The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Finally, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

中文翻译：

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验证非负矩阵分解以快速评估大量原子对分布函数数据

非负矩阵分解 (NMF) 技术的使用已得到验证，可从时间序列数据（如原位）中的原子对分布函数 (PDF) 数据中自动提取物理相关分量实验。在同步加速器束中发生的化学合成反应的背景下，对 PDF 数据使用两种矩阵分解技术（主成分分析和 NMF）进行了比较，将该方法应用于已知正确成分并进行测量的合成数据来自先前发布的实验数据的 PDF。NMF 方法产生的数学分量非常接近系统的化学成分的 PDF，并且权重的时间演化与基本事实非常接近。最后，讨论了如果随着实验的进行在光束线上进行分析，这将如何出现在流上下文中。