Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context
This work addresses the problem of real-time analysis for streaming PDF data in materials science, but it is incremental as it validates existing methods rather than introducing new ones.
The researchers validated matrix factorization techniques, specifically PCA and NMF, for automatically identifying components from atomic pair distribution function data in streaming contexts, applying them to simulated and experimental datasets from in situ experiments.
We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly developed software infrastructure for analyzing the PDF data arriving in streaming manner. We then apply two matrix factorization techniques, Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF), to study simulated and experiment datasets in the context of in situ experiment.