Artificial Neural Network-Group Contribution Method for Predicting Standard Enthalpy of Formation in the Solid State: C–H, C–H–O, C–H–N, and C–H–N–O Compounds

Autor:	Farid Bagui, Soufiane Guella, Ahmed Yahiaoui, Kadda Argoub, Ali Mustapha Benkouider, Rachid Kessas
Rok vydání:	2015
Předmět:	chemistry.chemical_classification Crystal Work (thermodynamics) Coefficient of determination Hydrocarbon chemistry chemistry.chemical_element Physical chemistry Condensed Matter Physics Organic compound Nitrogen Standard enthalpy of formation Group contribution method
Zdroj:	International Journal of Thermophysics. 36:2820-2832
ISSN:	1572-9567 0195-928X
Popis:	In this work, an artificial neural network-group contribution model is developed to predict the standard enthalpy of formation in the solid (crystal) state of pure compounds. Several classes of hydrocarbon compounds CH, oxygenated compounds CHO, nitrogen compounds CHN, and energetic compounds CHNO are investigated to propose a comprehensive and predictive model. The new model is developed and tested for 1222 organic compounds containing complex molecular structures. The performance of the new model has been compared with previous work and is shown to be far more accurate. The obtained results show an average absolute deviation of $$9.33 \,\hbox {kJ}{\cdot }\hbox {mol}^{-1}$$ and a coefficient of determination of 0.9972 for the experimental values.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::57f97d12c7c507d0d98c9ddf199b0ebb https://doi.org/10.1007/s10765-015-1928-x Zobrazit plný text záznamu Full text from SpringerLink