Design of Supported Ionic Liquid Membranes for CO2 Capture Using a Generative AI-Based Approach

Abstract

Growing urgency to address climate change has accelerated the development of efficient carbon capture technologies. However, traditional approaches to design materials for CO2 capture are often hindered by time-consuming and costly experimental processes. This study investigates the application of generative AI, specifically a conditional variational autoencoder (CVAE), to accelerate the discovery and design of supported ionic liquid membranes (SILMs) for enhanced CO2 capture. By leveraging a limited experimental data set, our CVAE model generates and predicts a large number of synthetic SILM candidates, significantly reducing the need for extensive trial-and-error experiments. The SILMs with predicted CO2 capture capacity are then selected for synthesis and experimental evaluation. The experimental results indicate that the model demonstrates strong predictive accuracy, showing close agreement between predicted and measured values. This AI-driven approach offers a cost-effective and efficient pathway to rapidly explore vast design spaces, potentially revolutionizing the development of advanced materials for carbon capture.