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title: Psc Predict MCP Server
emoji: ☀️
colorFrom: yellow
colorTo: yellow
sdk: docker
app_port: 7860
pinned: false
license: mit
---
# Psc_Predict MCP Server
Perovskite Solar Cell Performance Prediction MCP Service
## Features
Predict 8 performance metrics from CIF crystal structure files:
| Metric | Description | Unit |
|--------|-------------|------|
| PCE | Power Conversion Efficiency | % |
| DFT Band Gap | DFT calculated band gap | eV |
| Energy Above Hull | Thermodynamic stability | eV/atom |
| Stability Retention | Stability retention percentage | % |
| Stability T80 | T80 lifetime | hours |
| Voc | Open-circuit voltage | V |
| Jsc | Short-circuit current density | mA/cm² |
| FF | Fill factor | - |
## Available Model Types
| Model | Description | Recommendation |
|-------|-------------|----------------|
| **XGBoost** | Gradient boosting ensemble | ⭐ Default & Recommended |
| **Random Forest** | Ensemble of decision trees | Good interpretability |
| **Neural Network** | 3-layer MLP (128-64-32) | Deep learning approach |
## MCP Connection
### SSE Connection Configuration
```json
{
"mcpServers": {
"psc-predict": {
"url": "https://your-space.hf.space/sse"
}
}
}
```
### Available Tools (Simplified)
| Tool | Description | Parameters |
|------|-------------|------------|
| `predict_ensemble` | **Predict using ALL 3 models with ensemble statistics** | cif, targets |
| `parse_cif_features` | Parse CIF and extract features | cif |
| `get_model_info` | Get model information | - |
| `list_available_models` | List all available models | - |
### Ensemble Prediction (Autonomous Multi-Model)
The `predict_ensemble` tool automatically calls all three models and provides:
- Individual predictions from XGBoost, Random Forest, and Neural Network
- Ensemble statistics: mean, std, min, max, range
- Recommendation based on model performance benchmarks
Example response:
```json
{
"results": {
"pce": {
"predictions": {"xgboost": 18.5, "random_forest": 17.8, "neural_network": 19.1},
"ensemble": {"mean": 18.47, "std": 0.53, "range": 1.3},
"recommendation": {"model": "xgboost", "value": 18.5}
}
}
}
```
## Input Example
```cif
data_CsPbI3
_cell_length_a 6.2894
_cell_length_b 6.2894
_cell_length_c 6.2894
_cell_angle_alpha 90.0
_cell_angle_beta 90.0
_cell_angle_gamma 90.0
_cell_volume 248.89
_chemical_formula_sum 'Cs1 Pb1 I3'
```
## Model Information
- **Model Types**: XGBoost (recommended), Random Forest, Neural Network
- **Input Features**: 93-dimensional (7 lattice parameters + 86 element fractions)
- **Training Data**: Perovskite solar cell database
## Local Development
```bash
# Install dependencies
pip install -r requirements.txt
# Run server
python server.py
# Or with Docker
docker build -t psc-predict .
docker run -p 7860:7860 psc-predict
```
## License
MIT
|