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

Solid State ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Group Contribution ◽

Prediction of standard enthalpy of formation in the solid state by a third-order group contribution method

Fluid Phase Equilibria ◽

10.1016/j.fluid.2014.08.003 ◽

2014 ◽

Vol 380 ◽

pp. 121-127 ◽

Cited By ~ 14

Author(s):

Kadda Argoub ◽

Ali Mustapha Benkouider ◽

Ahmed Yahiaoui ◽

Rachid Kessas ◽

Sofiane Guella ◽

...

Keyword(s):

Solid State ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Group Contribution ◽

Third Order ◽

Order Group

Fast estimation of standard enthalpy of formation with chemical accuracy by artificial neural network correction of low-level-of-theory ab initio calculations

Chemical Engineering Journal ◽

10.1016/j.cej.2021.131304 ◽

2021 ◽

pp. 131304

Author(s):

Pieter P. Plehiers ◽

Istvan Lengyel ◽

David H. West ◽

Guy B. Marin ◽

Christian V. Stevens ◽

...

Keyword(s):

Neural Network ◽

Ab Initio Calculations ◽

Ab Initio ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Low Level ◽

Fast Estimation ◽

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors

10.22541/au.159135487.70823246 ◽

2020 ◽

Author(s):

zhongyu Wan ◽

Quan de Wang ◽

Jinhu Liang

Keyword(s):

Neural Network ◽

Quantum Chemistry ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Molecular Descriptors ◽

Accurate Prediction ◽

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors

International Journal of Quantum Chemistry ◽

10.1002/qua.26441 ◽

2020 ◽

Vol 121 (2) ◽

Author(s):

Zhongyu Wan ◽

Quan‐De Wang ◽

Jinhu Liang

Keyword(s):

Neural Network ◽

Quantum Chemistry ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Molecular Descriptors ◽

Accurate Prediction ◽

Determination of Parachor of Various Compounds Using an Artificial Neural Network−Group Contribution Method

Industrial & Engineering Chemistry Research ◽

10.1021/ie102464t ◽

2011 ◽

Vol 50 (9) ◽

pp. 5815-5823 ◽

Cited By ~ 31

Author(s):

Farhad Gharagheizi ◽

Ali Eslamimanesh ◽

Amir H. Mohammadi ◽

Dominique Richon

Keyword(s):

Neural Network ◽

Group Contribution ◽

Estimation of Pure Component Parameters of PC-SAFT EoS by an Artificial Neural Network Based on a Group Contribution Method

Fluid Phase Equilibria ◽

10.1016/j.fluid.2021.113179 ◽

2021 ◽

pp. 113179

Author(s):

Hiroaki Matsukawa ◽

Masayuki Kitahara ◽

Katsuto Otake

Keyword(s):

Neural Network ◽

Pure Component ◽

Group Contribution ◽

Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.7b02753 ◽

2017 ◽

Vol 56 (42) ◽

pp. 12236-12245 ◽

Cited By ~ 16

Author(s):

William L. Kubic ◽

Rhodri W. Jenkins ◽

Cameron M. Moore ◽

Troy A. Semelsberger ◽

Andrew D. Sutton

Keyword(s):

Neural Network ◽

Organic Compounds ◽

Group Contribution ◽

Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network−Group Contribution Method

Journal of Chemical & Engineering Data ◽

10.1021/je101061t ◽

2011 ◽

Vol 56 (4) ◽

pp. 720-726 ◽

Cited By ~ 41

Author(s):

Farhad Gharagheizi ◽

Ali Eslamimanesh ◽

Amir H. Mohammadi ◽

Dominique Richon

Keyword(s):

Neural Network ◽

Group Contribution ◽

Pure Compounds ◽

Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition Temperature

Industrial & Engineering Chemistry Research ◽

10.1021/ie0300373 ◽

2003 ◽

Vol 42 (22) ◽

pp. 5708-5714 ◽

Cited By ~ 35

Author(s):

Tareq A. Albahri ◽

Reena S. George

Keyword(s):

Neural Network ◽

Ignition Temperature ◽

Group Contribution ◽

Structural Group ◽

Auto Ignition ◽

Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method

International Journal of Refrigeration ◽

10.1016/j.ijrefrig.2014.09.007 ◽

2014 ◽

Vol 48 ◽

pp. 188-200 ◽

Cited By ~ 9

Author(s):

Majid Moosavi ◽

Elaheh Sedghamiz ◽

Maryam Abareshi

Keyword(s):

Neural Network ◽

Group Contribution ◽

Liquid Density ◽

Density Prediction ◽