Kabiru Haruna , Sani I. Abba , Jamil Usman , A.G. Usman , Abdulrahman Musa , Tawfik A. Saleh , Isam H. Aljundi
{"title":"Machine learning insight into inhibition efficiency modelling based on modified graphene oxide of diaminohexane (DAH-GO) and diaminooctane (DAO-GO)","authors":"Kabiru Haruna , Sani I. Abba , Jamil Usman , A.G. Usman , Abdulrahman Musa , Tawfik A. Saleh , Isam H. Aljundi","doi":"10.1016/j.cartre.2024.100373","DOIUrl":null,"url":null,"abstract":"<div><p>The effective prediction of corrosion inhibition efficiency (%IE) of modified graphene oxides (GOs); diaminohexane-modified graphene oxide (DAH-GO) and diaminooctane-modified graphene oxide (DAO-GO) is vital for advanced material applications. This study employs a dual-modelling scheme to predict the %IE, for this purpose, four stand-alone machine learning (ML) models (Multivariate Regression (MVR), Gaussian Process Regression (GPR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Neural Network (NN)), and five simple averaging (SA) ensemble paradigms (MVR-SA, GPR-SA, ANFIS-SA, NN-SA, and Decision Tree-SA (DT-SA)). Feature selection processes were carried out to develop three distinct models, leading to a comprehensive comparative analysis. The results demonstrated that the non-linear stand-alone models (GPR, ANFIS, NN) significantly outperform the linear MVR model, with the M2 model configuration yielding the highest performance across all models. Remarkably, GPR-M2 achieved perfect model tuning with zero error rates, indicating its superior predictive capabilities. Ensemble techniques further improved performance, reflecting the experimental data's complexities in %IE modelling. The hierarchical order of performance in the training phase in the testing phase is DT-SA < MVR-SA < ANFIS-SA < NN-SA < GPR-SA. The GPR-SA ensemble emerged as the most accurate technique, substantially enhancing the predictive accuracy of the ensemble models by up to 67.73% in the training phase and 50.71% in the testing phase. These findings suggest the potential of GPR-SA in boosting the performance of ensemble approaches in material science applications. The study recommended a promising future for ML in the development and application of corrosion-inhibitors.</p></div>","PeriodicalId":52629,"journal":{"name":"Carbon Trends","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667056924000543/pdfft?md5=a20d8003d5b921de8be788888ef22dda&pid=1-s2.0-S2667056924000543-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carbon Trends","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667056924000543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The effective prediction of corrosion inhibition efficiency (%IE) of modified graphene oxides (GOs); diaminohexane-modified graphene oxide (DAH-GO) and diaminooctane-modified graphene oxide (DAO-GO) is vital for advanced material applications. This study employs a dual-modelling scheme to predict the %IE, for this purpose, four stand-alone machine learning (ML) models (Multivariate Regression (MVR), Gaussian Process Regression (GPR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Neural Network (NN)), and five simple averaging (SA) ensemble paradigms (MVR-SA, GPR-SA, ANFIS-SA, NN-SA, and Decision Tree-SA (DT-SA)). Feature selection processes were carried out to develop three distinct models, leading to a comprehensive comparative analysis. The results demonstrated that the non-linear stand-alone models (GPR, ANFIS, NN) significantly outperform the linear MVR model, with the M2 model configuration yielding the highest performance across all models. Remarkably, GPR-M2 achieved perfect model tuning with zero error rates, indicating its superior predictive capabilities. Ensemble techniques further improved performance, reflecting the experimental data's complexities in %IE modelling. The hierarchical order of performance in the training phase in the testing phase is DT-SA < MVR-SA < ANFIS-SA < NN-SA < GPR-SA. The GPR-SA ensemble emerged as the most accurate technique, substantially enhancing the predictive accuracy of the ensemble models by up to 67.73% in the training phase and 50.71% in the testing phase. These findings suggest the potential of GPR-SA in boosting the performance of ensemble approaches in material science applications. The study recommended a promising future for ML in the development and application of corrosion-inhibitors.