Using collective variables to drive molecular dynamics simulations

By G. Fiorin, M. Klein, J'erome H'enin. 2013

941 citations recorded by the source corpus.

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation engines.

Subfields: Atomic and Molecular Physics, and Optics, Condensed Matter Physics

View Paper: https://www.semanticscholar.org/paper/2f7abfa0fa7c3627a06891402102a9a56a755d5b

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In Atomic and Molecular Physics, and Optics

In Condensed Matter Physics

Citations in the corpus

Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration (2013, methodology): “The native564 collective variable module for NAMD has been recently ported to LAMMPS [242].”
The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask (2014, methodology): “39 Although better sampling of orthogonal degrees of freedom is the most promising direction for increasing the e ﬃ ciency of the adapting biasing force method, there is also room for improvement in achieving convergence along the transition coordinate.”
Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0] (2022, methodology): “The Colvars Module is interfaced with NAMD [236], LAMMPS [237], GROMACS [238], and VMD [239] for colvar analysis [240].”