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Use of multivariate methods in the analysis of calculated reaction pathways
Author(s) -
Alsberg Bjørn K.,
Jensen Vidar R.,
Børve Knut J.
Publication year - 1996
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/(sici)1096-987x(19960730)17:10<1197::aid-jcc2>3.0.co;2-j
Subject(s) - principal component analysis , multivariate statistics , chemistry , electrophile , interpretability , computational chemistry , biological system , computer science , artificial intelligence , organic chemistry , machine learning , catalysis , biology
It is suggested that multivariate methods such as principal component analysis (PCA)are useful tools in the analysis of large data sets from quantum chemical computationsof reaction pathways. The potential of this methodology is investigated through an examination of the details of a medium‐sized reaction: the Ziegler–Natta ethyleneinsertion reaction. Furthermore, PCA is used to compare two reaction pathways for the electrophilic addition of hydrochloric acid to propene. In both instances, the reactionpathways are determined at the Hartree–Fock level using the intrinsic reaction coordinate approach. The analyses are carried out on various kinds of descriptors, including geometry parameters, Mulliken charges, and overlap populations, and their relative efficiencies in terms of PCA modeling of the reactions are assessed. The results show that it may be necessary to combine analyses based on different descriptors and to analyze subsections of the reaction path separately in order to obtain both high resolution and interpretability. © 1996 by John Wiley & Sons,Inc.