Journal of Multiscale Materials Informatics
Vol. 1 No. 1 (2024): 2024: April

Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds

Aprilyani Nur Safitri (Universitas Dian Nuswantoro)
Gustina Alfa Trisnapradika (Universitas Dian Nuswantoro)
Achmad Wahid Kurniawan (Universitas Dian Nuswantoro)
Wahyu AJi Eko Prabowo (Universitas Dian Nuswantoro)
Muhamad Akrom (Universitas Dian Nuswantoro)

Article Info

Publish Date
25 Jun 2024

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.

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Journal Info
Journal of Multiscale Materials Informatics
Website

Abbrev

jimat

Publisher

Universitas Dian Nuswantoro

Subject

Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Industrial & Manufacturing Engineering Materials Science & Nanotechnology

Description

Journal of Multiscale Materials Informatics (JIMAT) is a peer-reviewed, open-access, free of APC (until December 2025), and published 2 times (April and October) in one year. JIMAT is an interdisciplinary journal emphasis on cutting-edge research situated at the intersection of materials science and ...