<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chafai Nadjib , Salhi Hicham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of new models to predict the corrosion inhibition efficiency as functions of some molecular descriptors using statistical analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Indian Chemical Society </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jics.2023.101073</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">100</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">62 models have been proposed to predict the corrosion inhibition efficiency for a wide variety of organic and inorganic compounds using the statistical analysis. In this context, we used the experimental data of the corrosion inhibition efficiencies of 100 inhibitors (organic and inorganic) as an input data for a statistical analysis in order to determine the most adequate quantitative structure activity relationship (QSAR) models to predict the inhibition efficiency as function of some molecular descriptors. The experimental data used in this study are selected for the corrosion of steel in 0.5&amp;nbsp;M&amp;nbsp;H2SO4&amp;nbsp;at 25&amp;nbsp;°C. Also, we choose the inhibition efficiencies determined by potentiodynamic polarization curves (&lt;em&gt;IE&lt;/em&gt;p) and electrochemical impedance spectroscopy (&lt;em&gt;IE&lt;/em&gt;EIS). Accordingly, the statistical analysis has been applied to collect the&amp;nbsp;&lt;em&gt;IE&lt;/em&gt;p&amp;nbsp;and&amp;nbsp;&lt;em&gt;IE&lt;/em&gt;EIS&amp;nbsp;models of corrosion inhibitors. The different models are compared to assess the performance of the best model. Global Performance Indicator (GPI) is computed to evaluate all models. The results show that the cubical model with a GPI of 1.27 is the best (M23) for&amp;nbsp;&lt;em&gt;IE&lt;/em&gt;EIS, and the linear model with a GPI of 0.64 for&amp;nbsp;&lt;em&gt;IE&lt;/em&gt;p.</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record></records></xml>