Multiple ensemble graph neural networks for the high-throughput screening of ionic liquids for carbon capture: Modeling study and experimental validation | Synapse
March 3, 2026
Multiple ensemble graph neural networks for the high-throughput screening of ionic liquids for carbon capture: Modeling study and experimental validation
Key Points
High-throughput screening identifies ionic liquids with promising carbon capture capabilities.
The top-performing ionic liquid demonstrated a carbon capture efficiency of 78% in experimental validation.
Modeling study employed multi-ensemble graph neural networks to predict ionic liquid performance.
Findings highlight the potential of machine learning methods to optimize carbon capture materials.