Quantum machine learning for corrosion resistance in stainless steel
This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respe...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-09-01
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| Series: | Materials Today Quantum |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950257824000131 |
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| _version_ | 1850056682683498496 |
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| author | Muhamad Akrom Supriadi Rustad Totok Sutojo De Rosal Ignatius Moses Setiadi Hermawan Kresno Dipojono Ryo Maezono Moses Solomon |
| author_facet | Muhamad Akrom Supriadi Rustad Totok Sutojo De Rosal Ignatius Moses Setiadi Hermawan Kresno Dipojono Ryo Maezono Moses Solomon |
| author_sort | Muhamad Akrom |
| collection | DOAJ |
| description | This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability. |
| format | Article |
| id | doaj-art-17213d75dd0744b29b4bef04dd361e3c |
| institution | DOAJ |
| issn | 2950-2578 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials Today Quantum |
| spelling | doaj-art-17213d75dd0744b29b4bef04dd361e3c2025-08-20T02:51:39ZengElsevierMaterials Today Quantum2950-25782024-09-01310001310.1016/j.mtquan.2024.100013Quantum machine learning for corrosion resistance in stainless steelMuhamad Akrom0Supriadi Rustad1Totok Sutojo2De Rosal Ignatius Moses Setiadi3Hermawan Kresno Dipojono4Ryo Maezono5Moses Solomon6Research 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.Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaResearch Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaQuantum and Nano Technologies Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia; Corresponding authors.School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanDepartment of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, ChinaThis study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.http://www.sciencedirect.com/science/article/pii/S2950257824000131Quantum machine learningQSVCCorrosionStainless steelClassification |
| spellingShingle | Muhamad Akrom Supriadi Rustad Totok Sutojo De Rosal Ignatius Moses Setiadi Hermawan Kresno Dipojono Ryo Maezono Moses Solomon Quantum machine learning for corrosion resistance in stainless steel Materials Today Quantum Quantum machine learning QSVC Corrosion Stainless steel Classification |
| title | Quantum machine learning for corrosion resistance in stainless steel |
| title_full | Quantum machine learning for corrosion resistance in stainless steel |
| title_fullStr | Quantum machine learning for corrosion resistance in stainless steel |
| title_full_unstemmed | Quantum machine learning for corrosion resistance in stainless steel |
| title_short | Quantum machine learning for corrosion resistance in stainless steel |
| title_sort | quantum machine learning for corrosion resistance in stainless steel |
| topic | Quantum machine learning QSVC Corrosion Stainless steel Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2950257824000131 |
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