Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors
This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three ne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Results in Chemistry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221171562300365X |
| _version_ | 1797398621767860224 |
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| author | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono |
| author_facet | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono |
| author_sort | Muhamad Akrom |
| collection | DOAJ |
| description | This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three new amino acid compounds, namely L-asparagine (LA), L-isoleucine (LI), and L-proline (LP). The result is that the Gradient Boosting Regressor (GBR) model is proven to be the best predictive model based on the coefficient of determination (R2) and root mean square error (RMSE) metrics used. The study found that the three amino acid compounds LA, LI, and LP tested had high CIE values, ranging from 90.49% to 93.67%. These results are also relevant to the CIE values resulting from experimental studies and show a trend that is by the adsorption energy trend. This engineering breakthrough can be used to predict the corrosion inhibition properties of new compounds before experimental synthesis. |
| first_indexed | 2024-03-09T01:28:12Z |
| format | Article |
| id | doaj.art-59d3bf5f4e9e4966ad77319a23aa85ea |
| institution | Directory Open Access Journal |
| issn | 2211-7156 |
| language | English |
| last_indexed | 2024-03-09T01:28:12Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Chemistry |
| spelling | doaj.art-59d3bf5f4e9e4966ad77319a23aa85ea2023-12-10T06:15:20ZengElsevierResults in Chemistry2211-71562023-12-016101126Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitorsMuhamad Akrom0Supriadi Rustad1Hermawan Kresno Dipojono2Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Corresponding authors.Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia; Corresponding authors.Advanced Functional Materials Research Group, Institut Teknologi Bandung, Bandung 40132, Indonesia; Corresponding authors.This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three new amino acid compounds, namely L-asparagine (LA), L-isoleucine (LI), and L-proline (LP). The result is that the Gradient Boosting Regressor (GBR) model is proven to be the best predictive model based on the coefficient of determination (R2) and root mean square error (RMSE) metrics used. The study found that the three amino acid compounds LA, LI, and LP tested had high CIE values, ranging from 90.49% to 93.67%. These results are also relevant to the CIE values resulting from experimental studies and show a trend that is by the adsorption energy trend. This engineering breakthrough can be used to predict the corrosion inhibition properties of new compounds before experimental synthesis.http://www.sciencedirect.com/science/article/pii/S221171562300365XMachine learningQSPRCorrosion inhibitorAmino acid |
| spellingShingle | Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors Results in Chemistry Machine learning QSPR Corrosion inhibitor Amino acid |
| title | Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| title_full | Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| title_fullStr | Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| title_full_unstemmed | Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| title_short | Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| title_sort | machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors |
| topic | Machine learning QSPR Corrosion inhibitor Amino acid |
| url | http://www.sciencedirect.com/science/article/pii/S221171562300365X |
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