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A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds

Authors

  • Noor Ageng Setiyanto Universitas Dian Nuswantoro
  • Harun Al Azies Universitas Dian Nuswantoro
  • Usman Sudibyo Universitas Dian Nuswantoro
  • Ayu Pertiwi Universitas Dian Nuswantoro
  • Setyo Budi Universitas Dian Nuswantoro
  • Muhamad Akrom Universitas Dian Nuswantoro

DOI:

https://doi.org/10.62411/jimat.v1i1.10429

Abstract

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.

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2024-04-29

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