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Cited by 682

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OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation

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Eastman, Peter, Friedrichs, Mark S., Chodera, John D., Radmer, Randall J., Bruns, Christopher M., Ku, Joy P., Beauchamp, Kyle A., Lane, Thomas J., Wang, Lee-Ping, Shukla, Diwakar, Tye, Tony, Houston, Mike, Stich, Timo, Klein, Christoph, Shirts, Michael R., Pande, Vijay S.·2013·Journal of Chemical Theory and Computation
DOI 10.1021/ct300857j
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Abstract

OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.

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Citations

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Metadata

Authors
Eastman, P., Friedrichs, M. S., Chodera, J. D., Radmer, R. J., Bruns, C. M., Ku, J. P., Beauchamp, K. A., Lane, T. J., Wang, L.-P., Shukla, D., Tye, T., Houston, M., Stich, T., Klein, C., Shirts, M. R., & Pande, V. S.
Venue
Journal of Chemical Theory and Computation
Publisher
American Chemical Society (ACS)
Published
2012-11-07
Volume
9
Issue
1
Pages
461-469
ISSN
  • 1549-9618 (ISSN-L)
  • 1549-9626
DOI
10.1021/ct300857j
OpenAlex
W1976203248
Cited by
682
References
1
FWCI
45.87
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.