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 an

SUBMITTER: Ismail S 

PROVIDER: S-EPMC11869161 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature