ACEMD: MD Engine

ACEMD is a production level molecular dynamics software specially optimized to run on NVIDIA graphics processing units (GPUs) and it is one of the world’s fastest molecular dynamics engines.

This software features a powerful scripting and extension interface in Python using HTMD,  allows the use of the popular CHARMM and AMBER force field formats without any change, and permits multi-host execution for replica exchange methods.

ACEMD has been used to perform molecular dynamics simulations of membrane and globular proteins, oligosaccharides, nucleic acids, and synthetic polymers.

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Features

Runs everywhere

You can use ACEMD in your computer, but it also gives you direct access to AceCloud, our cloud-based computing service.

Fast

ACEMD is the fastest MD engine for GPUs in the world.

Robust

ACEMD is the engine behind one of the largest distributed computing projects worldwide: GPUGRID.net, where thousands of MD simulations are run daily.

Compatible

ACEMD reads CHARMM and AMBER force field input formats making it readily compatible with any NAMD or AMBER system setups.

Expandable

Expand ACEMD possibilities through its plug-in interface. Choose from the existing repertoire or write your own.

Professional Support

Extensive, professional-grade support from our team of expert scientists and developers is available.

Performance

Benchmarking conditions: ECC off. CUDA8 and ACEMD ver 3200 or greater. Periodic boundary conditions, 9 A cutoff, PME long range electrostatic grid size 1A, hydrogen mass repartitioning, rigid bonds, Langevin thermostat, time step 4 fs. NVE8 runs the benchmarks in NVE and cutoff 8.


More resources

  • Introduction to ACEMD
  • ACEMD documentation
  • M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). pdf
  • M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). pdf
  • M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). pdf
  • If you need help, send us an email to support@acellera.com
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