A machine learning approach for forecasting the efficacy of pyridazine corrosion inhibitors DOI
Gustina Alfa Trisnapradika,

Muhamad Akrom,

Supriadi Rustad

et al.

Theoretical Chemistry Accounts, Journal Year: 2024, Volume and Issue: 144(1)

Published: Dec. 5, 2024

Language: Английский

Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning DOI
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Computational and Theoretical Chemistry, Journal Year: 2024, Volume and Issue: 1236, P. 114599 - 114599

Published: April 8, 2024

Language: Английский

Citations

23
Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds DOI Creative Commons
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 2, P. 100007 - 100007

Published: April 16, 2024

This work used a variational quantum circuit (VQC) in conjunction with quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as inhibitors. Compared conventional methods like multilayer perceptron neural networks (MLPNN), VQC predicts CIE more accurately. With coefficient of determination (R2), root mean square error (RMSE), absolute (MAE), and deviation (MAD) values 0.989, 0.027, 0.024, 0.019, respectively, performs better. The established outstanding predictive accuracy for four newly synthesized pyrimidine derivative compounds: 1-(4-fluorophenyl)- 3-(4-(pyridin-4-ylmethyl)phenyl)urea (P1), 1-phenyl-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P2), 1-(4-methylphenyl)-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P3), quaternary ammonium salt dimer (P4). It generates remarkably high 92.87, 94.05, 94.96, 96.93 P1, P2, P3, P4, respectively. its ability streamline testing production processes novel anti-corrosion materials, this innovative approach holds potential revolutionize market.

Language: Английский

Citations

23
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
Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors DOI Creative Commons
David A. Winkler,

A.E. Hughés,

Can Özkan

et al.

Progress in Materials Science, Journal Year: 2024, Volume and Issue: unknown, P. 101392 - 101392

Published: Oct. 1, 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