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Materials database query skill for accessing structure and property data from multiple repositories
---
name: materials-database-querier-a4a5e2746c52
description: Materials database query skill for accessing structure and property data from multiple repositories
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: computational
priority: high
phase: 6
tools-libraries:
- pymatgen
- Materials Project API
- AFLOW REST API
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# Materials Database Querier
## Purpose
The Materials Database Querier skill provides unified access to multiple materials databases for structure and property retrieval, enabling comprehensive materials search and data aggregation across repositories.
## Capabilities
- Materials Project API integration
- AFLOW database queries
- ICSD/CSD structure retrieval
- NOMAD repository access
- Cross-database searches
- Property aggregation and comparison
## Usage Guidelines
### Database Query Workflow
1. **Query Design**
- Define search criteria
- Select target databases
- Set property filters
2. **Data Retrieval**
- Execute queries
- Handle pagination
- Aggregate results
3. **Data Processing**
- Standardize formats
- Compare across sources
- Export for analysis
## Process Integration
- Machine Learning Materials Discovery Pipeline
- DFT Calculation Pipeline for Nanomaterials
- Structure-Property Correlation Analysis
## Input Schema
```json
{
"query_type": "composition|structure|property",
"databases": ["materials_project", "aflow", "icsd"],
"criteria": {
"elements": ["string"],
"property_range": {"property": "string", "min": "number", "max": "number"}
},
"limit": "number"
}
```
## Output Schema
```json
{
"materials": [{
"id": "string",
"formula": "string",
"structure_file": "string",
"properties": {
"bandgap": "number",
"formation_energy": "number"
},
"source": "string"
}],
"total_found": "number",
"query_metadata": {
"databases_searched": ["string"],
"query_time": "number"
}
}
```
---
name: materials-database-querier-a4a5e2746c52
description: Materials database query skill for accessing structure and property data from multiple repositories
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: computational
priority: high
phase: 6
tools-libraries:
- pymatgen
- Materials Project API
- AFLOW REST API
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# Materials Database Querier
## Purpose
The Materials Database Querier skill provides unified access to multiple materials databases for structure and property retrieval, enabling comprehensive materials search and data aggregation across repositories.
## Capabilities
- Materials Project API integration
- AFLOW database queries
- ICSD/CSD structure retrieval
- NOMAD repository access
- Cross-database searches
- Property aggregation and comparison
## Usage Guidelines
### Database Query Workflow
1. **Query Design**
- Define search criteria
- Select target databases
- Set property filters
2. **Data Retrieval**
- Execute queries
- Handle pagination
- Aggregate results
3. **Data Processing**
- Standardize formats
- Compare across sources
- Export for analysis
## Process Integration
- Machine Learning Materials Discovery Pipeline
- DFT Calculation Pipeline for Nanomaterials
- Structure-Property Correlation Analysis
## Input Schema
```json
{
"query_type": "composition|structure|property",
"databases": ["materials_project", "aflow", "icsd"],
"criteria": {
"elements": ["string"],
"property_range": {"property": "string", "min": "number", "max": "number"}
},
"limit": "number"
}
```
## Output Schema
```json
{
"materials": [{
"id": "string",
"formula": "string",
"structure_file": "string",
"properties": {
"bandgap": "number",
"formation_energy": "number"
},
"source": "string"
}],
"total_found": "number",
"query_metadata": {
"databases_searched": ["string"],
"query_time": "number"
}
}
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.