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Validation of non‐negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data
Author(s) -
Liu Chia-Hao,
Wright Christopher J.,
Gu Ran,
Bandi Sasaank,
Wustrow Allison,
Todd Paul K.,
O'Nolan Daniel,
Beauvais Michelle L.,
Neilson James R.,
Chupas Peter J.,
Chapman Karena W.,
Billinge Simon J. L.
Publication year - 2021
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s160057672100265x
Subject(s) - non negative matrix factorization , context (archaeology) , principal component analysis , factorization , matrix decomposition , matrix (chemical analysis) , function (biology) , beamline , computer science , algorithm , beam (structure) , chemistry , physics , artificial intelligence , optics , paleontology , eigenvalues and eigenvectors , quantum mechanics , chromatography , evolutionary biology , biology
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.