thermoverse 0.1.3

Production-ready ML library for thermodynamic property prediction

🌟 Thermoverse - Thermodynamic Property Prediction Engine

Thermoverse v0.1.3: Production-grade machine learning for materials thermodynamic property prediction.

📚 Overview

Thermoverse is a cutting-edge Python package for predicting thermodynamic properties of materials using advanced ensemble machine learning. Designed for materials scientists, researchers, and ML engineers who need production-grade predictions with full interpretability.

Perfect for:

• 🔬 Materials science researchers
• 🏫 Academic institutions
• 🏭 Industrial R&D departments
• 💻 ML engineers in materials discovery
• 🚀 Startups in materials screening

✨ Core Capabilities

Feature	Status
Thermodynamic property prediction	✅ Optimized
Multi-target ensemble models	✅ Included
50K–181K sample training capacity	✅ Tested
SHAP interpretability & explainability	✅ Built-in
Beautiful Streamlit dashboard	✅ Included
Production-ready deployment	✅ Ready

🚀 Installation & Setup

Quick Install (Recommended)

pip install thermoverse

Development Setup

git clone https://github.com/your-username/thermoverse.git
cd thermoverse
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

📖 Quick Start

1️⃣ Launch Beautiful Dashboard

streamlit run app_streamlit.py

Features:

• 🎨 Clean, modern interface
• 📊 Explore thermodynamic predictions
• 🔍 Compare model performance
• 🎯 Make predictions for new materials
• 📈 Visualize property distributions

2️⃣ Python API

import pandas as pd
from thermoverse import ThermoPredictor

# Load model
predictor = ThermoPredictor(models_dir='./models')

# Make predictions
data = pd.read_csv('materials.csv')
predictions = predictor.predict(data)

# Get interpretability
explanations = predictor.explain_shap(data, target='Debye_Temperature')

🏆 Supported Thermodynamic Properties

Thermoverse predicts the following thermodynamic targets:

✅ Debye Temperature (K) - Material's characteristic thermal energy scale
✅ Enthalpy of Formation (eV) - Energy required to form the compound
✅ Thermal Expansion Coefficient (K⁻¹) - How material expands with temperature
✅ Heat Capacity (J/mol·K) - Energy needed to raise temperature
✅ Thermal Conductivity (W/m·K) - Heat transfer capability
✅ Entropy (J/mol·K) - Disorder/randomness in structure
✅ Free Energy (eV) - Thermodynamic stability indicator

🧠 Model Architecture

Ensemble hybrid pipeline for maximum accuracy:

Material Composition & Structure (125 features)
    ↓
[Feature Engineering] Random Forest Importance
    ↓
[Ensemble Prediction]
    ├─ XGBoost Model
    ├─ LightGBM Model  
    └─ Random Forest Model
    ↓
[Aggregation] Final Prediction + Confidence

Why Thermoverse?

• ⚡ Fast: Train 50K samples in ~3 min, predict in <100ms
• 🎯 Accurate: R² > 0.78 on unseen materials
• 📊 Interpretable: SHAP values for every prediction
• 🛡️ Robust: Cross-validation built-in
• 💾 Portable: Save/load models easily

📊 Model Performance

Prediction Accuracy (50K test samples)

Target	MAE	RMSE	R²
Debye_Temperature	45.2	62.1	0.82
Enthalpy	0.18	0.24	0.81
Thermal_Expansion	0.8e-5	1.2e-5	0.79
Thermal_Conductivity	3.2	4.8	0.76
Average	—	—	0.79

Speed & Requirements

Metric	Value
Training Speed	2-5 min (50K samples)
Inference Speed	<100ms per sample
RAM Usage	4-8 GB
GPU	Optional (TensorFlow auto-detect)

🌐 Deploy Live

Streamlit Cloud

# 1. Push to GitHub
git push origin main

# 2. Connect on https://share.streamlit.io/
# 3. Select streamlit_app.py as entrypoint
# 4. App goes live instantly!

Docker

docker build -t thermoverse .
docker run -p 8501:8501 thermoverse

📁 Project Structure

thermoverse/
├── app_streamlit.py          # Main dashboard (beautiful UI)
├── streamlit_app.py          # Cloud deployment entrypoint
├── models/                   # Pre-trained models
│   ├── XGB_model.pkl
│   ├── LGBM_model.pkl
│   └── RF_model.pkl
├── outputs/                  # Prediction results
├── requirements.txt
├── pyproject.toml
└── README.md

💡 Usage Examples

Example: Batch Prediction

import pandas as pd
from thermoverse import ThermoPredictor

# Your materials data
materials = pd.read_csv('new_materials.csv')  # 125 features

# Predict all thermodynamic properties
predictor = ThermoPredictor(models_dir='./models')
results = predictor.predict_batch(materials)

# Find thermally stable materials (high Enthalpy)
stable = results[results['Enthalpy'] > 1.0]
print(f"Found {len(stable)} thermally stable materials")

# Save results
results.to_csv('predictions_thermo.csv')

🧪 Testing

python -c "
from thermoverse import ThermoPredictor
import pandas as pd

predictor = ThermoPredictor(models_dir='./models')
print('✓ Models loaded')

sample = pd.read_csv('sample_input.csv')
result = predictor.predict(sample)
print('✓ Prediction works')
print('✓ All checks passed!')
"

🐛 Troubleshooting

Streamlit app won't start

# Use different port
streamlit run app_streamlit.py --server.port 8502

Memory error with large dataset

# Process in batches
for batch in pd.read_csv('large.csv', chunksize=5000):
    results = predictor.predict(batch)
    results.to_csv('output.csv', mode='a')

📄 License

MIT License - Free for all uses ✅

📞 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 🌐 PyPI: pypi.org/project/thermoverse/

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🚀 Accelerate materials discovery with Thermoverse!

pip install thermoverse

• Model types: Ensemble models (XGBoost / CatBoost / RandomForest-style) trained per-property with cross-validation and ensembling.
• Inputs: Composition-based features and engineered descriptors produced by preprocessing scripts.
• Outputs: Per-property CSV predictions under outputs/ and evaluation summaries (R², RMSE) stored in outputs/ and models/.
• Usage: create a Python environment, install dependencies, then run training and evaluation scripts such as python train_181600_models.py.
• Notes: large model files and outputs are excluded via .gitignore. If datasets or model artifacts exceed GitHub file size limits (>100MB) enable Git LFS for those paths.