About OpenMM

Backed by researchers and developers from Stanford University, MSKCC, 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.


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.reporters.append(PDBReporter('output.pdb', 1000))
simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True))
The cornerstone for the Omnia suite of tools for predictive biomolecular simulation. Learn more

Pande Lab

  • Vijay Pande
  • Peter Eastman
  • Yutong Zhao
  • Robert McGibbon
  • Lee-Ping Wang

Chodera Lab

  • John Chodera
  • Kyle Beauchamp


  • Jason Swails

If you publish papers using OpenMM, we kindly ask you cite the following to help feed hungry grad students:

P. Eastman, M. S. Friedrichs, J. D. Chodera, R. J. Radmer, C. M. Bruns, J. P. Ku, K. A. Beauchamp, T. J. Lane, L.-P. Wang, D. Shukla, T. Tye, M. Houston, T. Stich, C. Klein, M. R. Shirts, and V. S. Pande. "OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation." J. Chem. Theor. Comput. 9(1):461-469. (2013)

More publications

Trusted and used by scientists around the world.

Performance measured in ns/day. Details.

Simulation Type CUDA (Titan X Pascal) OpenCL (Titan X Pascal) CPU (Xeon E5-2697 v4)
Implicit, 2 fs 863 665 19.8
Implicit, 5 fs HMR 1386 816 44.1
Explicit-RF, 2 fs 607 486 21.0
Explicit-RF, 5 fs HMR 1047 689 43.6
Explicit-PME, 2 fs 384 288 15.6
Explicit-PME, 5 fs HMR 762 430 34.8

Maintained by the OpenMM team. Copyright 2017, Stanford University