Extension of scoring-assisted generative exploration for ionic liquids (SAGE-IL) and its application to ionic liquid design for CO<inf>2</inf> capture

Greenhouse gas emissions, particularly carbon dioxide (CO2), pose a significant threat to the climate, driving global warming and numerous environmental issues. Ionic liquids (ILs) have gained attention for CO2 capture due to their tunable properties, which can be optimized through the selection of specific cation-anion combinations. Traditional methods for discovering effective ILs are time-consuming and costly, necessitating the development of innovative computational approaches. This study extended the Scoring-Assisted Generative Exploration for Ionic Liquids (SAGE-IL) to generate novel ILs tailored for CO2 capture. SAGE-IL combines deep learning-based generative models with quantitative structure-property relationship models to design ILs with desired properties. Notably, SAGE-IL successfully performed single property optimization, either increasing or decreasing viscosity and melting point, through iterative interactions between generative and evaluation models. Furthermore, it achieved multiple property optimization, improving either CO2 solubility or activity coefficient while simultaneously optimizing density, melting point, viscosity, and synthetic accessibility. This approach not only accelerates the discovery of high-performance ILs but also highlights the potential of generative models in de novo molecular design, paving the way for future advancements in materials discovery.