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
  • Matthew Harrigan

Chodera Lab

  • John Chodera
  • Levi Naden
  • Chaya Stern
  • Rafal Wiewiora
  • Andrea Rizzi


  • Bernard Brooks
  • Andy Simmonett


  • Jason Swails
  • Robert McGibbon
  • Yutong Zhao
  • Kyle Beachamp
  • Lee-Ping Wang

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

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

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 912 903 9.2
Implicit, 5 fs HMR 1500 1299 22.4
Explicit-RF, 2 fs 612 623 19.2
Explicit-RF, 5 fs HMR 1093 980 43.0
Explicit-PME, 2 fs 421 375 11.6
Explicit-PME, 5 fs HMR 819 716 26.9