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The X 1 family of methods that combines B 3 LYP with neural network corrections for an accurate yet efficient prediction of thermochemistry
Author(s) -
Wu Jianming,
Zhou Yuwei,
Xu Xin
Publication year - 2015
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.24919
Subject(s) - thermochemistry , benchmarking , artificial neural network , dissociation (chemistry) , bond dissociation energy , computer science , standard enthalpy of formation , set (abstract data type) , chemistry , computational chemistry , machine learning , economics , management , programming language
B3LYP is currently the most widely used density functional approximation, while the X1 family of methods, namely X1, X1s, and X1se, is a set of neural network‐based methods that systematically correct the B3LYP errors. The performance of the X1 family of methods in the prediction of heats of formation (HOFs), bond dissociation enthalpies (BDEs), heats of isomerization (HOIs), and so forth, is summarized against some well‐established benchmarking datasets. X1 significantly eliminates the notorious size‐dependent errors of B3LYP in prediction of HOFs for larger molecules. X1s further exhibits a significant improvement for BDE calculations. X1se continues to improve its predecessors on HOIs. Such a progressive improvement relies on the increasingly comprehensive descriptors. Limitations of the present approaches and the direction for future improvements are discussed. © 2015 Wiley Periodicals, Inc.