo class.

# Cell 5: Compute Chemical Properties for Ammonia (NH3)

results = compute_thermo(
    atoms=molecule('NH3'),
    calc=calc_mol,
    geometry='nonlinear',
    symmetrynumber=3,
    spin=0,
    temperature=298.15,
    vib_name='nh3_vib',
)

potentialenergy_NH3 = results['potential_energy']
enthalpy_NH3_eV = results['enthalpy_eV']
enthalpy_NH3_kJ_mol = results['enthalpy_kJ_mol']

print(f"NH3 Potential Energy: {potentialenergy_NH3:.4f} eV = {potentialenergy_NH3 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of NH3: {enthalpy_NH3_eV:.4f} eV = ")
print(f"Enthalpy of NH3 at 298 K: {enthalpy_NH3_kJ_mol:.4f} kJ/mol")

Cell 5 Purpose: Utilize the result function created above to compute chemical properties for ammonia.

Ammonia¶

SMILES: N

Chemical Formula: NH₃

Click the power icon and then run the cell below to view an interactive model of an Ammonia 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/Ammonia(NH3).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()

# Cell 6: Compute Chemical Properties for CH3CHOHCH3 (Propanol)
# Create Molecule from SMILES, optimize geometry, and compute properties

# Propanol is not in the G2 database, so we need to create the geometry from the SMILES string and optimize it before computing thermochemistry.

def smiles_to_atoms(smiles, seed=64):
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol) # Adds explicit hydrogens to the molecule
    AllChem.EmbedMolecule(mol, randomSeed=64) # 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()
    return Atoms(symbols=symbols, positions=positions)

atoms_iPrOH = smiles_to_atoms('CCCO')

results = compute_thermo(
    atoms=atoms_iPrOH,
    calc=calc_mol,
    geometry='nonlinear',
    symmetrynumber=1,
    spin=0,
    temperature=298.15,
    vib_name='1propanol_vib',
)

potentialenergy_CH3CHOHCH3 = results['potential_energy']
enthalpy_CH3CHOHCH3_eV = results['enthalpy_eV']
enthalpy_CH3CHOHCH3_kJ_mol = results['enthalpy_kJ_mol']

print(f"1-Propanol Potential Energy: {potentialenergy_CH3CHOHCH3:.4f} eV = {potentialenergy_CH3CHOHCH3 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of CH3CHOHCH3: {enthalpy_CH3CHOHCH3_eV:.4f} eV")
print(f"Enthalpy of CH3CHOHCH3 at 298 K: {enthalpy_CH3CHOHCH3_kJ_mol:.4f} kJ/mol")

Cell 6 Purpose: Utilize the result function created above to compute chemical properties for propanol.

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()

# Cell 7: Compute Chemical Properties for Methanol (CH3OH)

results = compute_thermo(
    atoms=molecule('CH3OH'),
    calc=calc_mol,
    geometry='nonlinear',
    symmetrynumber=1,
    spin=0,
    temperature=298.15,
    vib_name='ch3oh_vib',
)

potentialenergy_CH3OH = results['potential_energy']
enthalpy_CH3OH_eV = results['enthalpy_eV']
enthalpy_CH3OH_kJ_mol = results['enthalpy_kJ_mol']

print(f"CH3OH Potential Energy: {potentialenergy_CH3OH:.4f} eV = {potentialenergy_CH3OH * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of CH3OH: {enthalpy_CH3OH_eV:.4f} eV")
print(f"Enthalpy of CH3OH at 298 K: {enthalpy_CH3OH_kJ_mol:.4f} kJ/mol")

Cell 7 Purpose: Utilize the result function created above to compute chemical properties for methanol.

Methanol¶

SMILES: CO

Chemical Formula: CH₄O

Click the power icon and then run the cell below to view an interactive model of a Methanol 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/Methanol(CH4O).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()

# Cell 8: Compute Chemical Properties for C3H8 (Propane)

results = compute_thermo(
    atoms=molecule('C3H8'),
    calc=calc_mol,
    geometry='nonlinear',
    symmetrynumber=2,
    spin=0,
    temperature=298.15,
    vib_name='c3h8_vib',
)

potentialenergy_C3H8 = results['potential_energy']
enthalpy_C3H8_eV = results['enthalpy_eV']
enthalpy_C3H8_kJ_mol = results['enthalpy_kJ_mol']

print(f"C3H8 Potential Energy: {potentialenergy_C3H8:.4f} eV = {potentialenergy_C3H8 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of C3H8: {enthalpy_C3H8_eV:.4f} eV")
print(f"Enthalpy of C3H8 at 298 K: {enthalpy_C3H8_kJ_mol:.4f} kJ/mol")

Cell 8 Purpose: Utilize the result function created above to compute chemical properties for propane.

