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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. | ||
| - | '''This paper is to appear in Computer Physics Communications''' | + | '''Computer Physics Communications [https://doi.org/10.1016/j.cpc.2020.107168 2020]''' (open-source paper, awaiting full bibliographic references). |
| [http://arxiv.org/pdf/1907.12502 Electronic version (from arXiv)] | [http://arxiv.org/pdf/1907.12502 Electronic version (from arXiv)] | ||
| - | [https://github.com/jellyfysh GitHub site of the JeLLyFysh organization, from which the open-source application can be forked (that is, downloaded)] | + | [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]. |
Hoellmer Qin Faulkner Maggs Krauth 2019
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