1. Introduction

OpenMM consists of two parts:

  1. A set of libraries that lets programmers easily add molecular simulation features to their programs

  2. An “application layer” that exposes those features to end users who just want to run simulations

This guide is divided into three sections:

  • Part I describes the application layer. It is relevant to all users, but especially relevant to people who want to use OpenMM as a stand-alone application for running simulations.

  • Part II describes how to use the OpenMM libraries within your own applications. It is primarily relevant to programmers who want to write simulation applications.

  • Part III describes the mathematical theory behind the features found in OpenMM. It is relevant to all users.

1.1. Online Resources

You can find more documentation and other material at our website https://openmm.org/. In particular, you may want to consult the Python API documentation or the C++ API documentation, read the Frequently Asked Questions (FAQ), or browse the discussion forum on GitHub.

1.2. Referencing OpenMM

Any work that uses OpenMM should cite the following publication:

P. Eastman, R. Galvelis, R. P. Peláez, C. R. A. Abreu, S. E. Farr, E. Gallicchio, A. Gorenko, M. M. Henry, F. Hu, J. Huang, A. Krämer, J. Michel, J. A. Mitchell, V. S. Pande, J. PGLM Rodrigues, J. Rodriguez-Guerra, A. C. Simmonett, S. Singh, J. Swails, P. Turner, Y. Wang, I. Zhang, J. D. Chodera, G. De Fabritiis, and T. E. Markland. “OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.” J. Phys. Chem. B 128(1), pp. 109-116 (2023).

We depend on academic research grants to fund the OpenMM development efforts; citations of our publication will help demonstrate the value of OpenMM.

1.3. Acknowledgments

OpenMM research and development activities are supported by various individuals and funded from a number of sources: for up-to-date information, consult https://openmm.org/development.