{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Ali M"],"pubmed_abstract":["Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO<sub>2</sub>). The prediction of CO<sub>2</sub> solubility in ILs is crucial for optimizing CO<sub>2</sub> capture processes. This study investigates the use of deep learning models for CO<sub>2</sub> solubility prediction in ILs with a comprehensive dataset of 10,116 CO<sub>2</sub> solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO<sub>2</sub> solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO<sub>2</sub> solubility, with coefficient of determination (R<sup>2</sup>) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO<sub>2</sub> solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO<sub>2</sub> solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO<sub>2</sub> capture applications."],"journal":["Scientific reports"],"pagination":["14730"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11208552"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of CO<sub>2</sub> solubility in Ionic liquids for CO<sub>2</sub> capture using deep learning models."],"pmcid":["PMC11208552"],"pubmed_authors":["Mazari SA","Ghalib L","Mubarak NM","Sarwar T","Bibi A","Ali M","Karri RR"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of CO<sub>2</sub> solubility in Ionic liquids for CO<sub>2</sub> capture using deep learning models.","description":"Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO<sub>2</sub>). The prediction of CO<sub>2</sub> solubility in ILs is crucial for optimizing CO<sub>2</sub> capture processes. This study investigates the use of deep learning models for CO<sub>2</sub> solubility prediction in ILs with a comprehensive dataset of 10,116 CO<sub>2</sub> solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO<sub>2</sub> solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO<sub>2</sub> solubility, with coefficient of determination (R<sup>2</sup>) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO<sub>2</sub> solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO<sub>2</sub> solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO<sub>2</sub> capture applications.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2025-04-21T20:18:22.784Z","creation":"2025-02-19T00:17:23.001Z"},"accession":"S-EPMC11208552","cross_references":{"pubmed":["38926595"],"doi":["10.1038/s41598-024-65499-y"]}}