Neural network correction for heats of formation with a larger experimental training set and new descriptors

Xue Mei Duan, Zhen Hua Li, Guo Liang Song, Wen Ning Wang, Guan Hua Chen, Kang Nian Fan

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

A neural-network-based approach was applied to correct the systematic deviations of the calculated heats of formation for 180 organic molecules and led to greatly improved calculation results compared to the first-principles methods [J. Chem. Phys. 119 (2003) 11501]. In this work, this neural network approach has been improved by using new descriptors obtained from natural bond orbital analysis and an enlarged training set including organic, inorganic molecules and radicals. After the neural network correction, the root-mean-square deviations for the enlarged set decreases from 11.2, 15.2, 327.1 to 4.4, 3.5, 9.5 kcal/mol for the B3LYP/6-31G(d), B3LYP/6-311G(2d,d,p) and HF/6-31G(d) methods, respectively.

Original languageEnglish (US)
Pages (from-to)125-130
Number of pages6
JournalChemical Physics Letters
Volume410
Issue number1-3
DOIs
StatePublished - Jul 10 2005

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China Grant Nos. (20273015 and 20433020) and the Natural Science Foundation of Shanghai Science & Technology Committee Grant No. (02DJ14023).

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