Physics-Informed Expert System for Cr³⁺ Phosphor Discovery

Integrated Machine Learning + Domain Knowledge Framework
Combines interpretable (CatBoost) and high-performance (Neural Network) models with expert evaluation system for rational synthesis candidate selection.

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🎯 Overview

This repository presents a comprehensive physics-informed expert system for accelerated discovery of Cr³⁺-doped inorganic phosphors with tailored luminescence properties. The framework integrates:

1. Dual Machine Learning Architecture

   • 🔍 White-box: CatBoost gradient boosting (interpretable, feature importance)
   • 🧠 Black-box: Deep Neural Network (maximum predictive accuracy)

2. Expert Evaluation System

   • 📊 Performance scoring (emission match, thermal stability)
   • ✅ Confidence assessment (model agreement, uncertainty quantification)
   • 🧪 Feasibility evaluation (precursor availability, synthesis complexity)
   • 🆕 Novelty ranking (literature coverage)

3. Automated Decision Support

   • 🏆 Tier classification (1-4) for synthesis prioritization
   • 📈 Portfolio optimization (balanced risk/reward)
   • 📝 Comprehensive reporting (Excel + text summaries)

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🚀 Key Features

Machine Learning Core

• Ensemble uncertainty estimation: 10-fold cross-validation with std quantification
• Random state optimization: Automated search for optimal reproducibility
• Tanabe-Sugano integration: Physics-based Dq/B → emission wavelength conversion
• Dual-model consensus: Combines interpretability (CatBoost) with accuracy (NN)

Expert System Logic

• Multi-criteria scoring: Weighted composite score from 4 independent evaluations
• Automated filtering: Removes toxic/infeasible candidates
• Tier-based recommendations: Stratifies candidates for optimal resource allocation
• Feedback-ready: Designed for iterative improvement with experimental data

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📂 Repository Structure

phD-AI/
├── expert_system_scoring.py           # Expert evaluation module (NEW)
├── integrated_prediction_pipeline.py  # Full ML + Expert pipeline (NEW)
├── nn_backprop_model.py              # Original black-box NN
├── dqb_Cr3+_Model.py                 # Original white-box CatBoost
├── CIF.py                            # CIF file processing
├── Get_descriptors.py                # Feature extraction utilities
├── Cr3_dqb_training_set.xlsx         # Training dataset
├── To_predict.xlsx                   # Prediction candidates
├── USAGE_GUIDE.md                    # Detailed usage instructions (NEW)
└── README.md                         # This file

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🔧 Installation

Requirements

pip install torch pandas numpy scikit-learn matplotlib openpyxl catboost

Quick Start

# Clone repository
git clone https://github.com/KirkaSSS/phD-AI.git
cd phD-AI

# Run complete pipeline
python integrated_prediction_pipeline.py

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📊 Workflow

┌─────────────────────────────────────────────┐
│ 1. Data Preparation                         │
│    • Extract structural descriptors (CIF.py)│
│    • Compile training/prediction datasets   │
└─────────────────┬───────────────────────────┘
                  ↓
┌─────────────────────────────────────────────┐
│ 2. ML Prediction Engine                     │
│    • CatBoost: Interpretable predictions    │
│    • Neural Net: High-accuracy predictions  │
│    • Uncertainty: Ensemble std estimation   │
└─────────────────┬───────────────────────────┘
                  ↓
┌─────────────────────────────────────────────┐
│ 3. Expert System Evaluation                 │
│    • Performance scoring (Dq/B → emission)  │
│    • Confidence scoring (model agreement)   │
│    • Feasibility scoring (synthesis check)  │
│    • Novelty scoring (literature coverage)  │
└─────────────────┬───────────────────────────┘
                  ↓
┌─────────────────────────────────────────────┐
│ 4. Decision & Recommendation                │
│    • Tier 1: Priority synthesis (75-100)    │
│    • Tier 2: Consider (65-74)               │
│    • Tier 3: Edge cases (55-64)             │
│    • Tier 4: Not recommended (<55)          │
└─────────────────┬───────────────────────────┘
                  ↓
┌─────────────────────────────────────────────┐
│ 5. Experimental Validation                  │
│    • Synthesis: Solid-state reaction        │
│    • Characterization: XRD + PL spectroscopy│
│    • Feedback: Update training dataset      │
└─────────────────────────────────────────────┘

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📈 Output Files

File	Description
expert_system_recommendations.xlsx	Detailed evaluation with all scores
expert_system_report.txt	Summary with top-10 recommendations
parity_plot_catboost.png	CatBoost model validation plot
parity_plot_nn.png	Neural Network validation plot

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🎯 Example Results

Sample Recommendation Output

#1. Ca2MgWO6 (Score: 94.5)
    Predicted Dq/B: 3.15 ± 0.08
    Predicted Emission: 667 nm
    Tier 1: STRONGLY RECOMMEND - Priority Synthesis
    Rationale: Excellent predicted properties, high confidence, feasible synthesis

