Access parsed properties

print(f"Number of atoms: {data.natom}") print(f"Atomic numbers: {data.atomnos}") print(f"Molecular orbitals: {data.moenergies}") print(f"Vibrational frequencies: {data.vibfreqs}")

Specific parser selection

from cclib.parser import Gaussian, ORCA, QChem parser = Gaussian("gaussian.log") data = parser.parse()


# Key Features

## Supported Packages
- Gaussian, ORCA, Q-Chem, ADF, GAMESS, NWChem, PSI4, Molpro, Jaguar

## Extracted Properties
- `atomnos`, `atomcoords`: Atomic numbers and coordinates
- `moenergies`, `mooccs`: Molecular orbital energies and occupations
- `vibfreqs`, `vibirs`: Vibrational frequencies and intensities
- `scfenergies`, `scfvalues`: SCF energies and convergence
- `atomcharges`: Atomic charge analyses (Mulliken, ESP, etc.)
- `dipole`, `moments`: Dipole and multipole moments
- `homos`: HOMO indices
- `etenergies`, `etsecs`: Electronic transition energies

## Algorithms Module
```python
from cclib.method import population, MPA
pop = population.MPA(data)
pop.calculate()
print(pop.aonames)  # Atomic orbital names

Common Use Cases

Automated Analysis Pipeline

import cclib
from pathlib import Path

for log_file in Path("calculations/").glob("*.log"):
    data = cclib.parser.parse(str(log_file))
    energies = data.scfenergies[-1]  # Final SCF energy

ML Dataset Creation

import numpy as np

structures = []
for log_file in log_files:
    data = cclib.parser.parse(log_file)
    structures.append({
        "coords": data.atomcoords[-1],
        "energy": data.scfenergies[-1],
        "charges": data.atomcharges.get("mulliken")
    })

Comparing Methods

gaussian_data = cclib.parser.parse("gaussian.log")
orca_data = cclib.parser.parse("orca.log")
print(f"Gaussian energy: {gaussian_data.scfenergies[-1]}")
print(f"ORCA energy: {orca_data.scfenergies[-1]}")

Bridge Interfaces

# Convert to ASE Atoms
from cclib.bridge import ase
atoms = ase.makease(data)

# Convert to pymatgen Structure
from cclib.bridge import pymatgen
struct = pymatgen.makestructure(data)

When to Use This Skill

Parsing quantum chemistry output files from Gaussian, ORCA, Q-Chem, ADF, GAMESS, NWChem, PSI4
Extracting molecular properties (energies, orbitals, frequencies) into Python data structures
Building automated analysis pipelines for computational chemistry results
Converting parsed data to ASE Atoms or pymatgen Structures for further analysis
Creating ML-ready datasets from quantum chemistry calculations

Best Practices

Always specify the parser explicitly if auto-detection fails
Use data.scfenergies[-1] for final SCF energy in converged calculations
Validate parsed data with data.metadata for calculation parameters
Leverage bridge interfaces for seamless integration with ASE/pymatgen
Check convergence flags before using extracted energies

cclib

Overview

Installation

Quick Start