Propane¶

SMILES: CCC

Chemical Formula: C₃H₈

Click the power icon and then run the cell below to view an interactive model of a Propane 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/Propane(C3H8).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()

# Cell 9: Compute Chemical Properties for C4H4S (Thiophene)

results = compute_thermo(
    atoms=molecule('C4H4S'),
    calc=calc_mol,
    geometry='nonlinear',
    symmetrynumber=2,
    spin=0,
    temperature=298.15,
    vib_name='c4h4s_vib',
)

potentialenergy_C4H4S = results['potential_energy']
enthalpy_C4H4S_eV = results['enthalpy_eV']
enthalpy_C4H4S_kJ_mol = results['enthalpy_kJ_mol']

print(f"C4H4S Potential Energy: {potentialenergy_C4H4S:.4f} eV = {potentialenergy_C4H4S * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of C4H4S: {enthalpy_C4H4S_eV:.4f} eV")
print(f"Enthalpy of C4H4S at 298 K: {enthalpy_C4H4S_kJ_mol:.4f} kJ/mol")

Cell 9 Purpose: Utilize the result function created above to compute chemical properties for thiophene.

Thiophene¶

SMILES: c1ccsc1

Chemical Formula: C₄H₄S

Click the power icon and then run the cell below to view an interactive model of a Thiophene 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/Thiophene(C4H4S).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()

Compute Molecular Properties for Standard Forms¶

# Cell 10: Carbon Properties (Assumed to be Graphite, anchored via experimental sublimation enthalpy using NIST)

H_sub_C_kJ = 716.7  # Experimental enthalpy of sublimation: C(graphite) -> C(g), NIST

c_atom = Atoms('C', positions=[[10, 10, 10]], cell=[20, 20, 20], pbc=False)
c_atom.calc = calc_mol # Attach Calculator (MACE-OFF)
E_C = c_atom.get_potential_energy()

# Convert to kJ/mol and add translational thermal contribution (5/2 RT) for consistency
R = 8.314462618e-3  # kJ/(mol K)
T = 298.15
E_C_kJ = E_C * (1 / (kJ / mol))
H_C_atom_kJ = E_C_kJ + (5/2) * R * T # Thermal correction for C atom (Typically IdealGasThermo handles this)

# Anchor to graphite using the sublimation enthalpy cycle:
# E_C(atom) = H(C, graphite) + H_sub  =>  H(C, graphite) = H(C, atom) - H_sub
H_graphite_kJ = H_C_atom_kJ - H_sub_C_kJ

print(f"C atom potential energy: {E_C:.4f} eV = {E_C * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of C atom at 298 K: {H_C_atom_kJ:.4f} kJ/mol")
print(f"Enthalpy of C (graphite) at 298 K: {H_graphite_kJ:.4f} kJ/mol")

Cell 10 Purpose: Computes the energy of carbon gas, and adds the energy to turn directly into a solid (sublimation). Getting the enthalpy of carbon solid is required here to adhere to the rules of standard enthalpy of formation being based on the standard state of a substance.

# Cell 11: Compute Chemical Properties for H2

results = compute_thermo(
    atoms=molecule('H2'),
    calc=calc_mol,
    geometry='linear',
    symmetrynumber=2,
    spin=0,
    temperature=298.15,
    vib_name='h2_vib',
)

potentialenergy_H2 = results['potential_energy']
enthalpy_H2_eV = results['enthalpy_eV']
enthalpy_H2_kJ_mol = results['enthalpy_kJ_mol']

print(f"H2 Potential Energy: {potentialenergy_H2:.4f} eV = {potentialenergy_H2 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of H2: {enthalpy_H2_eV:.4f} eV")
print(f"Enthalpy of H2 at 298 K: {enthalpy_H2_kJ_mol:.4f} kJ/mol")

Cell 11 Purpose: Utilize the result function created above to compute chemical properties for H₂.

