用于高通量数据注释的可扩展多组分光谱分析
Scalable multicomponent spectral analysis for high-throughput data annotation
摘要：
在物质和化学成分的高通量定量分析中，大规模的多组分光谱的参数拟合是一项重要但未得到充分重视的任务。为了自动化的光谱和衍射数据的注释过程中收集的计数数百到数千，我们提出了一个系统的方法，兼容高性能的计算基础设施，使用MapReduce模型和基于任务的并行化。我们在软件中实现的方法，并证明线性计算缩放相对于光谱成分使用多维实验材料表征数据集从光电子能谱和粉末电子衍射作为基准。我们的方法可以有效地生成高质量的数据注释和在线光谱分析，并适用于各种分析技术在材料科学和化学作为一个模块的闭环实验系统。
英文摘要：
Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. To automate the annotation process for spectroscopic and diffraction data collected in counts of hundreds to thousands, we present a systematic approach compatible with high-performance computing infrastructures using the MapReduce model and task-based parallelization. We implement the approach in software and demonstrate linear computational scaling with respect to spectral components using multidimensional experimental materials characterization datasets from photoemission spectroscopy and powder electron diffraction as benchmarks. Our approach enables efficient generation of high-quality data annotation and online spectral analysis and is applicable to a variety of analytical techniques in materials science and chemistry as a building block for closed-loop experimental systems.
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