AdsorpQC

Automated Quality-Control, GCMC Burn-in Detection, Isotherm Model Fitting, and Reproducibility Toolkit for Adsorption Simulations in Nanoporous Materials.

Overview

AdsorpQC is an open-source scientific software toolkit tailored for the rigorous validation and certification of molecular adsorption simulations (Grand Canonical Monte Carlo, GCMC, RASPA, LAMMPS) and experimental gas isotherms in MOFs, COFs, Zeolites, and Porous Carbons.

In GCMC simulations, reporting average loadings before reaching true thermodynamic equilibrium (under-equilibrated burn-in) or during unresolved systematic drift leads to invalid capacities. adsorpqc addresses this with an automated quality-control pipeline in a single command:

• ⏳ Automated GCMC Burn-in Detection:
  • Maximizes the number of statistically independent production samples ($N_{\text{eff}}$).
  • Automatically identifies and truncates un-equilibrated initialization cycles.
• 📉 Loading Drift & Stationarity Audit:
  • Compares the first and last 20% of the production phase. The drift is flagged only when it is statistically significant against the autocorrelation-corrected noise ($z > 2$ / $z > 3$) and large relative to the mean.
  • Generates clear safety alerts: "DO NOT REPORT PRODUCTION AVERAGE" if the drift is significant ($z > 3$) and larger than 8% of the mean.
• 🧪 Non-Linear Isotherm Fitting & Model Selection:
  • Fits Langmuir, Dual-Site Langmuir (DSLG), Sips (Langmuir-Freundlich) and Toth models.
  • Selects the model with the lowest small-sample corrected Akaike Information Criterion (AICc), and reports $R^2$ and RMSE.
• ⚡ Thermodynamic Energetics & Selectivity:
  • Henry coefficient ($K_H$), obtained by extrapolating $q/P$ to zero loading. On RASPA3 methane/MFI data it agrees with Widom insertion within 0.1%.
  • Isosteric heat of adsorption ($q_{\text{st}}$), from the $\mu VT$ particle–energy fluctuation formula. It needs the number of molecules and the energy with explicit units; RASPA3's own value is read when available.
  • Ideal Adsorbed Solution Theory (IAST) binary mixture selectivity ($S_{A/B}$) with 95% Bootstrap Confidence Intervals. This part has not yet been validated against an independent IAST implementation.
• 📑 Publication Deliverables:
  • Interactive self-contained report.html dashboard.
  • Vector publication plots (Isotherm fit curves, GCMC burn-in shaded trajectory) in SVG, PDF, PNG (300 DPI).
  • Ready-to-compile LaTeX summary tables (.tex).
  • Draft Methods text paragraph and BibTeX citation (citation.bib).

    GCMC Simulation (RASPA) / Isotherm CSV
                      │
                      ▼
  ┌───────────────────────────────────────────────────────────┐
  │                         AdsorpQC                          │
  │  ├── Automated GCMC Burn-in / Equilibration Cutoff        │
  │  ├── Production Loading Drift & Stationarity Audit        │
  │  ├── Non-Linear Isotherm Fitting (Langmuir, Sips, Toth)   │
  │  ├── Henry Constant (K_H) & Isosteric Heat (q_st)        │
  │  └── IAST Selectivity (S_A/B) with Bootstrap 95% CI       │
  └───────────────────────────────────────────────────────────┘
                      │
                      ▼
  ┌───────────────────────────────────────────────────────────┐
  │                   Publication Deliverables                │
  │  ├── report.html (Interactive Dashboard & Badges)         │
  │  ├── adsorpqc_isotherm_fit.pdf/svg/png                    │
  │  ├── adsorpqc_gcmc_burnin_trajectory.pdf/svg/png         │
  │  ├── adsorpqc_summary_table.tex / .csv                    │
  │  ├── methods_snippet.txt (Ready for Manuscript)           │
  │  └── citation.bib (BibTeX Reference)                      │
  └───────────────────────────────────────────────────────────┘

Installation

From PyPI

pip install adsorpqc

From Source

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

Quickstart (CLI)

1. Run Benchmark Demo (Instant GCMC + Isotherm Simulation)

adsorpqc demo -o my_adsorption_audit/

Open my_adsorption_audit/report.html in your browser to explore the interactive dashboard!

2. Assess Isotherm CSV File

adsorpqc assess -i my_isotherm.csv --framework "Mg-MOF-74" --adsorbate "CO2" --temp 298.15 -o output_report/

3. Assess RASPA Simulation Output

# one RASPA3 run (output/output_<T>_<P>.s0.txt) or RASPA2 run (output_*.data)
adsorpqc assess -i output/output_300_1e+05.s0.txt -o raspa_audit/
# a folder of RASPA runs, one pressure each; Widom runs are compared with the isotherm K_H
adsorpqc assess -i runs/ -o raspa_isotherm/

Python API Usage

import numpy as np
from adsorpqc import assess_adsorption_quality
from adsorpqc.parsers import parse_isotherm_csv
from adsorpqc.reporters import generate_adsorption_figures, generate_adsorption_manuscript_assets, generate_adsorption_html_report

# 1. Parse isotherm data
data = parse_isotherm_csv("co2_isotherm.csv")

# 2. Assess quality and fit models
report = assess_adsorption_quality(
    metadata={"framework": "HKUST-1", "adsorbate": "CO2", "temperature_k": 298.15},
    pressure_isotherm=data["pressure"],
    loading_isotherm=data["loading"],
    temperature_k=298.15
)

print(f"Overall Certification: {report.overall_status}")
print(f"Optimal Model: {report.isotherm_fits['best_model_name']} (R^2 = {report.isotherm_fits['best_r_squared']:.4f})")

# 3. Export all publication assets
generate_adsorption_figures(report, "output_dir/", pressure_raw=data["pressure"], loading_raw=data["loading"])
generate_adsorption_manuscript_assets(report, "output_dir/")
generate_adsorption_html_report(report, "output_dir/report.html")

Citation

If you use AdsorpQC in your publications, please cite:

@software{monreal2026adsorpqc,
  author = {Monreal-Hern{\'a}ndez, Andre},
  title = {{AdsorpQC: An Open-Source Toolkit for Quality-Control, GCMC Burn-in Detection, Isotherm Fitting, and Reproducibility Assessment of Adsorption Simulations}},
  year = {2026},
  version = {1.0.0},
  publisher = {Zenodo},
  url = {https://github.com/sircalch/adsorpqc}
}

License

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