Analyzing Particle Systems for Machine Learning and Data Visualization with freud
Analyzing Particle Systems for Machine Learning and Data Visualization with freud
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DOI：
10.25080/majora-7ddc1dd1-004
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发表时间：
2019
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影响因子：
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通讯作者：
Bradley D Dice;Vyas Ramasubramani;Eric S. Harper;Matthew Spellings;Joshua A. Anderson;S. Glotzer
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文献类型：
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作者：
Bradley D Dice;Vyas Ramasubramani;Eric S. Harper;Matthew Spellings;Joshua A. Anderson;S. Glotzer
Freud Python库分析从分子动力学模拟输出的粒子数据。该库的设计及其各种高性能方法使其成为许多现代应用程序的强大工具。特别是，freud可以用作机器学习（ML）算法的数据生成管道的一部分，用于分析粒子模拟，它可以很容易地与各种模拟可视化工具集成，实现同步可视化和实时分析。在这里，我们提出了大量的例子，都使用弗洛伊德分析纳米尺度的粒子系统，通过耦合传统的模拟分析，机器学习库和可视化的每粒子的数量计算弗洛伊德分析方法。我们包括这种可视化的代码和示例，表明一般来说，将弗洛伊德引入现有的ML和可视化工作流程是平滑和非侵入性的。我们证明了在计算分子科学中使用的Python包中，弗洛伊德提供了一套独特的分析方法，具有高效的计算和无缝耦合到强大的数据分析管道中。
The freud Python library analyzes particle data output from molecular dynamics simulations. The library’s design and its variety of highperformance methods make it a powerful tool for many modern applications. In particular, freud can be used as part of the data generation pipeline for machine learning (ML) algorithms for analyzing particle simulations, and it can be easily integrated with various simulation visualization tools for simultaneous visualization and real-time analysis. Here, we present numerous examples both of using freud to analyze nano-scale particle systems by coupling traditional simulational analyses to machine learning libraries and of visualizing per-particle quantities calculated by freud analysis methods. We include code and examples of this visualization, showing that in general the introduction of freud into existing ML and visualization workflows is smooth and unintrusive. We demonstrate that among Python packages used in the computational molecular sciences, freud offers a unique set of analysis methods with efficient computations and seamless coupling into powerful data analysis pipelines.