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JELLYFYSH-Version1.0-a Python application for all-atom event-chain Monte Carlo

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Krauth,  Werner
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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1907.12502.pdf
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Citation

Hoellmer, P., Qin, L., Faulkner, M. F., Maggs, A. C., & Krauth, W. (2020). JELLYFYSH-Version1.0-a Python application for all-atom event-chain Monte Carlo. Computer Physics Communications, 253: 107168. doi:10.1016/j.cpc.2020.107168.


Cite as: https://hdl.handle.net/21.11116/0000-0009-1774-B
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 mirrors the mathematical formulation of ECMC. Local potentials, long-range Coulomb interactions and multibody 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.
Program summary
Program title: JELLYFYSH-Version1.0
Program files doi: http://dx.doi.org/10.17632/srrjt9493d.1
Licensing provisions: GNU GPLv3
Programming language: Python 3
Nature of problem: Event-chain Monte Carlo (ECMC) simulations for classical N-body simulations in statistical mechanics, biophysics and electrochemistry.
Solution method: Event-driven irreversible Markov-chain Monte Carlo algorithm.
Additional comments: The application is complete with sample configuration files, docstrings, and unittests. The manuscript is accompanied by a frozen copy of JELLYFYSH-Version1.0 that is made publicly available on GitHub (repository http://github.com/jellyfysh/JeLLyFysh, commit hash d453d497256e7270e8babc8e04d20fb6d847dee4). (C) 2020 The Authors. Published by Elsevier B.V.