The HSAB principle in the description of the inhibitive effectiveness of heterocyclic N-bases

Doroshenko, T. F.; Lyashchuk, S. N.; Skrypnik, Yu. G.

doi:10.1007/bf02758399

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…The dataset employed in this study comprises pyridine-quinoline derivative compounds gathered from Set et al [19], Skrypnik et al [23], and Doroshenko et al [24], constituting a total of 41 samples, each delineated by 20 quantum chemical descriptors (See Table 1). These descriptors function as input features for predicting the Corrosion Inhibition Efficiency (CIE) value as output target.…”

Section: Datasetmentioning

confidence: 99%

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Budi,

Akrom,

Al Azies

et al. 2024

KEG

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Historically, the exploration of corrosion inhibitor technology has relied extensively on experimental methodologies, which are inherently associated with substantial costs, prolonged durations, and significant resource utilization. However, the emergence of ML approaches has recently garnered attention as a promising avenue for investigating potential materials with corrosion inhibition properties. This study endeavors to enhance the predictive capacity of ML models by leveraging polynomial functions. Specifically, the investigation focuses on assessing the effectiveness of pyridinequinoline compounds in mitigating corrosion. Diverse ML models were systematically evaluated, integrating polynomial functions to augment their predictive capabilities. The integration of polynomial functions notably amplifies the predictive accuracy across all tested models. Notably, the SVR model emerges as the most adept, exhibiting R² of 0.936 and RMSE of 0.093. The outcomes of this inquiry underscore a significant enhancement in predictive accuracy facilitated by the incorporation of polynomial functions within ML models. The proposed SVR model stands out as a robust tool for prognosticating the corrosion inhibition potential of pyridine-quinoline compounds. This pioneering approach contributes invaluable insights into advancing machine learning methodologies geared toward designing and engineering materials with promising corrosion inhibition properties. Keywords: machine learning, polynomial, corrosion inhibition, pyridine-quinoline

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Section: Datasetmentioning

confidence: 99%

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Budi,

Akrom,

Al Azies

et al. 2024

KEG

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show abstract

“…In this work, we used published datasets from our references research [11], [21], [22]. This regression dataset is pyridines-quinoline derivatives that have 41 derivatives compounds and 20 quantum chemical descriptors.…”

Section: Dataset and Descriptormentioning

confidence: 99%

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Budi

Akrom

Trisnapradika

et al. 2023

SJI

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Purpose: Experimental studies are usually costly, time-consuming, and resource-intensive when it comes to investigating prospective corrosion inhibitor compounds. Machine learning (ML) based on the quantitative structure-property relationship model (QSPR) has become a massive method for testing the effectiveness of chemical compounds as corrosion inhibitors. The main challenge in the ML method is to design a model that produces high prediction accuracy so that the properties of a material can be predicted accurately. In this study, we examine the performance of polynomial functions in the ML-based NuSVR algorithm in evaluating the regression dataset of corrosion inhibition efficiency of pyridine-quinoline compounds.Methods: Polynomial functions for NuSVR algorithm-based ML.Result: The outcomes demonstrate that the NuSVR model's prediction ability is greatly enhanced by the application of polynomial functions. Originality: The combination of polynomial functions and deep machine learning based NuSVR algorithms to increase the accuracy of predictive models.

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“…In this work, we develop a QSPR-based ML model with a comparative analysis between algorithms to evaluate the corrosion inhibition performance of pyridine-quinoline organic compounds using datasets in the literature [20], [23], [24], [25], [26]. Various DFT-calculated quantum chemical descriptors in the dataset were used to build a statistically validated QSPR model to consider, analyze, and model to guide the design of corrosion inhibition.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning

Herowati,

Akrom,

Hidayat

et al. 2024

JIMAT

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Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.

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The HSAB principle in the description of the inhibitive effectiveness of heterocyclic N-bases

Cited by 4 publications

References 1 publication

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Implementation of Polynomial Functions to Improve the Accuracy of Machine Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds as Corrosion Inhibitors

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning

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