Investigasi Model Machine Learning Regresi Pada Senyawa Obat Sebagai Inhibitor Korosi DOI Open Access

Muhammad Reesa Rosyid,

Lubna Mawaddah,

Muhamad Akrom

et al.

Jurnal Algoritma, Journal Year: 2024, Volume and Issue: 21(1), P. 332 - 342

Published: July 29, 2024

Korosi merupakan tantangan signifikan bagi daya tahan material, yang seringkali menyebabkan kerugian ekonomi besar. Penelitian ini memanfaatkan teknik Machine Learning (ML) untuk memprediksi efektivitas senyawa obat sebagai inhibitor korosi. Kami menggunakan lima algoritma ML menonjol: Regresi Linear, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Model-model dilatih dievaluasi dataset terdiri dari 14 fitur molekuler dengan efisiensi inhibisi korosi (IE%) variabel target. Hasil pelatihan model awal mengidentifikasi Forest XGBoost berkinerja terbaik berdasarkan metrik seperti Mean Squared Error (MSE), Root (RMSE), Absolute (MAE), R-squared (R²). Penyetelan hiperparameter lebih lanjut GridSearchCV menunjukkan bahwa XGBoost, setelah penyetelan, secara mengungguli lainnya, mencapai kesalahan terendah nilai R² tertinggi, akurasi prediktif superior aplikasi ini. Temuan menegaskan potensi ML, khususnya dalam meningkatkan pemodelan korosi, sehingga memberikan wawasan berharga bidang ilmu

Quantum machine learning for ABO3 perovskite structure prediction DOI
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Computational Materials Science, Journal Year: 2025, Volume and Issue: 250, P. 113694 - 113694

Published: Jan. 16, 2025

Language: Английский

Citations

3
Implementation of Quantum Machine Learning in Predicting Corrosion Inhibition Efficiency of Expired Drugs DOI

Muhammad Reesa Rosyid,

Lubna Mawaddah,

Akbar Priyo Santosa

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 40, P. 109830 - 109830

Published: July 17, 2024

Language: Английский

Citations

9
Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation DOI Creative Commons

Muhamad Akrom

Journal of Multiscale Materials Informatics, Journal Year: 2024, Volume and Issue: 1(1), P. 22 - 37

Published: April 29, 2024

This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing potential plant extracts, this study scrutinizes their effectiveness in mitigating alloys through multi-faceted approach. By integrating computational modeling with empirical experimentation, deeper understanding inhibitive mechanisms is achieved, offering insights practical application. The synthesizes findings from diverse studies, elucidating pivotal role DFT predicting inhibitor behavior optimizing performance. Furthermore, validation provides crucial theoretical predictions, highlighting synergistic relationship between simulation real-world Through journey exploration, underscores promise derived natural sources, paving way sustainable control practices alloys.

Language: Английский

Citations

6
A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds DOI Creative Commons
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100073 - 100073

Published: July 10, 2024

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating chemical properties (QCP) features reduced computational burden by strategically reducing from 11 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial networks (ANN) and multilayer perceptron (MLPNN), coefficient determination (R2) value 0.987, diminished root mean square error (RMSE), absolute (MAE), deviation (MAD) values 0.97, 0.92, 1.10, respectively. Predictions for six newly synthesized derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] (Q6), show remarkable CIE 95.12, 96.72, 91.02, 92.43, 89.58, 93.63 %, This breakthrough technique simplifies testing production procedures new anti-corrosion materials.

Language: Английский

Citations

6
Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning DOI
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Journal of Materials Engineering and Performance, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 23, 2024

Language: Английский

Citations

6
A feature restoration for machine learning on anti-corrosion materials DOI Creative Commons
Supriadi Rustad, Muhamad Akrom,

T. Sutojo

et al.

Case Studies in Chemical and Environmental Engineering, Journal Year: 2024, Volume and Issue: 10, P. 100902 - 100902

Published: Aug. 16, 2024

Language: Английский

Citations

4
State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors DOI
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 6, 2025

Artificial intelligence (AI) and machine learning (ML) have attracted the interest of research community in recent years. ML has found applications various areas, especially where relevant data that could be used for algorithm training retraining are available. In this review article, been discussed relation to its corrosion science, monitoring control. tools techniques, structure modeling methods, were thoroughly discussed. Furthermore, detailed inhibitor design/modeling coupled with associated limitations future perspectives reported.

Language: Английский

Citations

0
Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application DOI

Dzaki Asari Surya Putra,

Nibras Bahy Ardyansyah,

Nicholaus Verdhy Putranto

et al.

Journal of Bio- and Tribo-Corrosion, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 21, 2025

Language: Английский

Citations

0
Quantum Circuit Learning for Predicting Nature of Band Gap of Perovskite Oxides DOI

Muhamad Akrom,

Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Stacking Classical-Quantum Hybrid Learner Approach for Corrosion Inhibition Efficiency of N-Heterocyclic Compounds DOI Creative Commons
Muhamad Akrom, Supriadi Rustad,

T. Sutojo

et al.

Results in Surfaces and Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 100462 - 100462

Published: Feb. 1, 2025

Language: Английский

Citations

0