[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125114-en":3,"doc-seo-125114-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125114,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds - Research Report","Corrosion presents persistent risks to industrial operations and research activities, producing negative effects across economics, environment, societal welfare, and safety. Corrosion inhibitors offer an effective means to slow degradation, while experimental screening of candidates remains costly in time and resources. This study applies machine learning with a QSPR approach to predict the inhibitory efficiency of quinoxaline compounds, comparing AdaBoost, Gradient Boosting, and Extreme Gradient Boosting regressors. Hyperparameter tuning via grid search yields the best-performing XGBR model, achieving R² of 0.970 with low error metrics and supporting more efficient identification of inhibitor candidates.","Utilization of Machine Learning for Predicting Corrosion Inhibition by  \nQuinoxaline Compounds  \nMuhamad Fadil 1*, Muhamad Akrom 2**, Wise Herowati 3**  \n* Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang, Indonesia  \n** Research Center for Quantum Computing and Materials Informatics, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang, Indonesia  \n[111202012733@mhs.dinus.ac.id](111202012733@mhs.dinus.ac.id1)[1](111202012733@mhs.dinus.ac.id1), [m.akrom@dsn.dinus.ac.id](m.akrom@dsn.dinus.ac.id2)[2](m.akrom@dsn.dinus.ac.id2), [wise@dsn.dinus.ac.id](wise@dsn.dinus.ac.id3)[3](wise@dsn.dinus.ac.id3)  \n\n| Article history:\u003Cbr>Received 2024-11-14 Revised 2024-11-25 Accepted 2024-12-04 | Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.\u003Cbr>\u003Cbr>This is an open access article under the CC–BY-SA license. |\n| --- | --- |\n| Keyword:\u003Cbr>Corrosion, Inhibitor; Machine Learning, Quinoxaline. |  |\n\nArticle Info ABSTRACT  \nI. PENDAHULUAN  \nKorosi merupakan masalah serius bagi sektor industri dan akademik karena mempunyai dampak negatif yang signifikanpada banyak bidang seperti ekonomi, lingkungan, masyarakat, industri, dan keselamatan [1],[2],[3] . Salah satu cara untuk menghambat laju korosi adalah dengan menggunakan bahan penghambat korosi (inhibitor) [4]. Inhibitor adalah senyawa kimia yang ketika ditambahkandalam jumlah kecil ke lingkungan korosif (elektrolit), dapat menghambat proses korosi [5] . Studi eksperimental untuk mengevaluasi banyak kandidat bahan kimia sebagai penghambat korosi memerlukan investasi uang, waktu, dansumber daya yang signifikan [6], [7], [8]. Belakangan ini, metode Machine Learning (ML) berbasis Quantitative Structure-Properties Relationship (QSPR) menjadipendekatan yang efektif dan efisien dalam pengembanganserta pencarian kandidat inhibitor korosi. [9], [10]. Karenasifat elektronik dan reaktivitas kimiawi suatu senyawa dapat  \ndikaitkan secara kuantitatif dengan struktur kimianya, metode QSPR berbasis ML ini dapat digunakan lebih lanjut untuk menyelidiki berbagai kandidat senyawa inhibitor [11], [12],[13] .  \nQuadri et al. melakukan penelitian menggunakan model multilinear regression (MLR) dan model artificial neural network (ANN) untuk memprediksi efisiensi dataset senyawaquinoxaline sebagai inhibitor korosi [14]. penelitian tersebut menghasilkan bahwa model ANN memiliki keunggulandalam memprediksi dibandingkan dengan model MLR. Dengan model ANN menunjukkan hasil evaluasi root mean squared error (RMSE), mean square error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) dengan nilai masing 5.416, 29.333, 2.381, 5.038. Namun, dengan hasil tersebut tingkat keakuratan model tersebut masih rendah dan masih bisa ditingkatkan lagi. Karena senyawa yang digunakan dalam penelitian tersebut sama, kami berhipotesis ","cbCail4t9zsLEAMP","https://ap.wps.com/l/cbCail4t9zsLEAMP","pdf",290333,1,5,"English","en",105,"# Introduction\n## Background and problem statement\n## Machine learning and QSPR rationale\n# Methods\n## Model development workflow\n## Dataset and preprocessing\n## K-Fold cross validation\n## Algorithms and hyperparameter tuning\n# Results and evaluation","[{\"question\":\"Why are corrosion inhibitors important and what limitation exists in conventional screening?\",\"answer\":\"Corrosion inhibitors effectively slow corrosion and reduce associated economic, environmental, and safety impacts. Conventional experimental screening of many inhibitor candidates requires significant time, money, and resources.\"},{\"question\":\"How does the study use QSPR-based machine learning for corrosion inhibition prediction?\",\"answer\":\"The study links quantitative structure-property relationships to molecular descriptors of quinoxaline compounds and trains machine learning regressors to predict inhibition efficiency based on those descriptors.\"},{\"question\":\"Which model performs best and what evidence supports that conclusion?\",\"answer\":\"The Extreme Gradient Boosting Regressor (XGBR) performs best. With hyperparameter tuning using grid search, it reaches the highest R² value (0.970) and shows the lowest error metrics (RMSE, MSE, MAD, and MAPE).\"}]","Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds - Research Report | PDF",1785896724,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"utilization-of-machine-learning-for-predicting-corrosion-inhibition-by-quinoxaline-compounds-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/utilization-of-machine-learning-for-predicting-corrosion-inhibition-by-quinoxaline-compounds-research-report/125114/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are corrosion inhibitors important and what limitation exists in conventional screening?","Question",{"text":75,"@type":76},"Corrosion inhibitors effectively slow corrosion and reduce associated economic, environmental, and safety impacts. 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