Official Resources
- Source Repository: https://github.com/aiidaplugins/aiida-lammps
- Documentation: https://aiida-lammps.readthedocs.io/
- License: Open source (MIT)
Overview
aiida-lammps is an AiiDA plugin for the LAMMPS molecular dynamics code. It enables running LAMMPS calculations within the AiiDA framework with provenance tracking, potential management, and output parsing for classical MD simulations.
Scientific domain: AiiDA plugin for LAMMPS molecular dynamics
Target user community: Researchers using LAMMPS with AiiDA workflow management
Theoretical Methods
- LAMMPS input generation and management
- Potential parameter handling
- Output parsing
- AiiDA workflow integration
- Provenance tracking
Capabilities (CRITICAL)
- LAMMPS calculation submission via AiiDA
- Potential management (pair_style, pair_coeff)
- Output parsing (energies, forces, trajectory)
- Workflow automation
- Provenance tracking
Sources: GitHub repository
Key Strengths
LAMMPS Integration:
- Multiple potential types
- Input parameter handling
- Output parsing
- Error recovery
Provenance:
- Full calculation tracking
- Reproducibility
- Data management
Inputs & Outputs
-
Input formats:
- LAMMPS input parameters
- Potential data
- Structure data
-
Output data types:
- Parsed LAMMPS output
- Energies, forces, trajectory
- Provenance graph
Interfaces & Ecosystem
- AiiDA: Workflow framework
- LAMMPS: MD code
- Python: Core language
Performance Characteristics
- Speed: Workflow management (fast)
- Accuracy: Potential-dependent
- System size: Any
- Automation: Full
Computational Cost
- Plugin: Negligible
- LAMMPS calculations: Minutes to hours
Limitations & Known Constraints
- LAMMPS only: No other code support
- AiiDA required: Must have AiiDA
- Potential files: Need potential data
- Classical MD: Not ab initio
Comparison with Other Codes
- vs aiida-vasp: Different code type (MD vs DFT)
- vs LAMMPS directly: aiida-lammps adds provenance
- Unique strength: AiiDA plugin for LAMMPS with potential management and provenance tracking
Application Areas
MD Workflows:
- Automated LAMMPS calculations
- High-throughput MD
- Property prediction from MD
Mixed-Code:
- DFT + MD via AiiDA
- Multi-scale simulations
Best Practices
Setup:
- Install AiiDA and configure LAMMPS
- Set up potential data
- Test with simple calculation
Community and Support
- Open source (MIT)
- ReadTheDocs documentation
- AiiDA plugins community
Verification & Sources
Primary sources:
- GitHub: https://github.com/aiidaplugins/aiida-lammps
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Specialized strength: AiiDA plugin for LAMMPS with potential management and provenance tracking