README.md

---
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