Official Resources
- Homepage: https://github.com/bch-gnome/JADE-NAMD
- Documentation: https://jade-namd.readthedocs.io/
- Source Repository: https://github.com/bch-gnome/JADE-NAMD
- License: MIT License
Overview
JADE-NAMD is a Python-based software package designed for performing on-the-fly nonadiabatic molecular dynamics (NAMD) simulations. It employs the trajectory surface hopping method and serves as a flexible interface driver that connects various quantum chemistry packages (calculators) with dynamics propagation. It is designed to be user-friendly and easily extensible for different electronic structure methods.
Scientific domain: Nonadiabatic molecular dynamics, excited-state dynamics, trajectory surface hopping
Target user community: Researchers using standard QC packages for excited-state dynamics
Theoretical Methods
- Trajectory Surface Hopping (TSH)
- Fewest-Switches Surface Hopping (FSSH)
- On-the-fly dynamics
- Numerical gradient integration
- Velocity Verlet integration
- Decoherence corrections (ID-A, EDC)
- Landau-Zener probability
Capabilities (CRITICAL)
- Molecular dynamics propagation
- Surface hopping algorithms
- Interface with multiple QC codes
- Energy and gradient handling
- Non-adiabatic coupling vectors (NAC)
- Probabilistic hopping
- Trajectory analysis
- Kinetic energy conservation
- State tracking
Sources: GitHub repository, Documentation
Key Strengths
Flexible Interfaces:
- Turbomole
- GAMESS-US
- Gaussian
- Molpro
- MNDO
- Easy to extend for other codes
Python-Based:
- Modern code structure
- Easy installation (pip)
- Readable codebase
- Scriptable workflows
Methodological Generality:
- Independent of electronic structure method
- Supports any method providing E, Grad, NAC
- Various decoherence schemes
Inputs & Outputs
-
Input formats:
- Python driver script
- Configuration files (JSON/YAML)
- Geometry files (XYZ)
- Calculator templates
-
Output data types:
- Trajectory coordinates/velocities
- Energy evolution
- State population
- Hopping logs
- Restart files
Interfaces & Ecosystem
- Calculators: Turbomole, GAMESS, Gaussian, Molpro, MNDO
- Language: Pure Python
- Dependencies: NumPy, SciPy
- Analysis: Matplotlib, standard trajectory tools
Advanced Features
Decoherence Handling:
- Instantaneous Decoherence (ID-A)
- Energy-based Decoherence (EDC)
- Improved hopping consistency
Modular Design:
- Calculator abstract base class
- Pluggable dynamics engines
- Customizable propagators
Performance Characteristics
- Speed: Driven by external QC code
- Overhead: Minimal Python overhead
- Parallelization: Script-level trajectory parallelism
Computational Cost
- Bottleneck: Quantum chemistry calculations
- Scaling: Dependent on QC method (TDDFT vs CASSCF)
- Typical: Tens to hundreds of trajectories
Limitations & Known Constraints
- Calculator Dependence: Needs external software
- NAC Availability: Requires QC code to compute couplings
- System Size: Limited by QC method capabilities
Comparison with Other Codes
- vs SHARC: JADE is lighter weight, Python-centric
- vs NEXMD: JADE uses ab initio codes, NEXMD is semiempirical
- vs Newton-X: JADE is a simpler, more modern Python alternative
- Unique strength: Lightweight, Python-native, easy interfacing
Application Areas
- Photoisomerization: Azobenzene, retinal
- Photodissociation: Bond breaking dynamics
- Intersystem Crossing: Spin-forbidden transitions
- Materials: Molecular switches
Best Practices
- Interface check: Verify QC output parsing
- Timestep: Appropriate for nuclear motion (0.5-1.0 fs)
- Ensemble: Run sufficient trajectories for statistics
- Cleanup: Manage scratch files from QC codes
Community and Support
- Open-source MIT license
- GitHub issue tracker
- Documentation on ReadTheDocs
- Active development by detailed contributors
Verification & Sources
Primary sources:
- GitHub: https://github.com/bch-gnome/JADE-NAMD
- Documentation: https://jade-namd.readthedocs.io/
Confidence: VERIFIED - Active GitHub project
Verification status: ✅ VERIFIED
- Official homepage: ACCESSIBLE
- Source code: OPEN (MIT)
- Active development: Recent commits
- Specialized strength: Python-based surface hopping driver