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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Results in Chemistry (2023) Vol. 6, pp. 101126-101126
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Physica Scripta (2024) Vol. 99, Iss. 3, pp. 036006-036006
Closed Access | Times Cited: 29
Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Materials Today Communications (2024) Vol. 39, pp. 108758-108758
Closed Access | Times Cited: 26
Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Computational and Theoretical Chemistry (2024) Vol. 1236, pp. 114599-114599
Closed Access | Times Cited: 23
Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Deleted Journal (2024) Vol. 2, pp. 100007-100007
Open Access | Times Cited: 23
SMILES-based machine learning enables the prediction of corrosion inhibition capacity
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
MRS Communications (2024) Vol. 14, Iss. 3, pp. 379-387
Closed Access | Times Cited: 22
Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms
Wise Herowati, Wahyu Aji Eko Prabowo, Muhamad Akrom, et al.
KnE Engineering (2024)
Open Access | Times Cited: 19
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
Setyo Budi, Muhamad Akrom, Harun Al Azies, et al.
KnE Engineering (2024)
Open Access | Times Cited: 17
Unlocking the potential of FTIR for corrosion inhibition prediction exploiting principal component analysis: Machine learning for QSPR modeling
Ahmad‐Reza Sadeghi, M. Shariatmadar, S. Amoozadeh, et al.
Journal of the Taiwan Institute of Chemical Engineers (2025) Vol. 169, pp. 105998-105998
Closed Access | Times Cited: 2
Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds
Muhamad Akrom, T. Sutojo, Ayu Pertiwi, et al.
Journal of Physics Conference Series (2023) Vol. 2673, Iss. 1, pp. 012014-012014
Open Access | Times Cited: 29
Implementation of Quantum Machine Learning in Predicting Corrosion Inhibition Efficiency of Expired Drugs
Muhammad Reesa Rosyid, Lubna Mawaddah, Akbar Priyo Santosa, et al.
Materials Today Communications (2024) Vol. 40, pp. 109830-109830
Closed Access | Times Cited: 9
Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation
Muhamad Akrom
Journal of Multiscale Materials Informatics (2024) Vol. 1, Iss. 1, pp. 22-37
Open Access | Times Cited: 6
A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 2, pp. 100073-100073
Open Access | Times Cited: 6
Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono
Journal of Materials Engineering and Performance (2024)
Closed Access | Times Cited: 6
A feature restoration for machine learning on anti-corrosion materials
Supriadi Rustad, Muhamad Akrom, T. Sutojo, et al.
Case Studies in Chemical and Environmental Engineering (2024) Vol. 10, pp. 100902-100902
Open Access | Times Cited: 4
State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application
Dzaki Asari Surya Putra, Nibras Bahy Ardyansyah, Nicholaus Verdhy Putranto, et al.
Journal of Bio- and Tribo-Corrosion (2025) Vol. 11, Iss. 1
Closed Access
Stacking Classical-Quantum Hybrid Learner Approach for Corrosion Inhibition Efficiency of N-Heterocyclic Compounds
Muhamad Akrom, Supriadi Rustad, T. Sutojo, et al.
Results in Surfaces and Interfaces (2025), pp. 100462-100462
Open Access
Machine learning-based model for predicting performances of pyridines-quinolines as corrosion inhibitors on iron surfaces
Muhamad Akrom, Setyo Budi, Gustina Alfa Trisnapradika, et al.
AIP conference proceedings (2025) Vol. 3197, pp. 020010-020010
Closed Access
Predicting CO2 Adsorption in Metal-Organic Frameworks: Integrating Machine Learning with Virtual Sample Generation
Wahyu Aji Eko Prabowo, Muhamad Akrom, Supriadi Rustad, et al.
Results in Surfaces and Interfaces (2025), pp. 100505-100505
Open Access
Machine Learning and Density Functional Theory Investigation of Corrosion Inhibition Capability of Ionic Liquid
Aprilyani Nur Safitri, Muhamad Akrom, Harun Al Azies, et al.
International Journal of Advances in Data and Information Systems (2025) Vol. 6, Iss. 1, pp. 165-177
Open Access
Machine learning for pyrimidine corrosion inhibitor small dataset
Wise Herowati, Wahyu Aji Eko Prabowo, Muhamad Akrom, et al.
Theoretical Chemistry Accounts (2024) Vol. 143, Iss. 8
Closed Access | Times Cited: 3
Data-Driven Machine Learning Models and Computational Simulation Techniques for Prediction of Anti-Corrosion Properties of Novel Benzimidazole Derivatives
Christopher Ikechukwu Ekeocha, Ikechukwu Nelson Uzochukwu, Ini-Ibehe Nabuk Etim, et al.
Materials Today Communications (2024) Vol. 41, pp. 110156-110156
Closed Access | Times Cited: 2
Corrosion inhibition by Myristica fragrans extract in neem biodiesel: Evaluation by machine learning and traditional methods
S. Iyyappan, KP Vinod Kumar, P Ponram
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024) Vol. 238, Iss. 17, pp. 8829-8837
Closed Access | Times Cited: 1
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds
Aprilyani Nur Safitri, Gustina Alfa Trisnapradika, Achmad Wahid Kurniawan, et al.
Journal of Multiscale Materials Informatics (2024) Vol. 1, Iss. 1, pp. 16-21
Open Access | Times Cited: 1
A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds
Noor Ageng Setiyanto, Harun Al Azies, Usman Sudibyo, et al.
Journal of Multiscale Materials Informatics (2024) Vol. 1, Iss. 1, pp. 10-15
Open Access | Times Cited: 1
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