About OpenMM

Backed by researchers and developers from Stanford University, MSKCC, UPF, and others around the world.

Custom Forces

Want a custom force between two atoms? No problem. Write your force expressions in string format, and OpenMM will generate blazing fast code to do just that. No more hand-writing GPU kernels.

Highly Optimized

OpenMM is optimized for the latest generation of compute hardware, including AMD (via OpenCL) and NVIDIA (via CUDA) GPUs. We also heavily optimize for CPUs using intrinsics.

Portable

We strive to make our binaries as portable as possible. We've tested OpenMM on many flavors of Linux, OS X, and even Windows.

from simtk.openmm.app import *
from simtk.openmm import *
from simtk.unit import *
from sys import stdout

pdb = PDBFile('input.pdb')
forcefield = ForceField('amber99sb.xml', 'tip3p.xml')
system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds)
integrator = LangevinIntegrator(300*kelvin, 1/picosecond, 0.002*picoseconds)
simulation = Simulation(pdb.topology, system, integrator)
simulation.context.setPositions(pdb.positions)
simulation.minimizeEnergy()
simulation.reporters.append(PDBReporter('output.pdb', 1000))
simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True))
simulation.step(10000)
The cornerstone for the Omnia suite of tools for predictive biomolecular simulation. Learn more

Current Citation

Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, Vijay S. Pande. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS Comput. Biol. 13:e1005659, 2017.

More publications

A community of researchers and developers supporting OpenMM.

  • John Chodera (MSKCC)
  • Karmen Condic-Jurkic (MSKCC)
  • Peter Eastman (Stanford)
  • Gianni de Fabritiis (Pompeu Fabra)
  • Emilio Gallicchio (Brooklyn College)
  • Tom Markland (Stanford)
  • Justin McCallum (UCalgary)
  • Vijay Pande (Stanford)
  • Jean-Philip Piquemal (Sorbonne)
  • Jay Ponder (WashU)
  • Josh Rackers (Sandia)
  • Pengyu Ren (UT Austin)
  • Julia Rice (IBM)
  • Andy Simmonett (NIH)
  • Bill Swope (IBM)
OpenMM is currently (May 2020-Apr 2021) funded by a Chan Zuckerberg Initiative Essential Open Source Software for Science grant

Trusted and used by scientists around the world.

Performance on DHFR measured in ns/day. Details.

Simulation Type CUDA (Titan X Pascal) CUDA (Titan V) CPU (Core i7-7740X)
Implicit, 2 fs 927 1004 9.4
Implicit, 5 fs HMR 1528 1437 23.2
Explicit-RF, 2 fs 626 697 20.2
Explicit-RF, 5 fs HMR 1118 1071 45.6
Explicit-PME, 2 fs 393 419 16.5
Explicit-PME, 5 fs HMR 752 785 37.9