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. We develop four types of ML models based on these representations and calculate RU as the standard deviation of predictions across these models. Compared to traditional model uncertainty (MU), which is based on hyperparameter variations within a single representation, RU outperforms MU in identifying unreliable predictions across four IL property data sets: viscosity, toxicity, refractive index, and CO absorption capacity. Furthermore, we develop ensemble models from the four types of models, which show superior predictive performance compared with that of individual models. Using the RU approach, we screened 1420 ILs and identified 37 promising candidates with low viscosity, low toxicity, and high CO absorption capacity. The predictive performance of our ensemble model, along with the effectiveness of the RU-based approach, was experimentally validated by testing the CO absorption capacity of 14 ILs. This study not only offers a more reliable method for screening and designing ILs, accelerating the discovery process, but also introduces a new perspective on developing ensemble models with enhanced predictive performance.