Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
<p dir="ltr">Machine learning (ML) frameworks are transforming the development of corrosion inhibitors by enabling quantitative prediction of inhibition efficiency before synthesis. This work identifies the most reliable machine learning (ML) strategies for forecasting corrosion inhi...
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513531499511808 |
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| author | Najam Us Sahar Riyaz (22927843) |
| author2 | Mazen Khaled (2979294) Ali Alshami (18358488) Ibnelwaleed A. Hussein (5535953) |
| author2_role | author author author |
| author_facet | Najam Us Sahar Riyaz (22927843) Mazen Khaled (2979294) Ali Alshami (18358488) Ibnelwaleed A. Hussein (5535953) |
| author_role | author |
| dc.creator.none.fl_str_mv | Najam Us Sahar Riyaz (22927843) Mazen Khaled (2979294) Ali Alshami (18358488) Ibnelwaleed A. Hussein (5535953) |
| dc.date.none.fl_str_mv | 2025-07-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s13369-025-10386-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning-Driven_Prediction_of_Corrosion_Inhibitor_Efficiency_Emerging_Algorithms_Challenges_and_Future_Outlooks/30971074 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Materials engineering Machine learning Corrosion inhibitor Predictive modeling QSAR modeling Feature selection Artificial intelligence |
| dc.title.none.fl_str_mv | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Machine learning (ML) frameworks are transforming the development of corrosion inhibitors by enabling quantitative prediction of inhibition efficiency before synthesis. This work identifies the most reliable machine learning (ML) strategies for forecasting corrosion inhibitor efficiency before synthesis, thereby shortening development cycles and reducing experimental cost. Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. Cohesive data collections exceeding four hundred molecules under standardized test conditions deliver coefficients of determination above 0.90 and root-mean-square errors below 0.05. In contrast, fragmented datasets suffer from overfitting with R<sup>2</sup> often under 0.70. Graph neural networks lower prediction error by up to thirty percent relative to descriptor-driven QSAR models for structurally diverse inhibitors. However, their accuracy diminishes for large, flexible molecules unless explicit three-dimensional information is provided. Ensemble schemes such as Gaussian process regression with simple averaging and gradient boosting regressors fortified by permutation feature importance improve robustness in noisy or multi-alloy environments. At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. Persistent obstacles include limited public databases, inconsistent experimental protocols, and the opaque nature of deep learners. Researchers, engineers, and material scientists will gain valuable insights into optimizing ML-driven corrosion predictions, guiding future experimental studies.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Arabian Journal for Science and Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s13369-025-10386-5" target="_blank">https://dx.doi.org/10.1007/s13369-025-10386-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fe89c76921640589c4f3681134045ecb |
| identifier_str_mv | 10.1007/s13369-025-10386-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30971074 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future OutlooksNajam Us Sahar Riyaz (22927843)Mazen Khaled (2979294)Ali Alshami (18358488)Ibnelwaleed A. Hussein (5535953)EngineeringChemical engineeringMaterials engineeringMachine learningCorrosion inhibitorPredictive modelingQSAR modelingFeature selectionArtificial intelligence<p dir="ltr">Machine learning (ML) frameworks are transforming the development of corrosion inhibitors by enabling quantitative prediction of inhibition efficiency before synthesis. This work identifies the most reliable machine learning (ML) strategies for forecasting corrosion inhibitor efficiency before synthesis, thereby shortening development cycles and reducing experimental cost. Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. Cohesive data collections exceeding four hundred molecules under standardized test conditions deliver coefficients of determination above 0.90 and root-mean-square errors below 0.05. In contrast, fragmented datasets suffer from overfitting with R<sup>2</sup> often under 0.70. Graph neural networks lower prediction error by up to thirty percent relative to descriptor-driven QSAR models for structurally diverse inhibitors. However, their accuracy diminishes for large, flexible molecules unless explicit three-dimensional information is provided. Ensemble schemes such as Gaussian process regression with simple averaging and gradient boosting regressors fortified by permutation feature importance improve robustness in noisy or multi-alloy environments. At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. Persistent obstacles include limited public databases, inconsistent experimental protocols, and the opaque nature of deep learners. Researchers, engineers, and material scientists will gain valuable insights into optimizing ML-driven corrosion predictions, guiding future experimental studies.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Arabian Journal for Science and Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s13369-025-10386-5" target="_blank">https://dx.doi.org/10.1007/s13369-025-10386-5</a></p>2025-07-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s13369-025-10386-5https://figshare.com/articles/journal_contribution/Machine_Learning-Driven_Prediction_of_Corrosion_Inhibitor_Efficiency_Emerging_Algorithms_Challenges_and_Future_Outlooks/30971074CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309710742025-07-01T00:00:00Z |
| spellingShingle | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks Najam Us Sahar Riyaz (22927843) Engineering Chemical engineering Materials engineering Machine learning Corrosion inhibitor Predictive modeling QSAR modeling Feature selection Artificial intelligence |
| status_str | publishedVersion |
| title | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| title_full | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| title_fullStr | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| title_full_unstemmed | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| title_short | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| title_sort | Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks |
| topic | Engineering Chemical engineering Materials engineering Machine learning Corrosion inhibitor Predictive modeling QSAR modeling Feature selection Artificial intelligence |