Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives
文献类型：期刊论文
| 作者 | Ekeocha, Christopher Ikechukwu5,6; Uzochukwu, Ikechukwu Nelson6; Onyeachu, Ikenna Benedict4,6; Etim, Ini-Ibehe Nabuk2,3,6; Oguzie, Emeka Emmanuel1,6 |
| 刊名 | JOURNAL OF PHYSICAL CHEMISTRY A
 |
| 出版日期 | 2024-10-22 |
| 页码 | 25 |
| ISSN号 | 1089-5639 |
| DOI | 10.1021/acs.jpca.4c03671 |
| 通讯作者 | Oguzie, Emeka Emmanuel(emeka.oguzie@futo.edu.ng) |
| 英文摘要 | This study aimed to predict the selected antipyrine compounds' inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables. Using machine learning techniques such as K-nearest neighbor (KNN) and artificial neural network (ANN), a predictive model was built for 39 antipyrine compounds with known corrosion inhibition efficiencies for carbon and low alloy steel in hydrochloric acid solutions. The models' predictive capability was assessed using cross-validation, with the ANN model showing superior performance, achieving a coefficient of determination (R-2) value of 0.715 compared to 0.548 for the KNN model. Performance metrics such as the mean square error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) further confirmed the superiority of the ANN model over the KNN model. The corrosion inhibition efficiencies (CIEs) of the selected antipyrine compounds ranged from 68.78 to 99.79%, with compound A1 demonstrating the highest CIE of 99.79% and compound A3 the lowest, as evaluated by the ANN model. Analysis of Fukui index parameters obtained from the Mulliken population analysis suggested that the nucleophilic and electrophilic sites play a crucial role in the interactions between the inhibitor and the metal atom through electron donor-acceptor interactions. Moreover, the energy of adsorption (E-ads) in kcal |
| WOS关键词 | MILD-STEEL CORROSION ; BENZIMIDAZOLE DERIVATIVES ; PYRIDINE-DERIVATIVES ; ACIDIC MEDIUM ; SCHIFF-BASES ; CARBON-STEEL ; INHIBITION ; SURFACE ; QSAR ; COMPLEXES |
| 资助项目 | World Bank[6510-NG] ; Federal University of Technology, Owerri, Imo State, Nigeria |
| WOS研究方向 | Chemistry ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001338440600001 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/199516]  |
| 专题 | 海洋研究所_海洋腐蚀与防护研究发展中心 |
| 通讯作者 | Oguzie, Emeka Emmanuel |
| 作者单位 | 1.Fed Univ Technol Owerri, Fac Sci, Dept Chem, Owerri 1526, Imo State, Nigeria 2.Chinese Acad Sci, Inst Oceanol, Key Lab Adv Marine Mat, Key Lab Marine Environm Corros & Biofouling, Qingdao 266071, Peoples R China 3.Akwa Ibom State Univ, Dept Marine Sci, Marine Chem & Corros Res Grp, Mkpat Enin 53211, Nigeria 4.Wigwe Univ, Fac Sci & Comp, Dept Chem, Isiokpo 511101, Rivers State, Nigeria 5.Natl Math Ctr, Math Programme, Abuja 902101, Nigeria 6.Electrochem Syst Fed Univ Technol ACEFUELS FUTO, Africa Ctr Excellence Future Energies, Owerri 460114, Imo State, Nigeria |
| 推荐引用方式 GB/T 7714 | Ekeocha, Christopher Ikechukwu,Uzochukwu, Ikechukwu Nelson,Onyeachu, Ikenna Benedict,et al. Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives[J]. JOURNAL OF PHYSICAL CHEMISTRY A,2024:25. |
| APA | Ekeocha, Christopher Ikechukwu,Uzochukwu, Ikechukwu Nelson,Onyeachu, Ikenna Benedict,Etim, Ini-Ibehe Nabuk,&Oguzie, Emeka Emmanuel.(2024).Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives.JOURNAL OF PHYSICAL CHEMISTRY A,25. |
| MLA | Ekeocha, Christopher Ikechukwu,et al."Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives".JOURNAL OF PHYSICAL CHEMISTRY A (2024):25. |
入库方式： OAI收割
来源：海洋研究所
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