🔬 GasLambda — Gas Thermal Conductivity Predictor

QSPR/ML model for predicting gas-phase thermal conductivity (λ) of organic compounds from molecular structure.

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Overview

GasLambda predicts the thermal conductivity of organic gases at low pressure (≈1 atm) as a function of temperature, using only the molecular structure (SMILES) as input. It combines:

• RDKit molecular descriptors — physics-informed features (degrees of freedom, polarity, molecular shape, Eucken factor proxy)
• XGBoost gradient boosting — robust ML for small datasets with built-in regularization
• Applicability domain check — 3-method detection (z-score, Williams plot, Mahalanobis distance)

Performance (GroupKFold CV by compound)

Metric	Value
R²	0.857
MAPE	7.3%
MdAPE	4.8%
MAE	0.0016 W/(m·K)
Training compounds	90 unique, 135 data points
Temperature range	250–600 K

GroupKFold groups by SMILES to prevent data leakage — all temperatures of the same compound stay in the same fold. This gives honest generalization estimates for unseen compounds.

Key features

• Single prediction, temperature sweep, and batch modes
• Interactive Streamlit web interface with Plotly visualizations
• Parity plot, error distribution, and feature importance analysis
• CSV export for all results
• Applicability domain detection (z-score + leverage + Mahalanobis)
• Duan smearing correction for log-transform bias

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Live Demo

👉 gaslambda.streamlit.app

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Quick Start

Installation

git clone https://github.com/philippe-robin/Gaslambda.git
cd Gaslambda

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

Train the model

python data/build_dataset.py   # Build reference dataset
python src/train.py             # Train XGBoost model

Run the web app

streamlit run app.py

Python API

from src.predict import ThermalConductivityPredictor

predictor = ThermalConductivityPredictor()

# Single prediction
result = predictor.predict("CCO", temperature_K=400)
print(f"λ(ethanol, 400K) = {result['thermal_conductivity_W_mK']:.4f} W/(m·K)")

# Temperature sweep
df = predictor.predict_temperature_sweep("c1ccccc1", T_min=300, T_max=600)

# Batch
results = predictor.predict_batch(
    ["C", "CC", "CCC", "CCCC"],
    temperatures=[300, 300, 300, 300]
)

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Project Structure

gaslambda/
├── app.py                          # Streamlit web application
├── requirements.txt
├── setup.py
├── LICENSE
├── data/
│   ├── build_dataset.py            # Reference data from DIPPR/Yaws/NIST
│   └── reference_thermal_conductivity.csv
├── src/
│   ├── __init__.py
│   ├── descriptors.py              # RDKit molecular descriptor computation
│   ├── train.py                    # Model training pipeline (GroupKFold)
│   └── predict.py                  # Prediction + applicability domain (3 methods)
└── models/
    ├── xgb_thermal_conductivity.joblib
    ├── scaler.joblib
    ├── metrics.json                # GroupKFold metrics (primary)
    ├── metrics_comparison.json     # GroupKFold vs KFold comparison
    ├── training_stats.json         # Covariance, leverage, thresholds
    ├── feature_importance.json
    ├── feature_names.json
    └── cv_predictions.csv

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Scientific Background

Why QSPR for gas thermal conductivity?

Classical methods (Eucken, Chung, Stiel-Thodos) rely on critical properties and acentric factors that may not be available for novel compounds. QSPR predicts directly from molecular structure.

Descriptor rationale

The 41 descriptors are chosen based on the physics of gas thermal conductivity:

Category	Descriptors	Physical basis
Temperature	T, T^0.7, T/MW	λ ∝ T^n for polyatomic gases
Molecular size	MW, heavy atoms, 1/√MW	Kinetic theory: λ ∝ 1/√M
Degrees of freedom	DOF_vib, Cv_R, Eucken factor	Eucken correction: λ = η·(Cv + 9R/4M)
Polarity	TPSA, LogP, MolMR, H-bond donors/acceptors	Intermolecular forces affect mean free path
Topology	Kappa, Chi, Wiener index, BertzCT	Molecular shape → collision cross-section
Atom types	C, H, O, N, S, F, Cl, Br counts	Element-specific contributions
Functionality	Rings, aromaticity, sp3 fraction, bond types	Rigidity affects vibrational modes

Data sources

• DIPPR 801 — AIChE recommended values (primary source)
• Yaws' Handbook — C.L. Yaws, Transport Properties of Chemicals and Hydrocarbons
• NIST WebBook — webbook.nist.gov

Limitations

• Low-pressure only (~1 atm). High-pressure correction not included.
• Organic compounds only. Not validated for inorganics, metals, or ionic species.
• Small training set (135 points / 90 compounds). Expand with DIPPR 801 or TDE licensed data for production use.
• Temperature range: best within 250–600 K.
• Applicability domain: always check the domain flag before using predictions.

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Extending the Model

Adding data

Add entries to REFERENCE_DATA in data/build_dataset.py, then retrain:

("MyCompound", "C(=O)OCC", 300, 0.0142, "LITERATURE"),

Adding descriptors

Extend compute_descriptors() in src/descriptors.py and add the new name to FEATURE_NAMES.

Alternative models

Replace XGBoost in src/train.py with any scikit-learn compatible regressor (Random Forest, SVR, neural network).

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References

1. Poling, B.E., Prausnitz, J.M. & O'Connell, J.P. The Properties of Gases and Liquids, 5th ed., McGraw-Hill, 2001. Chapters 10–11.
2. Gharagheizi, F. et al. Ind. Eng. Chem. Res., 2013, 52, 7165–7174. DOI: 10.1021/ie4005008
3. Chen, T. & Guestrin, C. "XGBoost: A Scalable Tree Boosting System", KDD, 2016.
4. DIPPR 801 Database, AIChE/BYU.
5. Yaws, C.L. Transport Properties of Chemicals and Hydrocarbons, 2nd ed., Elsevier, 2014.

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License

MIT License — see LICENSE.

Author

Alysophil SAS — AI-driven flow chemistry www.alysophil.com