Hoellmer Qin Faulkner Maggs Krauth 2019

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-'''P. Hoellmer, L. Qin, M. F. Faulkner, A. C. Maggs, W. Krauth''' '''''JeLLyFysh-Version1.0 -- a Python application for all-atom event-chain Monte Carlo''''' ''' Physical Review B 99 064435 (2019)'''+'''P. Hoellmer, L. Qin, M. F. Faulkner, A. C. Maggs, W. Krauth''' '''''JeLLyFysh-Version1.0 -- a Python application for all-atom event-chain Monte Carlo''''' ''' (2020)'''
=Paper= =Paper=
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We present JeLLyFysh-Version1.0, an open-source Python application for event-chain Monte Carlo (ECMC), an event-driven irreversible Markov-chain Monte Carlo algorithm for classical N-body simulations in statistical mechanics, biophysics and electrochemistry. The application's architecture closely mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules. We present JeLLyFysh-Version1.0, an open-source Python application for event-chain Monte Carlo (ECMC), an event-driven irreversible Markov-chain Monte Carlo algorithm for classical N-body simulations in statistical mechanics, biophysics and electrochemistry. The application's architecture closely mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules.
-'''Computer Physics Communications [https://doi.org/10.1016/j.cpc.2020.107168 2020]''' (open-source paper, awaiting full bibliographic references).+'''[https://doi.org/10.1016/j.cpc.2020.107168 Computer Physics Communications 253 107168 (2020)]''' (open-access publication).
 + 
 +[https://doi.org/10.1016/j.cpc.2020.107168 doi: 10.1016/j.cpc.2020.107168]
[http://arxiv.org/pdf/1907.12502 Electronic version (from arXiv)] [http://arxiv.org/pdf/1907.12502 Electronic version (from arXiv)]
-[https://github.com/jellyfysh/JeLLyFysh https://github.com/jellyfysh GitHub repertory], from which the project may be [https://en.wikipedia.org/wiki/Fork_(software_development) forked].+[https://github.com/jellyfysh/JeLLyFysh https://github.com/jellyfysh/JeLLyFysh GitHub repository] from which the JellyFysh application may be [https://en.wikipedia.org/wiki/Fork_(software_development) forked], cloned, or simply copied.

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P. Hoellmer, L. Qin, M. F. Faulkner, A. C. Maggs, W. Krauth JeLLyFysh-Version1.0 -- a Python application for all-atom event-chain Monte Carlo (2020)

Paper

Abstract

We present JeLLyFysh-Version1.0, an open-source Python application for event-chain Monte Carlo (ECMC), an event-driven irreversible Markov-chain Monte Carlo algorithm for classical N-body simulations in statistical mechanics, biophysics and electrochemistry. The application's architecture closely mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules.

Computer Physics Communications 253 107168 (2020) (open-access publication).

doi: 10.1016/j.cpc.2020.107168

Electronic version (from arXiv)

https://github.com/jellyfysh/JeLLyFysh GitHub repository from which the JellyFysh application may be forked, cloned, or simply copied.

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