# Cell 12: Compute Chemical Properties for N2

results = compute_thermo(
    atoms=molecule('N2'),
    calc=calc_mol,
    geometry='linear',
    symmetrynumber=2,
    spin=0,
    temperature=298.15,
    vib_name='n2_vib',
)

potentialenergy_N2 = results['potential_energy']
enthalpy_N2_eV = results['enthalpy_eV']
enthalpy_N2_kJ_mol = results['enthalpy_kJ_mol']

print(f"N2 Potential Energy: {potentialenergy_N2:.4f} eV = {potentialenergy_N2 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of N2: {enthalpy_N2_eV:.4f} eV")
print(f"Enthalpy of N2 at 298 K: {enthalpy_N2_kJ_mol:.4f} kJ/mol")

Cell 12 Purpose: Utilize the result function created above to compute chemical properties for N₂.

# Cell 13: Compute Chemical Properties for O2

results = compute_thermo(
    atoms=molecule('O2'),
    calc=calc_mol,
    geometry='linear',
    symmetrynumber=2,
    spin=1,
    temperature=298.15,
    vib_name='o2_vib',
)

potentialenergy_O2 = results['potential_energy']
enthalpy_O2_eV = results['enthalpy_eV']
enthalpy_O2_kJ_mol = results['enthalpy_kJ_mol']

print(f"O2 Potential Energy: {potentialenergy_O2:.4f} eV = {potentialenergy_O2 * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of O2: {enthalpy_O2_eV:.4f} eV")
print(f"Enthalpy of O2 at 298 K: {enthalpy_O2_kJ_mol:.4f} kJ/mol")

Cell 13 Purpose: Utilize the result function created above to compute chemical properties for O₂.

# Cell 14: Compute Chemical Properties for S (rhombic)

H_sub_S_kJ = 277.0 / 8  # Experimental enthalpy of sublimation: S(rhombic) -> S(g), NIST

s_atom = Atoms('S', positions=[[10, 10, 10]], cell=[20, 20, 20], pbc=False)
s_atom.calc = calc_mol
E_S = s_atom.get_potential_energy()

E_S_kJ = E_S * (1 / (kJ / mol))
H_S_atom_kJ = E_S_kJ + (5/2) * R * T # Thermal Correction for S atom (Typically IdealGasThermo handles this)
H_rhombic_S_kJ = H_S_atom_kJ - H_sub_S_kJ

print(f"S atom potential energy: {E_S:.4f} eV = {E_S * (1 / (kJ / mol)):.4f} kJ")
print(f"Enthalpy of S atom at 298 K: {H_S_atom_kJ:.4f} kJ/mol")
print(f"Enthalpy of S (rhombic) at 298 K: {H_rhombic_S_kJ:.4f} kJ/mol")

Cell 14 Purpose: Similar to the carbon case, we need to add the energy of sublimation to S (rhombic) to get the energy of solid sulfur.

# Cell 15: Compute Standard Enthalpy of Formation in MACE-OFF

# Ammonia
dH_NH3 = enthalpy_NH3_kJ_mol - (0.5*enthalpy_N2_kJ_mol + 1.5 * enthalpy_H2_kJ_mol)

print(f"Enthalpy change for NH3 formation at 298 K: {(dH_NH3):.4f} kJ/mol")
print("")


# Propanol
dH_Pro = enthalpy_CH3CHOHCH3_kJ_mol - (3*H_graphite_kJ + 4*enthalpy_H2_kJ_mol + 0.5*enthalpy_O2_kJ_mol)

print(f"Enthalpy change for ISP formation at 298 K: {(dH_Pro):.4f} kJ/mol")
print("")


# Methanol
dH_CH3OH = enthalpy_CH3OH_kJ_mol - (H_graphite_kJ + 2*enthalpy_H2_kJ_mol + 0.5*enthalpy_O2_kJ_mol)

print(f"Enthalpy change for CH3OH formation at 298 K: {dH_CH3OH:.4f} kJ/mol")
print("")


