NAMD
NAMD is a parallel, object-oriented molecular dynamics code
designed for high-performance simulation of large biomolecular systems.
NAMD is written using the Charm++ parallel programming model,
noted for its parallel efficiency and often used to simulate large
systems (millions of atoms). NAMD is implemented using the Converse
runtime system. Converse provides machine-independent interface to all
popular parallel computers as well as workstation clusters.
Features include:
- VMD used to prepare molecular structure for simulation
- Also reads X-PLOR, CHARMM, AMBER, and GROMACS input files
- Psfgen tool generates structure and coordinate files for
CHARMM force field
- Efficient conjugate gradient minimization
- Fixed atoms and harmonic restraints
- Thermal equilibration via periodic rescaling,
reinitialization, or Langevin dynamics
- Force Field Compatibility
- Efficient Full Electrostatics Algorithms
- Multiple Time Stepping
- Input and Output Compatibility
- Dynamics Simulation Options - MD simulations using options
such as:
- Constant energy dynamics
- Constant temperature dynamics via:
- Velocity rescaling
- Velocity reassignment
- Langevin dynamics
- Periodic boundary conditions
- Constant pressure dynamics via
- Berendsen pressure coupling
- Nosé-Hoover Langevin piston
- Energy minimization
- Fixed atoms
- Rigid waters
- Rigid bonds to hydrogen
- Harmonic restraints
- Spherical or cylindrical boundary restraints
- Interactive molecular dynamics simulations
- Accelerated molecular dynamics provides a robust biasing
potential that increases
the escape rates from potential wells, while still converging to
the correct canonical distribution.
- Load balancing
- Shared-Memory Multicore and SMP Builds
- Replica-based umbrella sampling via collective variables
module
- Optimized shared-memory single-node and multiple-node CUDA
builds
- CUDA GPU-accelerated generalized Born implicit solvent
(GBIS) model
- CUDA GPU-accelerated energy evaluation and minimization
- Native CRAY XE/XK uGNI network layer implementation
- Faster grid forces and lower-accuracy "lite" implementation
- Hybrid MD with knowledge-based Go forces to drive folding
- Linear combination of pairwise overlaps (LCPO) SASA for
GBIS model
- Weeks-Chandler-Anderson decomposition for alchemical FEP
simulations
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Last Updated Saturday, April 13 2013 @ 04:11 PM EDT
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