Precise Prediction on the Corrosion Prevention Ability of 1,2,4-
<b>Title:</b> Precise Prediction on the Corrosion Prevention Ability of 1,2,4-Triazole Derivatives: An Artificial Neural Network Approach<br><b>Overview:</b><br>This record contains the computational dataset, quantitative structure-activity relationship (QSAR) parameters, and artificial neural network (ANN) predictive model metrics evaluating the corrosion inhibition efficiency of 1,2,4-triazole derivatives. Industrial corrosion causes severe environmental contamination and equipment degradation, driving the need for sustainable material protection strategies. This work models complex non-linear relationships between quantum chemical parameters and experimental inhibition efficiency (IE<sub>exp</sub>) to provide a rapid, cost-effective screening framework for organic corrosion inhibitors.<br><b>Key Predictive Findings:</b> <b>Model Superiority:</b> The proposed ANN framework significantly outperforms traditional non-linear models in predicting inhibitor performance. <b>Correlation Coefficient (R):</b> Increased from <b>0.888</b> (conventional non-linear) to <b>0.967</b> (ANN). <b>Error Reduction:</b> Mean Square Error (MSE) was reduced from <b>4.33 × 10<sup>-4</sup></b> down to <b>9.81 × 10<sup>-5</sup></b>.<br><b>Data & Methodology Details:</b><br>The dataset correlates molecular descriptors (HOMO/LUMO energy levels, quantum chemical descriptors, electron transfer indices) of 1,2,4-triazole derivatives with experimentally measured inhibition efficiencies. The ANN configuration eliminates trial-and-error experimental cycles, serving as a scalable machine learning tool for corrosion mitigation in pickling and industrial environments.<br><b>Associated Publication:</b><br>If you utilize or build upon this dataset or computational model, please cite the published manuscript:Jalgham, R. T. T., Ouchenane, S., Dagdag, O., Ghodbane, H., & Das, H. S. <i>Precise Prediction on the Corrosion Prevention Ability of 1,2,4-Triazole Derivatives: An Artificial Neural Network Approach</i>. <b>ES Energy & Environment</b>.<br>DOI: https://dx.doi.org/10.30919/esee1344