Official Resources
- Homepage: http://openpathsampling.org/
- Documentation: http://openpathsampling.org/latest/
- Source Repository: https://github.com/openpathsampling/openpathsampling
- License: MIT
Overview
OpenPathSampling (OPS) is a Python framework for path sampling simulations, including transition path sampling (TPS) and transition interface sampling (TIS). It provides a flexible and extensible platform for studying rare events and reaction mechanisms.
Scientific domain: Path sampling, rare events, reaction mechanisms
Target user community: Researchers studying rare events and transition pathways
Theoretical Methods
- Transition path sampling (TPS)
- Transition interface sampling (TIS)
- Multiple state TIS (MSTIS)
- Replica exchange TIS (RETIS)
- Committor analysis
Capabilities (CRITICAL)
- Transition path sampling
- Transition interface sampling
- Rate constant calculations
- Mechanism analysis
- Committor analysis
- OpenMM integration
- Flexible storage
Key Strengths
Path Sampling Methods:
- TPS implementation
- TIS variants
- Rate calculations
- Mechanism analysis
Flexibility:
- Extensible framework
- Custom collective variables
- Multiple MD engines
- Python interface
Inputs & Outputs
-
Input formats:
- Python configuration
- OpenMM systems
-
Output data types:
- Path ensembles
- Rate constants
- Committor data
- Trajectories
Interfaces & Ecosystem
- OpenMM: Primary MD engine
- MDTraj: Trajectory analysis
- NGLView: Visualization
Advanced Features
- TPS: Transition path sampling
- TIS: Transition interface sampling
- RETIS: Replica exchange TIS
- Committor: Reaction coordinate analysis
- Storage: Efficient trajectory storage
Performance Characteristics
- Python-based
- OpenMM for MD
- Efficient storage
- Good for rare events
Computational Cost
- Path sampling expensive
- Many trajectories needed
- OpenMM provides speed
- Overall: Significant but necessary
Best Practices
- Define good order parameters
- Validate path ensemble
- Check convergence
- Use sufficient paths
Limitations & Known Constraints
- Path sampling expensive
- Requires good CVs
- OpenMM focus
- Learning curve
Application Areas
- Protein folding
- Chemical reactions
- Nucleation
- Conformational changes
- Rare events
Verification & Sources
Primary sources:
- Website: http://openpathsampling.org/
- D.W.H. Swenson et al., J. Chem. Theory Comput. 15, 813 (2019)
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: OPEN (GitHub, MIT)