# Generators

Generators are AI-powered tools that create new materials and structures using machine learning models. They enable the discovery of novel materials by generating candidates with desired properties. All generators implement the `BaseGenerator` interface with a standardized `generate()` method.

## Available Generators

### MatterGen - Microsoft's Crystal Structure Generator
**ID**: `mattergen`

Generative model for crystal structures developed by Microsoft Research.

**Capabilities**:
- Generate novel crystal structures
- Condition on chemical composition
- Ensure crystallographic validity
- High structural diversity

**Input Parameters**:
- `composition`: Target chemical composition
- `num_structures`: Number of structures to generate
- `temperature`: Generation temperature (creativity control)
- `constraints`: Structural constraints

**Output Format**:
- Crystal structures (CIF format)
- Generation confidence scores
- Structural validity metrics

**Use Cases**:
- Discovery of new inorganic materials
- Structure prediction for given compositions
- Crystal polymorph exploration

### GNoME - Graph Networks for Materials Exploration
**ID**: `gnome`

Deep learning framework for materials discovery using graph neural networks.

**Capabilities**:
- Graph-based material representation
- Property-conditioned generation
- Stability-aware structure generation
- Large-scale materials screening

**Input Parameters**:
- `target_properties`: Desired material properties
- `element_constraints`: Allowed chemical elements
- `num_candidates`: Number of materials to generate
- `stability_threshold`: Minimum stability requirement

**Output Format**:
- Material structures with property predictions
- Stability assessments
- Graph representations

**Use Cases**:
- Property-targeted material design
- Screening large chemical spaces
- Novel compound discovery

### iMatGen - Inverse Materials Generator
**ID**: `imatgen`

Inverse design tool for targeted materials generation with specific properties.

**Capabilities**:
- Inverse design from target properties
- Multi-objective optimization
- Synthesizability considerations
- Property gradient optimization

**Input Parameters**:
- `target_properties`: Property targets and ranges
- `optimization_weights`: Property importance weights
- `synthesis_constraints`: Manufacturing constraints
- `search_iterations`: Number of optimization steps

**Output Format**:
- Optimized material candidates
- Property predictions
- Synthesizability scores
- Optimization convergence data

**Use Cases**:
- Materials design for specific applications
- Multi-property optimization
- Synthesis-aware design

### MatGAN - Materials Generative Adversarial Network
**ID**: `matgan`

GAN-based generator for creating new materials with learned representations.

**Capabilities**:
- Adversarial training for realism
- Latent space interpolation
- Style transfer between material classes
- Conditional generation

**Input Parameters**:
- `material_class`: Type of material to generate
- `style_reference`: Reference material for style
- `diversity_factor`: Control structural diversity
- `num_samples`: Number of samples to generate

**Output Format**:
- Generated material structures
- Latent space embeddings
- Diversity metrics
- Realism scores

**Use Cases**:
- Exploring material design spaces
- Creating material variants
- Novel architecture discovery

### MolGAN - Molecular Generative Adversarial Network
**ID**: `molgan`

Specialized GAN for molecular and small crystal structure generation.

**Capabilities**:
- Molecular graph generation
- Small molecule and clusters
- Chemical validity enforcement
- Scaffold-based generation

**Input Parameters**:
- `molecule_type`: Target molecule class
- `size_range`: Molecular size constraints
- `scaffold`: Base molecular scaffold
- `chemical_constraints`: Allowed chemical features

**Output Format**:
- Molecular structures (MOL/SDF format)
- Chemical validity scores
- Property predictions
- Scaffold compliance metrics

**Use Cases**:
- Drug discovery applications
- Small molecule catalysts
- Molecular electronics materials

### CondDFCVAE - Conditional Deep Feature Consistent VAE
**ID**: `dfc-vae`

Variational autoencoder with conditional generation and feature consistency.

**Capabilities**:
- Conditional material generation
- Feature consistency enforcement
- Smooth latent space interpolation
- Property-guided sampling

**Input Parameters**:
- `condition_vector`: Property conditioning
- `latent_constraints`: Latent space constraints
- `consistency_weight`: Feature consistency importance
- `sampling_strategy`: Latent space sampling method

**Output Format**:
- Generated structures with conditions
- Latent space coordinates
- Feature consistency metrics
- Interpolation trajectories

**Use Cases**:
- Controlled property generation
- Material property optimization
- Design space exploration

### MyGen1 - Custom Generator 1
**ID**: `mygen1`

Customizable generator template for domain-specific applications.

**Capabilities**:
- Domain-specific material generation
- Customizable generation logic
- Integration with external tools
- Extensible architecture

**Input Parameters**:
- `domain_parameters`: Domain-specific inputs
- `generation_mode`: Generation strategy
- `custom_constraints`: User-defined constraints

**Output Format**:
- Domain-specific material structures
- Custom metrics
- Generation metadata

### MyGen2 - Custom Generator 2
**ID**: `mygen2`

Secondary custom generator for specialized applications.

**Capabilities**:
- Alternative generation approaches
- Specialized algorithms
- Custom property targeting
- Research-oriented features

**Input Parameters**:
- `algorithm_choice`: Generation algorithm
- `research_parameters`: Research-specific inputs
- `validation_criteria`: Output validation rules

**Output Format**:
- Specialized material outputs
- Algorithm-specific metrics
- Research data

## Usage Patterns

### Basic Material Generation

```python
# Example of using a generator in a Feature
gen_instance = generator_factory["mattergen"]("mattergen", logger)
inputs = {
    'composition': 'Si2O4',
    'num_structures': 10,
    'temperature': 0.8
}
results = gen_instance.generate(inputs)
```

### Property-Targeted Generation

```python
# Generate materials with specific properties
gen_instance = generator_factory["gnome"]("gnome", logger)
inputs = {
    'target_properties': {
        'band_gap': (1.0, 3.0),  # eV range
        'formation_energy': (-2.0, 0.0)  # eV/atom
    },
    'element_constraints': ['Si', 'O', 'Al'],
    'num_candidates': 50
}
candidates = gen_instance.generate(inputs)
```

### Multi-Generator Workflow

```python
# Use multiple generators for diverse candidates
generators = ['mattergen', 'gnome', 'imatgen']
all_candidates = []

for gen_id in generators:
    gen_instance = generator_factory[gen_id](gen_id, logger)
    candidates = gen_instance.generate(generation_inputs)
    all_candidates.extend(candidates)

# Filter and rank combined results
```

## Best Practices

### Generator Selection
- **MatterGen**: Excellent for general crystal structure generation
- **GNoME**: Best for property-targeted design
- **iMatGen**: Ideal for multi-objective optimization
- **MatGAN/MolGAN**: Good for exploring design spaces

### Input Design
- Start with broad constraints and narrow down
- Use multiple generation temperatures
- Consider computational cost vs. diversity trade-offs
- Validate input parameter ranges

### Output Processing
- Always validate generated structures
- Use multiple quality metrics
- Cross-reference with existing databases
- Consider experimental feasibility

### Integration Tips
- Combine with predictors for property validation
- Use databases for training data enhancement
- Implement iterative refinement loops
- Document generation parameters for reproducibility