Purpose: Experimental studies are usually costly, time-consuming, and resource-intensive when it comes to investigating prospective corrosion inhibitor compounds. Machine learning (ML) based on the quantitative structure-property relationship model (QSPR) has become a massive method for testing the effectiveness of chemical compounds as corrosion inhibitors. The main challenge in the ML method is to design a model that produces high prediction accuracy so that the properties of a material can be predicted accurately. In this study, we examine the performance of polynomial functions in the ML-based NuSVR algorithm in evaluating the regression dataset of corrosion inhibition efficiency of pyridine-quinoline compounds.Methods: Polynomial functions for NuSVR algorithm-based ML.Result: The outcomes demonstrate that the NuSVR model's prediction ability is greatly enhanced by the application of polynomial functions. Originality: The combination of polynomial functions and deep machine learning based NuSVR algorithms to increase the accuracy of predictive models.
In this work, we developed a QSAR model using the K-Nearest Neighbor (KNN) algorithm to predict the corrosion inhibition performance of the inhibitor compound. To overcome the small dataset problems, virtual samples are generated and added to the training set using a Virtual Sample Generation (VSG) method. The generalizability of the proposed KNN + VSG model is verified by using six small datasets from references and comparing their prediction performances. The research shows that for the six datasets, the proposed model is able to make predictions with the best accuracy. Adding virtual samples to the training data helps the algorithm recognize feature-target relationship patterns, and therefore increases the number of chemical quantum parameters correlated with corrosion inhibition efficiency. This proposed method strengthens the prospect of ML for developing material designs, especially in the case of small datasets.
Since corrosion causes considerable losses in many fields, including the economy, environment, society, industry, security, and safety, it is a major concern for the industrial and academic sectors. Damage control of materials based on organic compounds is currently a field of great interest. Because it is non-toxic, affordable, and effective in a variety of corrosive situations, pyrimidine has potential as a corrosion inhibitor. It takes a lot of time and resources to carry out experimental investigations in the exploration of potential corrosion inhibitor candidates. In this study, we evaluate the gradient boosting regressor (GBR), support vector regression (SVR), and k-nearest neighbor (KNN) algorithms as predictive models for corrosion inhibition efficiency using a machine learning (ML) approach based on the quantitative structure-property relationship model (QSPR). Based on the metric coefficient of determination (R2) and root mean square error (RMSE), we found that the GBR model had the best predictive performance compared to the SVR and KNN models as well as models from the literature for pyrimidine compound datasets. Overall, our study offers a new perspective on the ability of ML models to predict corrosion inhibition of iron surfaces
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