# Materials Exploration Features

Materials Exploration features focus on discovering, generating, and analyzing materials for fundamental research applications. These features provide comprehensive workflows for materials scientists to explore chemical space, discover new materials, and understand their properties.

## Available Features

### 1. Material Search (ID: 1)
**Class**: `MaterialSearchFeature`

Advanced database searching and filtering for materials discovery.

**Description**: "Material Search: Advanced search and filtering across multiple materials databases"

**Capabilities**:
- Multi-database querying
- Advanced filtering by composition, structure, and properties
- Similarity searching
- Recommendation systems

**Input Parameters**:
- `searchTerm`: Material name, formula, or keyword
- `elementList`: Required or forbidden elements
- `propertyRange`: Target property ranges (band gap, formation energy, etc.)
- `structureType`: Crystal system or space group constraints
- `maxResults`: Maximum number of results to return
- `active_databases`: Database configurations to search

**Processing Steps**:
1. Parse and validate search criteria
2. Query selected databases with optimized search terms
3. Aggregate and deduplicate results across databases
4. Apply filters and ranking algorithms
5. Generate similarity recommendations

**Output Format**:
```python
{
    'search_results': [
        {
            'material_id': str,
            'formula': str,
            'structure': dict,
            'properties': dict,
            'database_source': str,
            'similarity_score': float
        }
    ],
    'search_statistics': {
        'total_found': int,
        'databases_searched': list,
        'search_time': float
    },
    'recommendations': [...]
}
```

**Use Cases**:
- Finding materials with specific properties
- Literature review and prior art search
- Building training datasets
- Identifying research gaps

### 2. Material Generation (ID: 2)
**Class**: `MaterialGenerationFeature`

AI-powered generation of novel materials with targeted properties.

**Description**: "Material Generation: Generate novel materials using AI and machine learning models"

**Capabilities**:
- Multi-generator ensemble approaches
- Property-targeted generation
- Composition-constrained generation
- Novelty assessment

**Input Parameters**:
- `targetProperties`: Desired property values and ranges
- `elementConstraints`: Allowed/forbidden elements
- `compositionSpace`: Chemical composition constraints
- `numCandidates`: Number of materials to generate
- `noveltyThreshold`: Minimum novelty requirement
- `active_generators`: Generator configurations to use

**Processing Steps**:
1. Translate property targets to generator inputs
2. Apply composition and element constraints
3. Generate material candidates using selected generators
4. Validate structural feasibility
5. Assess novelty against existing databases
6. Rank candidates by target property alignment

**Output Format**:
```python
{
    'generated_materials': [
        {
            'structure': dict,
            'composition': str,
            'predicted_properties': dict,
            'novelty_score': float,
            'generator_source': str,
            'confidence': float
        }
    ],
    'generation_statistics': {
        'total_generated': int,
        'valid_structures': int,
        'novel_materials': int,
        'generation_time': float
    }
}
```

**Use Cases**:
- Discovery of materials with specific properties
- Filling gaps in materials space
- Inverse design applications
- High-throughput virtual screening

### 3. Database Extractor (ID: 3)
**Class**: `DatabaseExtractorFeature`

Bulk extraction and analysis of materials data from multiple databases.

**Description**: "Database Extractor: Extract and analyze large datasets from materials databases"

**Capabilities**:
- Bulk data extraction
- Cross-database correlation analysis
- Data quality assessment
- Statistical analysis and visualization

**Input Parameters**:
- `extractionQuery`: Broad query for data extraction
- `dataFields`: Specific data fields to extract
- `analysisType`: Type of analysis to perform
- `qualityFilters`: Data quality criteria
- `outputFormat`: Format for extracted data
- `active_databases`: Databases to extract from

**Processing Steps**:
1. Execute bulk queries across selected databases
2. Standardize data formats and units
3. Perform quality assessment and filtering
4. Conduct cross-database correlation analysis
5. Generate statistical summaries and visualizations

**Output Format**:
```python
{
    'extracted_data': {
        'materials': [...],
        'properties': {...},
        'statistics': {...}
    },
    'analysis_results': {
        'correlations': dict,
        'quality_metrics': dict,
        'coverage_analysis': dict
    },
    'visualizations': [...]
}
```

**Use Cases**:
- Building comprehensive datasets
- Materials informatics studies
- Trend analysis across materials classes
- Database comparison and validation

### 4. Material Characterization (ID: 4)
**Class**: `MaterialCharacterizationFeature`

Comprehensive characterization of material properties using multiple methods.