# Propane
dH_C3H8 = enthalpy_C3H8_kJ_mol - (3*H_graphite_kJ + 4*enthalpy_H2_kJ_mol)

print(f"Enthalpy change for C3H8 formation at 298 K: {dH_C3H8:.4f} kJ/mol")
print("")


# Thiophene
dH_C4H4S = enthalpy_C4H4S_kJ_mol - (4*H_graphite_kJ + 2*enthalpy_H2_kJ_mol + H_rhombic_S_kJ)

print(f"Enthalpy change for C4H4S formation at 298 K: {dH_C4H4S:.4f} kJ/mol")

Enthalpy change for NH3 formation at 298 K: -133.4838 kJ/mol

Enthalpy change for ISP formation at 298 K: -245.3568 kJ/mol

Enthalpy change for CH3OH formation at 298 K: -209.1296 kJ/mol

Enthalpy change for C3H8 formation at 298 K: -72.2470 kJ/mol

Enthalpy change for C4H4S formation at 298 K: -107.3572 kJ/mol

Cell 15 Purpose: Compute the standard enthalpy of formation for the 5 molecules above using the change in enthalpy of the base elements of each molecule.

Obtain Values from Database Method¶

# Cell 16: Define Location of Database (Located in Github Repository)

DATABASE_PATH = "https://raw.githubusercontent.com/cacherowan/CACHE-Rowan/main/Reference_Files/Chemical_Property_Database/Processed_Solvent_DF_v6_TEST.xlsx"
df = pd.read_excel(DATABASE_PATH)

Cell 16 Purpose: Define path of the database that will be utilized later on.

# Cell 17: Define Function to Obtain Values from Database Method

def obtain_Enthalpy_Of_Formation(MOLECULE, SMILES):
    """
    Find Properties of input Molecule using the ANN model and database.

    Parameters:
        MOLECULE (str): Name of the molecule (for display purposes).
        SMILES (str): SMILES string of the molecule to look up in the database.

    Returns:
        float: Predicted standard formation enthalpy in kJ/mol.
    """

    # 2. Read the DB:
    db = pd.read_excel(DATABASE_PATH)
    db = db.set_index('SMILES')
    descriptors = db.loc[SMILES]

    Enthalpy_Of_Formation = ['Standard Formation Enthalpy (Gas) [J/mol]']

    Property = descriptors.loc[Enthalpy_Of_Formation]

    return descriptors['Standard Formation Enthalpy (Gas) [J/mol]']

Cell 17 Purpose: Create a function that will search the database looking for the standard enthalpy of formation of the specific molecule input.

# Cell 18: Use Function to Obtain Values of Standard Enthalpy of Formation in (J/mol)

Ammonia_Enthalpy_Of_Formation = obtain_Enthalpy_Of_Formation("Ammonia", "N")
print("Ammonia Enthalpy of Formation", Ammonia_Enthalpy_Of_Formation, "J/mol")

Propanol_Enthalpy_Of_Formation = obtain_Enthalpy_Of_Formation("Propanol", "CCCO")
print("Propanol Enthalpy of Formation", Propanol_Enthalpy_Of_Formation, "J/mol")

Methanol_Enthalpy_Of_Formation = obtain_Enthalpy_Of_Formation("Methanol", "CO")
print("Methanol Enthalpy of Formation", Methanol_Enthalpy_Of_Formation, "J/mol")

Propane_Enthalpy_Of_Formation = obtain_Enthalpy_Of_Formation("Propane", "CCC")
print("Propane Enthalpy of Formation", Propane_Enthalpy_Of_Formation, "J/mol")

Thiophene_Enthalpy_Of_Formation = obtain_Enthalpy_Of_Formation("Thiophene", "s1cccc1")
print("Thiophene Enthalpy of Formation", Thiophene_Enthalpy_Of_Formation, "J/mol")

Ammonia Enthalpy of Formation -45940.0 J/mol
Propanol Enthalpy of Formation -256000.0 J/mol
Methanol Enthalpy of Formation -205000.0 J/mol
Propane Enthalpy of Formation -104700.0 J/mol
Thiophene Enthalpy of Formation 114900.0 J/mol

Cell 18 Purpose: Utilize the function in cell 17 to obtain the standard enthalpy of formation for the 5 molecules used above.