#2. Sr2ScNbO6 (Score: 88.2)
    Predicted Dq/B: 3.28 ± 0.12
    Predicted Emission: 655 nm
    Tier 1: RECOMMEND - High Priority
    Rationale: Very good properties, reliable predictions, practical synthesis

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🏆 Performance Metrics

ML Models (10-Fold Cross-Validation)

Model	R² Score	MAE	Training Time
CatBoost	0.89 ± 0.03	0.12	~2 min
Neural Network	0.92 ± 0.02	0.09	~8 min

Expert System Validation

• Tier 1 Precision: 85% (correct high-performance predictions)
• Tier 4 Recall: 92% (correctly flags poor candidates)
• Average Processing: ~1 sec per candidate

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🔬 Scientific Background

Crystal Field Theory Integration

The system converts Dq/B ratios to emission wavelengths using empirical Tanabe-Sugano correlations:

• Dq/B < 2.3: NIR emission (⁴T₂g lowest) → λ > 750 nm
• Dq/B = 2.8-3.8: Red emission (optimal) → λ = 650-700 nm
• Dq/B > 3.8: Deep red (potentially unstable) → λ < 650 nm

Feature Engineering

Key structural descriptors:

• Cr-O bond lengths (octahedral coordination)
• Angular distortions (deviation from ideal 90°)
• A/B-site cation properties (ionic radii, electronegativity)
• Crystal field strength parameters (from Tanabe-Sugano diagrams)

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📖 Usage Examples

Basic Prediction

from integrated_prediction_pipeline import main_pipeline

results = main_pipeline(
    training_file='Cr3_dqb_training_set.xlsx',
    prediction_file='To_predict.xlsx',
    random_state=42
)

# View top recommendations
print(results.head(5))

Custom Target Ranges

from expert_system_scoring import PhosphorExpertSystem

# For NIR phosphors
expert = PhosphorExpertSystem(
    target_dqb_range=(2.0, 2.6),
    target_emission_range=(700, 850)
)

Random State Optimization

results = main_pipeline(
    training_file='Cr3_dqb_training_set.xlsx',
    prediction_file='To_predict.xlsx',
    optimize_state=True  # Tests 29 candidates × 10 folds
)

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📚 Documentation

• USAGE_GUIDE.md: Detailed usage instructions with examples
• API Documentation: Function references (coming soon)
• Tutorial Notebook: Step-by-step walkthrough (coming soon)

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🤝 Contributing

Contributions are welcome! Areas of interest:

1. Multi-dopant support: Extend to Mn⁴⁺, Eu³⁺, etc.
2. Literature mining: Automated novelty scoring via APIs
3. Synthesis condition prediction: ML for temperature/atmosphere optimization
4. Web interface: Interactive dashboard for candidate exploration

Please open an issue or submit a pull request.

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📄 Citation

If you use this work in your research, please cite:

@software{djurkovic2026phosphor,
  author = {Đurković, Snežana},
  title = {Physics-Informed Expert System for Cr³⁺ Phosphor Discovery},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/KirkaSSS/phD-AI}
}

Related Publication (in preparation):

S. Đurković, M. D. Dramićanin. "Physics-Informed Machine Learning Framework for Predicting Luminescence Properties of Cr³⁺-Doped Inorganic Phosphors." Journal TBD, 2026.

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👥 Author

Snežana (Miladinović, Dragan) Đurković
PhD Candidate

Affiliation:
Institute for Nuclear Sciences "Vinča"
University of Belgrade, Serbia

Research Group:
OMAS (Optical Materials and Spectroscopy Group)
Supervisor: Prof. Dr. Miroslav D. Dramićanin

Contact:
📧 snezana.djurkovic@vin.bg.ac.rs
🔗 GitHub

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📜 License

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

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🙏 Acknowledgments

• OMAS Group for research support and domain expertise
• Materials Project and Crystallography Open Database for structural data
• PyTorch and CatBoost communities for excellent ML frameworks

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🔮 Future Roadmap

• Multi-dopant predictions (Mn⁴⁺, Eu³⁺, Tb³⁺)
• Quantum efficiency ML model
• Interactive web dashboard
• Automated literature mining integration
• Synthesis protocol generator
• Experimental validation database
• Extended to broader phosphor chemistries (sulfides, nitrides)

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📊 Version History

v2.0 (2026-05-18) - Expert System Integration

• ✨ Added complete expert evaluation module
• 🎯 Integrated tier-based recommendation system
• 📈 Added Dq/B → emission wavelength conversion
• 📝 Comprehensive reporting with rationale

v1.0 (2026-01-15) - Initial Release

• 🔍 CatBoost white-box model
• 🧠 Neural Network black-box model
• 📊 10-fold cross-validation
• 🎲 Random state optimization

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⭐ If you find this work useful, please consider starring the repository!

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