**Description**: "Material Characterization: Comprehensive analysis and characterization of material properties"

**Capabilities**:
- Multi-property prediction and analysis
- Experimental data integration
- Property correlation analysis
- Characterization recommendations

**Input Parameters**:
- `targetMaterial`: Material to characterize
- `propertyList`: Properties to analyze
- `characterizationMethods`: Experimental/computational methods
- `comparisonMaterials`: Reference materials for comparison
- `active_predictors`: Predictor configurations to use

**Processing Steps**:
1. Extract material structure and composition
2. Predict properties using selected predictors
3. Correlate with experimental data if available
4. Perform comparative analysis with similar materials
5. Generate characterization recommendations

**Output Format**:
```python
{
    'material_properties': {
        'structural': {...},
        'electronic': {...},
        'mechanical': {...},
        'thermal': {...}
    },
    'characterization_data': {
        'predictions': {...},
        'experimental': {...},
        'correlations': {...}
    },
    'recommendations': [...]
}
```

**Use Cases**:
- Complete material property assessment
- Validation of new materials
- Comparison with existing materials
- Experimental planning

### 5. DFT Calculation (ID: 5)
**Class**: `DFTCalculationFeature`

Density functional theory calculations for accurate property prediction.

**Description**: "DFT Calculation: Perform DFT calculations for accurate materials properties"

**Capabilities**:
- Structure optimization
- Electronic structure calculations
- Thermodynamic property prediction
- High-accuracy property calculations

**Input Parameters**:
- `inputStructure`: Initial crystal structure
- `calculationType`: Type of DFT calculation
- `functional`: Exchange-correlation functional
- `kPointDensity`: K-point mesh density
- `convergenceCriteria`: Convergence parameters
- `active_predictors`: DFT-based predictors to use

**Processing Steps**:
1. Prepare input structures for DFT calculation
2. Optimize structures using DFT methods
3. Calculate electronic structure properties
4. Compute thermodynamic and mechanical properties
5. Validate results and assess convergence

**Output Format**:
```python
{
    'optimized_structure': dict,
    'electronic_properties': {
        'band_structure': dict,
        'dos': dict,
        'band_gap': float,
        'work_function': float
    },
    'thermodynamic_properties': {
        'formation_energy': float,
        'heat_capacity': dict,
        'thermal_expansion': float
    },
    'calculation_details': {...}
}
```

**Use Cases**:
- High-accuracy property calculations
- Structure optimization
- Electronic structure analysis
- Thermodynamic stability assessment

### 6. Crystallographic Analysis (ID: 6)
**Class**: `CrystallographicAnalysisFeature`

Detailed analysis of crystal structures and symmetries.

**Description**: "Crystallographic Analysis: Analyze crystal structures, symmetries, and structural relationships"

**Capabilities**:
- Space group analysis
- Structure comparison and matching
- Defect analysis
- Phase relationship studies

**Input Parameters**:
- `crystalStructure`: Input crystal structure
- `analysisType`: Type of crystallographic analysis
- `tolerances`: Symmetry and matching tolerances
- `referenceStructures`: Structures for comparison
- `active_databases`: Databases for structural comparison

**Processing Steps**:
1. Analyze crystal symmetry and space group
2. Identify structural motifs and coordination environments
3. Compare with reference structures and databases
4. Analyze structural relationships and transformations
5. Generate crystallographic reports

**Output Format**:
```python
{
    'symmetry_analysis': {
        'space_group': str,
        'point_group': str,
        'lattice_system': str,
        'symmetry_operations': list
    },
    'structural_analysis': {
        'coordination_environments': dict,
        'bond_analysis': dict,
        'polyhedral_analysis': dict
    },
    'comparison_results': [...],
    'structural_relationships': [...]
}
```

**Use Cases**:
- Crystal structure determination
- Phase identification
- Structural relationship studies
- Defect and disorder analysis

### 7. Quantum Mechanics (ID: 7)
**Class**: `QuantumMechanicsFeature`

Quantum mechanical calculations for fundamental property understanding.

