JeLLyFysh-Version1.0 - a Python application for all-atom event-chain Monte Carlo
JeLLyFysh-Version1.0 - a Python application for all-atom event-chain Monte Carlo
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DOI：
10.1016/j.cpc.2020.107168
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Philipp Hoellmer;Liang Qin;Michael F Faulkner;A. C. Maggs;W. Krauth
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文献类型：
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作者：
Philipp Hoellmer;Liang Qin;Michael F Faulkner;A. C. Maggs;W. Krauth
摘要我们提出了JeLLyFysh版本1。0，一个用于事件链蒙特卡罗（ECMC）的开源Python应用程序，ECMC是一种事件驱动的不可逆马尔可夫链蒙特卡罗算法，用于统计力学，生物物理学和电化学中的经典N体模拟。应用程序的体系结构反映了ECMC的数学公式。本地潜力，远程库仑相互作用和多体弯曲潜力，以及边界潜力和细胞系统，包括细胞否决算法。配置文件说明了许多相互作用的原子、偶极子和水分子的具体实现。程序摘要程序标题：JeLLyFysh-Version 1. 0程序文件doi：http：//dx. doi。org/10.17632/srrjt9493d。1许可证条款：GNU GPL v3编程语言：Python 3问题性质：事件链蒙特卡罗（ECMC）模拟，用于统计力学、生物物理学和电化学中的经典N体模拟。求解方法：事件驱动不可逆马尔可夫链蒙特卡罗算法。附加注释：该应用程序包含示例配置文件、文档字符串和单元测试。手稿附有一份冰冻的JeLLyFysh-Version 1副本。0，在GitHub上公开提供（存储库https：//github. com/juyyfysh/JeLLyFysh，commit hash d453d497256e7270e8babc8e04d20fb6d847dee4）.
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 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. 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 https://github. com/jellyfysh/JeLLyFysh, commit hash d453d497256e7270e8babc8e04d20fb6d847dee4).