Investigation and Analysis of Influencing Factors of Innovation Quality of Data Science Students Based on Machine Learning DOI
Yingbing Fan,

Lina Sun,

Zhenxu Huang

et al.

Published: April 26, 2024

Language: Английский

SMILES-based machine learning enables the prediction of corrosion inhibition capacity DOI
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono

et al.

MRS Communications, Journal Year: 2024, Volume and Issue: 14(3), P. 379 - 387

Published: April 15, 2024

Language: Английский

Citations

22
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
Unlocking the potential of FTIR for corrosion inhibition prediction exploiting principal component analysis: Machine learning for QSPR modeling DOI
Ahmad‐Reza Sadeghi,

M. Shariatmadar,

S. Amoozadeh

et al.

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2025, Volume and Issue: 169, P. 105998 - 105998

Published: Jan. 28, 2025

Language: Английский

Citations

2
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
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