**Description**: "Quantum Mechanics: Quantum mechanical calculations and analysis of electronic properties"

**Capabilities**:
- Wavefunction analysis
- Orbital visualization
- Electronic property calculations
- Quantum mechanical descriptors

**Input Parameters**:
- `quantumSystem`: System for quantum calculation
- `calculationLevel`: Level of quantum theory
- `basisSet`: Quantum mechanical basis set
- `analysisOptions`: Types of quantum analysis
- `active_predictors`: Quantum-based predictors

**Processing Steps**:
1. Set up quantum mechanical calculations
2. Solve electronic structure problem
3. Analyze wavefunctions and orbitals
4. Calculate quantum mechanical properties
5. Generate quantum descriptors

**Output Format**:
```python
{
    'electronic_structure': {
        'orbital_energies': list,
        'orbital_occupations': list,
        'electron_density': dict
    },
    'quantum_properties': {
        'ionization_potential': float,
        'electron_affinity': float,
        'chemical_hardness': float
    },
    'wavefunction_analysis': {...},
    'quantum_descriptors': {...}
}
```

**Use Cases**:
- Fundamental electronic structure analysis
- Chemical bonding studies
- Reactivity prediction
- Quantum descriptor generation

### 8. Tensor Analysis (ID: 8)
**Class**: `TensorAnalysisFeature`

Analysis of tensor properties such as elastic, piezoelectric, and optical tensors.

**Description**: "Tensor Analysis: Calculate and analyze tensor properties including elastic, piezoelectric, and optical tensors"

**Capabilities**:
- Elastic tensor calculations
- Piezoelectric property analysis
- Optical tensor determination
- Anisotropy analysis

**Input Parameters**:
- `materialStructure`: Crystal structure for tensor analysis
- `tensorTypes`: Types of tensors to calculate
- `strainAmplitudes`: Strain amplitudes for elastic calculations
- `temperatureRange`: Temperature range for analysis
- `active_predictors`: Tensor-capable predictors

**Processing Steps**:
1. Generate strained structures for tensor calculations
2. Calculate response properties under various conditions
3. Fit tensor elements from response data
4. Analyze tensor symmetries and invariants
5. Compute derived properties from tensors

**Output Format**:
```python
{
    'elastic_properties': {
        'elastic_tensor': array,
        'bulk_modulus': float,
        'shear_modulus': float,
        'elastic_anisotropy': float
    },
    'piezoelectric_properties': {
        'piezoelectric_tensor': array,
        'piezoelectric_constants': dict
    },
    'optical_properties': {
        'dielectric_tensor': array,
        'refractive_indices': list,
        'birefringence': float
    },
    'anisotropy_analysis': {...}
}
```

**Use Cases**:
- Mechanical property analysis
- Piezoelectric device design
- Optical materials characterization
- Anisotropic property studies

## Common Workflow Patterns

### Multi-Feature Material Discovery
```python
# 1. Search for existing materials
search_results = material_search.process({
    'searchTerm': 'high mobility semiconductor',
    'propertyRange': {'mobility': (100, 10000)},
    'active_databases': database_configs
})

# 2. Generate new candidates
generation_results = material_generation.process({
    'targetProperties': {'mobility': (1000, 5000)},
    'elementConstraints': search_results['common_elements'],
    'active_generators': generator_configs
})

# 3. Characterize promising materials
for material in generation_results['top_candidates']:
    characterization = material_characterization.process({
        'targetMaterial': material,
        'propertyList': ['mobility', 'stability', 'band_gap'],
        'active_predictors': predictor_configs
    })
```

### Comprehensive Material Analysis
```python
# Complete analysis workflow
material = get_target_material()

# Structure and symmetry analysis
crystal_analysis = crystallographic_analysis.process({
    'crystalStructure': material.structure,
    'analysisType': 'full_symmetry'
})

# Electronic structure
dft_results = dft_calculation.process({
    'inputStructure': material.structure,
    'calculationType': 'electronic_structure'
})

# Tensor properties
tensor_results = tensor_analysis.process({
    'materialStructure': material.structure,
    'tensorTypes': ['elastic', 'piezoelectric', 'optical']
})
```

## Best Practices

### Feature Selection
- **Material Search**: Start here for any new research direction
- **Material Generation**: Use when existing materials don't meet requirements
- **DFT Calculation**: For high-accuracy, definitive property values
- **Material Characterization**: For comprehensive property assessment

### Input Guidelines
- Start with broad searches and narrow down progressively
- Use multiple databases and predictors for validation
- Consider computational cost vs. accuracy trade-offs
- Document all parameters for reproducibility

### Result Interpretation
- Cross-validate results across multiple features
- Consider uncertainty and confidence levels
- Validate against experimental data when available
- Use statistical analysis for large datasets