Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks - Najam Us Sahar Riyaz (22927843) (2025)
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 R2 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 R2 = 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. Other Information Published in: Arabian Journal for Science and Engineering License: https://creativecommons.org/licenses/by/4.0 See article on publisher's website: https://dx.doi.org/10.1007/s13369-025-10386-5