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Performance of group additivity methods for predicting the stability of uranyl complexes
Author(s) -
Petrus Enric,
Bo Carles
Publication year - 2020
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/jcc.26157
Subject(s) - uranyl , additive function , density functional theory , stability (learning theory) , group (periodic table) , connection (principal bundle) , computational chemistry , chemistry , chemical stability , population , set (abstract data type) , functional group , mathematics , thermodynamics , computer science , physics , organic chemistry , mathematical analysis , machine learning , sociology , programming language , polymer , ion , geometry , demography
Herein, we investigated the viability of two group additivity methods for predicting Gibbs energies of a set of uranyl complexes. In first place, we proved that both density functional theory (DFT)‐based methods and Serezhkin's stereoatomic model provide equivalent answers in terms of stability. Moreover, we proposed a novel methodology based on Mayer's population analysis for estimating Serezhkin's empirical parameters theoretically. On the other hand, we showed that Cheong and Persson linear algebra methodology can be successfully applied to uranyl complexes, and analyzed its performance in connection with the chemical nature of the compounds employed in the model.