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\begin{document}

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



\author[1]{zhongyu Wan}%
\author[2]{Quan-de Wang}%
\author[3]{Jinhu Liang}%
\affil[1]{Xuzhou Institute of Technology}%
\affil[2]{China University of Mining and Technology}%
\affil[3]{North University of China}%


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  \date{May 07, 2024}


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\selectlanguage{english}
\begin{abstract}
This work investigates possible improvements in the accuracy of
semiempirical quantum chemistry (SQC) methods for the prediction of
standard enthalpy of formation (\selectlanguage{greek}Δ\selectlanguage{english}\_f H\^{}o) through the use of
artificial neural network (ANN) with molecular descriptors. A total
number of 142 organic compounds with enough structural diversity has
been considered in the training set. Standard enthalpy of formation for
the selected compounds at the semiempirical PM3 and PM6 quantum
chemistry methods is collected from literature, and is calculated by
using semiempirical PM7 method in this work. The multiple stepwise
regression is first employed to screen effective molecular descriptors,
which are highly correlated with the error terms of the standard
enthalpy of formation compared with experimental values. The obtained 7
effective molecular descriptors are then used as input set to establish
three 7-11-1 neural network-based correction models to improve the
accuracy of SQC methods. By using the developed correction models, the
mean absolute errors (MAE) for \selectlanguage{greek}Δ\selectlanguage{english}\_f H\^{}oof PM3, PM6, and PM7 methods
are reduced from 22.36, 18.60, 17.27to 9.86, 9.83, 8.95, respectively in
kJ/mol. Meanwhile, the results of the test set show that the neural
network does not have the problem of over-fitting. Detailed analysis of
the 7 effective molecular descriptors indicates that the major source to
the correction models is from the electron withdrawing effect. The
developed ANN models for the three selected SQC methods provide an
efficient method for the quick and accurate prediction of thermodynamic
properties.%
\end{abstract}%



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\textbf{Hosted file}

\verb`manuscript_IJQC.docx`  available at \url{https://authorea.com/users/330249/articles/457092-accurate-prediction-of-standard-enthalpy-of-formation-based-on-semiempirical-quantum-chemistry-methods-with-artificial-neural-network-and-molecular-descriptors}

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