# Cell 19: Calculate Percent Error in MACE-OFF vs Database Based on NIST Data

# Convert from J/mol to kJ/mol
Ammonia_Enthalpy_Of_Formation = Ammonia_Enthalpy_Of_Formation / 1000
Propanol_Enthalpy_Of_Formation = Propanol_Enthalpy_Of_Formation / 1000
Methanol_Enthalpy_Of_Formation = Methanol_Enthalpy_Of_Formation / 1000
Propane_Enthalpy_Of_Formation = Propane_Enthalpy_Of_Formation / 1000
Thiophene_Enthalpy_Of_Formation = Thiophene_Enthalpy_Of_Formation / 1000

Percent_Error_NH3 = abs((dH_NH3 - Ammonia_Enthalpy_Of_Formation) / Ammonia_Enthalpy_Of_Formation) * 100

Percent_Error_CH3CHOHCH3 = abs((dH_Pro - Propanol_Enthalpy_Of_Formation) / Propanol_Enthalpy_Of_Formation) * 100

Percent_Error_CH3OH = abs((dH_CH3OH - Methanol_Enthalpy_Of_Formation) / Methanol_Enthalpy_Of_Formation) * 100

Percent_Error_C3H8 = abs((dH_C3H8 - Propane_Enthalpy_Of_Formation) / Propane_Enthalpy_Of_Formation) * 100

Percent_Error_C4H4S = abs((dH_C4H4S - Thiophene_Enthalpy_Of_Formation) / Thiophene_Enthalpy_Of_Formation) * 100

Cell 19 Purpose: Calculate the percent error of each molecule comparing the results from MACE-OFF to the database which is based on NIST data.

# Cell 20: Show Table of Percent Errors

import pandas as pd

data = {
    "Chemical Name": ["Ammonia", "Propanol", "Methanol", "Propane", "Thiophene"],
    "MACE-OFF Standard Formation Enthalpy [kJ/mol]": [dH_NH3, dH_Pro, dH_CH3OH, dH_C3H8, dH_C4H4S],
    "Database Standard Formation Enthalpy [kJ/mol]": [Ammonia_Enthalpy_Of_Formation, Propanol_Enthalpy_Of_Formation, Methanol_Enthalpy_Of_Formation, Propane_Enthalpy_Of_Formation, Thiophene_Enthalpy_Of_Formation],
    "MACE-OFF Percent Error": [Percent_Error_NH3, Percent_Error_CH3CHOHCH3, Percent_Error_CH3OH, Percent_Error_C3H8, Percent_Error_C4H4S],
}
pd.set_option("display.width", 1000)
df = pd.DataFrame(data)

df['MACE-OFF Percent Error'] = df['MACE-OFF Percent Error'].map(lambda x: f'{x:.3g}%')

display(df)

Chemical Name	MACE-OFF Standard Formation Enthalpy [kJ/mol]	Database Standard Formation Enthalpy [kJ/mol]	MACE-OFF Percent Error
0	Ammonia	-133.483751	-45.94	191%
1	Propanol	-245.356850	-256.00	4.16%
2	Methanol	-209.129604	-205.00	2.01%
3	Propane	-72.246995	-104.70	31%
4	Thiophene	-107.357161	114.90	193%

Cell 20 Purpose: Display percent error between simulated values and the database values.

Discussion / Analysis¶

In this code, you were able to use ASE and MACE-OFF to compute Chemical Properties, and a few edge cases were shown to help if you need to compute the Standard Enthalpy of Formation of a molecule with a product that has a irregular natural form. You also saw how to use the Database to compute any property of any chemical included in the excel sheet. After that, the error in using MACE-OFF was shown. The error in MACE-OFF varied from different molecules. That was due to trying to analyze elements that the model wasn’t trained on specifically. The Database is extensive and has many properties for known chemicals currently, but if you need a property for a chemical that isn’t included, you will need to use another method. MACE-OFF can be accurate on data it was trained on, but could significantly off when simulating atoms / molecules it has insufficient data on.

Molecular Dynamics Simulation Background

Background information of Molecular Dynamics Simulations

Machine Learning Background

Background information on Machine Learning and Artificial Neural Networks