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:

Parse and validate search criteria
Query selected databases with optimized search terms
Aggregate and deduplicate results across databases
Apply filters and ranking algorithms
Generate similarity recommendations

Output Format:

{
    '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:

Translate property targets to generator inputs
Apply composition and element constraints
Generate material candidates using selected generators
Validate structural feasibility
Assess novelty against existing databases
Rank candidates by target property alignment

Output Format:

{
    '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:

Execute bulk queries across selected databases
Standardize data formats and units
Perform quality assessment and filtering
Conduct cross-database correlation analysis
Generate statistical summaries and visualizations

Output Format:

{
    '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:

Extract material structure and composition
Predict properties using selected predictors
Correlate with experimental data if available
Perform comparative analysis with similar materials
Generate characterization recommendations

Output Format:

{
    '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:

Prepare input structures for DFT calculation
Optimize structures using DFT methods
Calculate electronic structure properties
Compute thermodynamic and mechanical properties
Validate results and assess convergence

Output Format:

{
    '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:

Analyze crystal symmetry and space group
Identify structural motifs and coordination environments
Compare with reference structures and databases
Analyze structural relationships and transformations
Generate crystallographic reports

Output Format:

{
    '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:

Set up quantum mechanical calculations
Solve electronic structure problem
Analyze wavefunctions and orbitals
Calculate quantum mechanical properties
Generate quantum descriptors

Output Format:

{
    '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:

Generate strained structures for tensor calculations
Calculate response properties under various conditions
Fit tensor elements from response data
Analyze tensor symmetries and invariants
Compute derived properties from tensors

Output Format:

{
    '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

# 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

# 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