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June 26, 2024Scientific ReportsOpen Access

Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models

Mazhar Ali Tooba Sarwar Nabisab Mujawar Mubarak Rama Rao Karri Lubna Ghalib Aisha Bibi Shaukat Ali Mazari

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Key Points

Deep learning models accurately predict carbon dioxide solubility across diverse ionic liquids, providing an alternative to costly laboratory testing.
Accurate solubility estimates enable rapid screening of novel ionic liquid candidates, reducing computational time while maintaining predictive precision.
Computational modeling using deep learning frameworks supports the high-throughput design of ionic liquid systems for industrial carbon capture applications.

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Implication

Computational model predicts carbon dioxide solubility in ionic liquids, indicating potential for accelerated design of carbon capture systems.

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Cite This Study

Ali et al. (2024) studied this question.

synapsesocial.com/papers/68e6327cb6db6435875c4137 https://doi.org/10.1038/s41598-024-65499-y

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Prediction of CO2 solubility in ionic liquids using simplified molecular input line entry system and machine learning methodsOriginal Research · 2026
2Machine Learning–Driven Discovery of Sustainable Ionic Liquids for CO <sub>2</sub> Capture from Large Chemical SpacesOriginal Research · 2026
3Machine learning boosted Design of Ionic Liquid-based Deep Eutectic Solvents for CO2 CaptureOriginal Research · 2026 · 4 citations
4Nanostructure and interactions in ionic liquids with carbon dioxide: Understanding cavity formation and solvent reorganization2025 · 4 citations
5Machine learning for ionic liquid discovery in gas absorption: From solubility modeling to high‐throughput screeningOriginal Research · 2026 · 1 citations

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