A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies

A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neur...

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Bibliographic Details
Main Authors: Hong Zhi Li, Li Hong Hu, Wei Tao, Ting Gao, Hui Li, Ying Hua Lu, Zhong Min Su
Format: Article
Language:English
Published: MDPI AG 2012-06-01
Series:International Journal of Molecular Sciences
Subjects:
Y-NO bond
homolysis<a href="app:ds:homolysis"></a> bond dissociation energies
density functional theory
self-organizing feature mapping neural network
radial basis function neural network
Online Access:http://www.mdpi.com/1422-0067/13/7/8051

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