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

Zhongyu Wan; Quan‐De Wang; Jinhu Liang

doi:10.1002/qua.26441

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 ◽

Artificial Neural Network ◽

Quantum Chemistry ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Molecular Descriptors ◽

Accurate Prediction ◽

Standard Enthalpy Of Formation ◽

Artificial Neural

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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 ◽

Artificial Neural Network ◽

Quantum Chemistry ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Molecular Descriptors ◽

Accurate Prediction ◽

Standard Enthalpy Of Formation ◽

Artificial Neural

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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 ◽

Artificial Neural Network ◽

Ab Initio Calculations ◽

Ab Initio ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Standard Enthalpy Of Formation ◽

Low Level ◽

Fast Estimation ◽

Artificial Neural

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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

International Journal of Thermophysics ◽

10.1007/s10765-015-1928-x ◽

2015 ◽

Vol 36 (10-11) ◽

pp. 2820-2832 ◽

Cited By ~ 5

Author(s):

Soufiane Guella ◽

Kadda Argoub ◽

Ali Mustapha Benkouider ◽

Ahmed Yahiaoui ◽

Rachid Kessas ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solid State ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Group Contribution ◽

Standard Enthalpy Of Formation ◽

Group Contribution Method ◽

Artificial Neural

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ChemInform Abstract: Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network.

ChemInform ◽

10.1002/chin.200007234 ◽

2010 ◽

Vol 31 (7) ◽

pp. no-no

Author(s):

Giuseppina Gini ◽

Marco Lorenzini ◽

Emilio Benfenati ◽

Paola Grasso ◽

Maurizio Bruschi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aromatic Compounds ◽

Molecular Descriptors ◽

Artificial Neural

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Accurate Prediction via Artificial Neural Network of OSNR Penalty Induced by Non-uniform WSS Filtering

10.1364/oecc.2021.m4a.1 ◽

2021 ◽

Author(s):

Arthur Minakhmetov ◽

Thierry Zami ◽

Bruno Lavigne ◽

Amirhossein Ghazisaeidi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Accurate Prediction ◽

Artificial Neural

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Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network

Journal of Chemical Information and Computer Sciences ◽

10.1021/ci9903096 ◽

1999 ◽

Vol 39 (6) ◽

pp. 1076-1080 ◽

Cited By ~ 55

Author(s):

Giuseppina Gini ◽

Marco Lorenzini ◽

Emilio Benfenati ◽

Paola Grasso ◽

Maurizio Bruschi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Aromatic Compounds ◽

Molecular Descriptors ◽

Artificial Neural

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Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

Energy Conversion and Management ◽

10.1016/j.enconman.2010.08.018 ◽

2011 ◽

Vol 52 (2) ◽

pp. 911-916 ◽

Cited By ~ 26

Author(s):

Bahman ZareNezhad ◽

Ali Aminian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Accurate Prediction ◽

Combustion Gases ◽

Artificial Neural

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Artificial Neural Network for accurate prediction of post-dialysis urea rebound

4th International Workshop on Soft Computing Applications ◽

10.1109/sofa.2010.5565606 ◽

2010 ◽

Cited By ~ 8

Author(s):

Ahmad. T. Azar ◽

Valentina E. Balas ◽

Teodora Olariu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Accurate Prediction ◽

Artificial Neural

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"Artificial neural network designed to identify NBOMe hallucinogens based on molecular descriptors "

Annals of the ”Dunarea de Jos” University of Galati. Fascicle II, Mathematics, Physics, Theoretical Mechanics ◽

10.35219/ann-ugal-math-phys-mec.2018.1.05 ◽

2018 ◽

Vol 41 (1) ◽

pp. 33-39

Author(s):

Steluța Gosev ◽

◽

Adelina Ion ◽

Mirela Praisler ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Descriptors ◽

Artificial Neural

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Modelling of Cytotoxicity Data (CC50) of Anti-HIV 1-[5-Chlorophenyl) Sulfonyl]-1H-Pyrrole Derivatives Using Calculated Molecular Descriptors and Levenberg-Marquardt Artificial Neural Network

Chemical Biology & Drug Design ◽

10.1111/j.1747-0285.2009.00790.x ◽

2009 ◽

Vol 73 (4) ◽

pp. 456-465 ◽

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Author(s):

M. Arab Chamjangali

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Molecular Descriptors ◽

Pyrrole Derivatives ◽

Levenberg Marquardt ◽

Artificial Neural ◽

Anti Hiv ◽

Hiv 1

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