Primary Publication
P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern, R. P. Wiewiora, B. R. Brooks, and V. S. Pande. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics." PLOS Comp. Biol. 13(7): e1005659. (2017)  View

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

Related Publications
M. S. Friedrichs, P. Eastman, V. Vaidyanathan, M. Houston, S. LeGrand, A. L. Beberg, D. L. Ensign, C. M. Bruns, V. S. Pande. “Accelerating Molecular Dynamic Simulation on Graphics Processing Units.” J. Comp. Chem., 30(6):864-872 (2009)  View

We describe a complete implementation of all-atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it can be more than 700 times faster than a conventional implementation running on a single CPU core.

P. Eastman and V.S. Pande. "OpenMM: A Hardware-Independent Framework for Molecular Simulations." Computing in Science & Engineering, 12:34-39. (2010)  View

The wide diversity of computer architectures today requires a new approach to software development. OpenMM is an abstraction layer for molecular mechanics simulations, allowing a single program to run efficiently on a variety of hardware platforms.

P. Eastman and V.S. Pande. "Constant Constraint Matrix Approximation: A Robust, Parallelizable Constraint Method for Molecular Simulations." J. Chem. Theor. Comput. 6:434-437. (2010)  View

We introduce a new algorithm, the constant constraint matrix approximation (CCMA), for constraining distances in molecular simulations. It combines the best features of many existing algorithms while avoiding their defects: it is fast and stable, can be applied to arbitrary constraint topologies, and can be efficiently implemented on modern parallel architectures. We test it on a protein with bond length and limited angle constraints and find that it requires less than one-sixth as many iterations as SHAKE to converge.

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)  View

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.

P. Eastman and V.S. Pande. "Efficient Nonbonded Interactions for Molecular Dynamics on a Graphics Processing Unit." J. Comput. Chem. 31:1268-72. (2010)  View

We describe an algorithm for computing nonbonded interactions with cutoffs on a graphics processing unit. We have incorporated it into OpenMM, a library for performing molecular simulations on high-performance computer architectures. We benchmark it on a variety of systems including boxes of water molecules, proteins in explicit solvent, a lipid bilayer, and proteins with implicit solvent. The results demonstrate that its performance scales linearly with the number of atoms over a wide range of system sizes, while being significantly faster than other published algorithms.