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AuthorSadah, Mohammed
AuthorEljack, Fadwa
AuthorKazi, Monzure-Khoda
AuthorAtilhan, Mert
Available date2024-08-22T05:21:28Z
Publication Date2024
Publication NameComputers and Chemical Engineering
ResourceScopus
ISSN981354
URIhttp://dx.doi.org/10.1016/j.compchemeng.2024.108715
URIhttp://hdl.handle.net/10576/57854
AbstractIn this article, we present a novel deep learning-based group contribution framework for the targeted design of ionic liquids (ILs). This computational framework can expedite and improve the process of finding desirable molecular structures of IL via accurate property predictions in a data-driven manner. Our proposed framework consists of two essential steps: establishing a correlation between IL viscosity and CO2 solubility by merging two deep learning models (DNN-GC and ANN-GC) and utilizing this correlation to identify the optimal IL structure with maximal CO2 absorption capacity. Our model achieves high accuracy with R2 values of 95%, 94.2%, and 96.4% for DNN-GC, ANN-GC, and DNN-ANN-GC, respectively. Correlation results align with the experimental data, affirming the applicability of our framework. Finally, the algorithm is employed in a CO2 capture case study to generate and select the best-performing novel ILs, which exhibit behavior consistent with established ILs in the literature.
SponsorThe authors acknowledge the paper was made possible by grant QUHI-CENG-22\u201323\u2013465 from Qatar University. The statements made herein are solely the responsibility of the author[s]. Open Access funding provided by the Qatar National Library.
Languageen
PublisherElsevier
SubjectCO2 capture
Computer-Aided Molecular Design
Deep learning
Group contribution
Ionic liquids
Machine learning
TitleDevelopment of a deep learning-based group contribution framework for targeted design of ionic liquids
TypeArticle
Volume Number186
dc.accessType Full Text


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