| name | aflow-materials-discovery |
| description | AFLOW automatic materials discovery skill for high-throughput DFT calculations and materials database queries |
| allowed-tools | ["Bash","Read","Write","Edit","Glob","Grep"] |
| metadata | {"specialization":"physics","domain":"science","category":"condensed-matter","phase":6} |
| graph | {"domains":["domain:physics"],"skillAreas":["skill-area:physics-simulation","skill-area:statistical-analysis","skill-area:mathematical-reasoning"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-scientist","role:computational-scientist"]} |
AFLOW Materials Discovery
Purpose
Provides expert guidance on AFLOW for high-throughput materials discovery, including database queries, workflow generation, and machine learning integration.
Capabilities
- AFLOW database API queries
- Automatic workflow generation
- Thermodynamic stability analysis
- Prototype structure generation
- Descriptor calculation
- Machine learning integration
Usage Guidelines
- Database Queries: Search AFLOW database for materials
- Workflow Generation: Create automatic DFT workflows
- Stability Analysis: Analyze thermodynamic stability
- Prototypes: Generate crystal structure prototypes
- ML Integration: Calculate descriptors for ML models
Tools/Libraries
- AFLOW
- aflow.py
- Materials Project API