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SMILES-based machine learning enables the prediction of corrosion inhibition capacity

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Abstract

This study explores the efficacy of using a simplified molecular input line entry system (SMILES) as the sole feature, replacing quantum chemical properties (QCP), in predicting corrosion inhibition efficiency (CIE) for N-heterocyclic compounds. The gradient boosting regressor (GBR) model outperforms k-nearest neighbors (KNN), support vector regression (SVR), and other models. SMILES accurately predicts CIE for various datasets, demonstrating potential as a standalone feature. Results indicate a moderate correlation between SMILES representation and corrosion inhibition properties. The proposed method identifies novel N-heterocyclic derivatives with high CIE, suggesting its utility in discovering corrosion inhibitors.

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Acknowledgments

All calculations were performed using the Computation Facility at the Research Center for Materials Informatics, Universitas Dian Nuswantoro, Indonesia.

Funding

Not Applicable.

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Authors and Affiliations

  1. Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia

    Muhamad Akrom

  2. Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia

    Muhamad Akrom & Supriadi Rustad

  3. Quantum and Nano Technologies Research Group, Institut Teknologi Bandung, Bandung, 40132, Indonesia

    Hermawan Kresno Dipojono

Authors
  1. Muhamad Akrom
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  2. Supriadi Rustad
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  3. Hermawan Kresno Dipojono
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Contributions

MA: Writing—original draft, Data collection & construction, Conceptualization, Methodology, Investigation, and Analysis; SR: Conceptualization, Review, and Supervision; HKD: Conceptualization, Review, and Supervision.

Corresponding authors

Correspondence to Muhamad Akrom, Supriadi Rustad or Hermawan Kresno Dipojono.

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The authors declare no competing financial interests or personal relationships that may have influenced the results reported in this paper.

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Akrom, M., Rustad, S. & Dipojono, H.K. SMILES-based machine learning enables the prediction of corrosion inhibition capacity. MRS Communications (2024). https://doi.org/10.1557/s43579-024-00551-6

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