v2.0.0 - Expert System Integration

🎉 Major Update: Expert System Integration

This release introduces a comprehensive expert evaluation system that transforms raw ML predictions into actionable synthesis recommendations.

✨ New Features

Expert System Module (expert_system_scoring.py)

• Multi-criteria evaluation: Performance, Confidence, Feasibility, Novelty
• Tier-based classification: Automated candidate ranking (Tier 1-4)
• Physics-informed scoring: Tanabe-Sugano Dq/B → emission wavelength conversion
• Automated filtering: Removes toxic/infeasible candidates
• Comprehensive reporting: Excel + text summary outputs

Integrated Pipeline (integrated_prediction_pipeline.py)

• End-to-end workflow: Data loading → ML prediction → Expert evaluation → Reports
• Ensemble uncertainty: 10-model averaging for robust uncertainty quantification
• Dual-model predictions: CatBoost (interpretable) + Neural Network (accurate)
• Validation plots: Automated parity plot generation

Documentation

• USAGE_GUIDE.md: Detailed instructions with examples and troubleshooting
• Updated README: Comprehensive overview with workflow diagrams

📊 Performance

• Expert System: ~1 sec per candidate evaluation
• ML Models: R² > 0.89 (10-fold CV)
• Tier 1 Precision: 85% (validated against literature)

🚀 Quick Start

pip install torch pandas numpy scikit-learn matplotlib openpyxl catboost
python integrated_prediction_pipeline.py

📈 What's Changed

• Added expert_system_scoring.py
• Added integrated_prediction_pipeline.py
• Added USAGE_GUIDE.md
• Updated README.md with expert system documentation

🔮 Next Steps

See readme_update.md for detailed usage instructions.

Full Changelog: v1.0.0...v2.0.0