QMCert

Automated Quality-Control, Stationary Point Certification, and Reproducibility Assessment for Quantum-Chemical Calculations.

Overview

QMCert is an open-source scientific toolkit designed to systematically audit, validate, and certify electronic structure calculations (DFT, HF, post-HF, semiempirical) from ORCA, Gaussian, Q-Chem, and NWChem.

Instead of manually inspecting log files to ensure calculations are publication-ready, qmcert performs a comprehensive automated audit with a single command:

• 🎯 Stationary Point Certification:
  • Automatically identifies imaginary frequencies ($\nu < 0\text{ cm}^{-1}$).
  • Validates Local Minima (0 imaginary modes) vs Transition States (exactly 1 imaginary mode).
  • Flags unphysical saddle points with clear diagnostic messages.
• 🔄 Wavefunction & Spin Contamination ($\langle S^2 \rangle$):
  • Audits expectation values $\langle S^2 \rangle_{\text{calc}}$ vs exact theoretical $S(S+1)$.
  • Calculates spin contamination percentage and triggers alerts if $> 5.0%$.
• ⚡ SCF & Geometry Convergence Verification:
  • Checks 4 standard convergence thresholds (Energy change, Max/RMS gradients, Max/RMS displacements).
• 🌡️ Advanced Thermochemistry & Quasi-Harmonic Corrections:
  • Extracts ZPVE, thermal enthalpy $H(T)$, Gibbs free energy $G(T)$, and entropy $S(T)$.
  • Applies Grimme's quasi-RRHO harmonic entropy correction to prevent rotational/vibrational divergence for low-frequency modes ($\nu < 100\text{ cm}^{-1}$).
• 🌈 Simulated IR Vibrational Spectra:
  • Lorentzian line-broadening with peak identification.
• 🚦 Quantum Chemistry Validation Score (PASS / WARNING / FAIL).
• 📑 Publication Deliverables: Interactive self-contained report.html, publication vector plots (SVG/PDF/PNG 300 DPI), LaTeX summary tables (.tex), and a draft Computational Details Methods paragraph with automated BibTeX citations.

  Quantum Chemical Output (.out, .log)
                    │
                    ▼
  ┌───────────────────────────────────────────────────────────┐
  │                          QMCert                           │
  │  ├── Stationary Point Certification (0 or 1 Imag Freq)    │
  │  ├── Spin Contamination Audit (<S^2> vs S(S+1))           │
  │  ├── SCF & Geometry Optimization Convergence              │
  │  ├── Grimme Quasi-RRHO Thermochemistry Corrections        │
  │  └── Frontier Orbital Gap & Simulated IR Spectrum         │
  └───────────────────────────────────────────────────────────┘
                    │
                    ▼
  ┌───────────────────────────────────────────────────────────┐
  │                   Publication Deliverables                │
  │  ├── report.html (Interactive Dashboard & Badges)         │
  │  ├── qmcert_simulated_ir_spectrum.pdf/svg/png             │
  │  ├── qmcert_summary_table.tex / .csv                      │
  │  ├── methods_snippet.txt (Ready for Manuscript)           │
  │  └── citation.bib (BibTeX Reference)                      │
  └───────────────────────────────────────────────────────────┘

Installation

From PyPI

pip install qmcert

From Source

git clone https://github.com/sircalch/qmcert.git
cd qmcert
pip install -e .[dev]

Quickstart (CLI)

1. Run Demonstration Mode (Instant Benchmark DFT Calculation)

qmcert demo -o my_qm_validation/

Open my_qm_validation/report.html in any browser to inspect the report and simulated IR spectrum!

2. Assess ORCA / Gaussian Output File

qmcert assess -i calculation.out -o qm_quality_report/

3. Certify a Transition State (TS) Calculation

qmcert assess -i ts_optimization.out --ts -o ts_report/

Python API Usage

from qmcert import assess_qm_quality
from qmcert.parsers import parse_qm_output
from qmcert.reporters import generate_qm_figures, generate_qm_manuscript_assets, generate_qm_html_report

# 1. Parse quantum chemistry output (ORCA / Gaussian)
parsed_data = parse_qm_output("my_dft_calc.out")

# 2. Assess calculation quality
report = assess_qm_quality(
    metadata=parsed_data["metadata"],
    scf_converged=parsed_data["scf_converged"],
    frequencies=parsed_data["frequencies"],
    intensities=parsed_data["intensities"],
    expected_point_type="MINIMUM",
    s2_calculated=parsed_data["s2_calculated"],
    thermochemistry=parsed_data["thermochemistry"]
)

print(f"Overall Certification: {report.overall_status}")
print(f"Stationary Point: {report.frequency_result.point_type}")

# 3. Export all publication assets
generate_qm_figures(report, "output_dir/")
generate_qm_manuscript_assets(report, "output_dir/")
generate_qm_html_report(report, "output_dir/report.html")

Citation

If you use QMCert to validate quantum-chemical calculations, certify stationary points, or calculate quasi-RRHO corrections, please cite:

@software{monreal2026qmcert,
  author = {Monreal-Hern{\'a}ndez, Andre},
  title = {{QMCert: Automated Quality-Control, Stationary Point Certification, and Reproducibility Assessment for Quantum-Chemical Calculations}},
  year = {2026},
  version = {1.0.0},
  publisher = {Zenodo},
  url = {https://github.com/sircalch/qmcert}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.