Hassane LGAZ

Associate Professor at Hanyang University, South Korea

Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids


Journal article


Taiwo W. Quadri, Lukman O. Olasunkanmi, Omolola E. Fayemi, Ekemini D. Akpan, Han-Seung Lee, Hassane Lgaz, Chandrabhan Verma, Lei Guo, Savaş Kaya, Eno E. Ebenso
Computational Materials Science, vol. 214, 2022


Cite

Cite

APA   Click to copy
Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Akpan, E. D., Lee, H.-S., Lgaz, H., … Ebenso, E. E. (2022). Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids. Computational Materials Science, 214. https://doi.org/10.1016/j.commatsci.2022.111753


Chicago/Turabian   Click to copy
Quadri, Taiwo W., Lukman O. Olasunkanmi, Omolola E. Fayemi, Ekemini D. Akpan, Han-Seung Lee, Hassane Lgaz, Chandrabhan Verma, Lei Guo, Savaş Kaya, and Eno E. Ebenso. “Multilayer Perceptron Neural Network-Based QSAR Models for the Assessment and Prediction of Corrosion Inhibition Performances of Ionic Liquids.” Computational Materials Science 214 (2022).


MLA   Click to copy
Quadri, Taiwo W., et al. “Multilayer Perceptron Neural Network-Based QSAR Models for the Assessment and Prediction of Corrosion Inhibition Performances of Ionic Liquids.” Computational Materials Science, vol. 214, 2022, doi:10.1016/j.commatsci.2022.111753.


BibTeX   Click to copy

@article{quadri2022a,
  title = {Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids},
  year = {2022},
  journal = {Computational Materials Science},
  volume = {214},
  doi = {10.1016/j.commatsci.2022.111753},
  author = {Quadri, Taiwo W. and Olasunkanmi, Lukman O. and Fayemi, Omolola E. and Akpan, Ekemini D. and Lee, Han-Seung and Lgaz, Hassane and Verma, Chandrabhan and Guo, Lei and Kaya, Savaş and Ebenso, Eno E.}
}