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The X1s Method for Accurate Bond Dissociation Energies
Author(s) -
Wu Jianming,
Ying Zhang Igor,
Xu Xin
Publication year - 2010
Publication title -
chemphyschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 140
eISSN - 1439-7641
pISSN - 1439-4235
DOI - 10.1002/cphc.201000273
Subject(s) - thermochemistry , bond dissociation energy , dissociation (chemistry) , absolute deviation , chemistry , molecule , computational chemistry , work (physics) , bond energy , bond length , artificial neural network , thermodynamics , computer science , mathematics , physics , machine learning , statistics , organic chemistry
Previously, we have put forward the X1 method that combines B3LYP with neural network correction for an accurate yet efficient prediction of thermochemistry. Without paying additional computational cost, X1 reduces B3LYP’s mean absolute deviation (MAD) for a set of 92 bond dissociation energies (BDEs) from 5.5 to 2.4 kcal mol −1 . In this work, we extend X1 and propose the X1s method by including the spin change from molecules to atoms during atomization as an additional descriptor. X1s further reduces the MAD for BDEs to 1.4 kcal mol −1 , thus showing substantial improvement.