Databases

Databases provide access to existing materials data from various sources including experimental measurements, computational results, and curated collections. All databases implement the BaseDatabase interface with a standardized retrieve() method.

Available Databases

ICSD - Inorganic Crystal Structure Database

ID: icsd

The world’s largest database for inorganic crystal structures, containing over 200,000 entries.

Capabilities:

Crystal structure data for inorganic compounds
Crystallographic parameters (space groups, lattice parameters)
Experimental conditions and synthesis information
Literature references

Input Parameters:

search_term: Material formula or name
space_group: Crystal space group filter
element_list: Required/forbidden elements

Output Format:

Crystal structure files (CIF format)
Crystallographic data
Database entry IDs and references

COD - Crystallography Open Database

ID: cod

Open-access database of crystal structures of organic, inorganic, and organometallic compounds.

Capabilities:

Over 500,000 crystal structures
Both organic and inorganic compounds
Quality indicators for each entry
Free and open access

Input Parameters:

formula: Chemical formula
compound_name: Compound name
quality_filter: Minimum quality threshold

Output Format:

CIF files with crystal structures
Quality metrics
Publication information

OQMD - Open Quantum Materials Database

ID: oqmd

Database of DFT-calculated structures and properties for materials discovery.

Capabilities:

Over 800,000 DFT calculations
Formation energies and stability
Electronic properties (band gaps, DOS)
Thermodynamic data

Input Parameters:

composition: Chemical composition
stability_range: Formation energy range
band_gap_range: Electronic band gap range

Output Format:

DFT-calculated properties
Crystal structures
Formation energies and phase diagrams

AFLOWLIB - Automatic-FLOW Database

ID: aflowlib

High-throughput database for materials discovery with comprehensive property data.

Capabilities:

Over 3 million entries
Automated DFT calculations
Mechanical, electronic, and thermal properties
Materials genome approach

Input Parameters:

prototype: Crystal prototype
species: Chemical species
property_filter: Property range filters

Output Format:

Comprehensive property data
Crystal prototypes
Computed structures and energies

MP - Materials Project Database

ID: mp

Comprehensive collection of computed materials properties using high-throughput DFT.

Capabilities:

Over 150,000 materials
Electronic, ionic, and structural properties
Phase diagrams and stability analysis
Battery and photovoltaic applications data

Input Parameters:

material_id: Materials Project ID
formula: Chemical formula
property_criteria: Property-based search

Output Format:

Complete property datasets
Crystal structures
Phase stability information

Alexandria - Materials Database

ID: alexandria

Comprehensive database for materials discovery and design with focus on novel materials.

Capabilities:

Curated materials data
Novel materials discovery
Property prediction integration
Advanced search capabilities

Input Parameters:

material_class: Type of material
target_properties: Desired properties
synthesis_route: Synthesis constraints

Output Format:

Materials recommendations
Property predictions
Synthesis feasibility data

NOMAD - Novel Materials Discovery Repository

ID: nomad

Repository for computational materials science data with standardized formats.

Capabilities:

FAIR data principles compliance
Multiple computational codes support
Raw calculation data access
Metadata and provenance tracking

Input Parameters:

calculation_type: Type of computation
code_name: Computational software used
material_formula: Chemical formula

Output Format:

Raw calculation files
Standardized metadata
Calculation provenance information

JARVIS - Joint Automated Repository for Various Integrated Simulations

ID: jarvis

Database of materials properties from various computational methods and experiments.

Capabilities:

Multi-scale modeling data
Classical and quantum simulations
Experimental data integration
Machine learning ready datasets

Input Parameters:

property_name: Specific property of interest
method_type: Computational method
material_type: Class of materials

Output Format:

Multi-method property data
Calculation details
Experimental correlations

Usage Patterns

Basic Database Query

# Example of using a database in a Feature
db_instance = database_factory["icsd"]("icsd", logger)
inputs = {
    'search_term': 'silicon dioxide',
    'space_group': 'P6422'
}
results = db_instance.retrieve(inputs)

Multiple Database Integration

# Using multiple databases for comprehensive coverage
active_databases = [
    {'value': 'icsd', 'name': 'ICSD'},
    {'value': 'mp', 'name': 'Materials Project'},
    {'value': 'oqmd', 'name': 'OQMD'}
]

for db_config in active_databases:
    db_instance = database_factory[db_config['value']](
        db_config['value'], logger
    )
    results = db_instance.retrieve(search_inputs)
    # Combine and analyze results

Error Handling

try:
    results = db_instance.retrieve(inputs)
    logger.log(f"Database query successful: {len(results)} entries found", 'info')
except Exception as e:
    logger.log(f"Database query failed: {str(e)}", 'error')
    # Handle error gracefully

Best Practices

Database Selection

ICSD: Best for inorganic crystal structures and experimental data
MP/OQMD: Ideal for DFT-calculated properties and stability
COD: Good for organic/organometallic structures
JARVIS: Excellent for machine learning applications

Query Optimization

Use specific search terms to reduce result sets
Apply appropriate filters for property ranges
Consider database-specific capabilities
Cache frequently used results

Data Validation

Cross-reference between multiple databases
Verify data quality indicators
Check calculation convergence criteria
Validate experimental conditions