Screening Environmentally Benign Ionic Liquids for CO Absorption Using Representation Uncertainty-Based Machine Learning.

Screening ionic liquids (ILs) with low viscosity, low toxicity, and high CO absorption using machine learning (ML) models is crucial for mitigating global warming. However, when candidate ILs fall into the extrapolation zone of ML models, predictions may become unreliable, leading to poor decision-making. In this study, we introduce a "representation uncertainty" (RU) approach to quantify prediction uncertainty by employing four IL representations: molecular fingerprint, molecular descriptor, molecular image, and molecular graph.