Ionic Liquids Curated by Machine Learning for Metal Extraction

Metals are key components of modern devices; however, available resources of these metals are limited. In this study, we used machine learning (ML) to curate ionic liquids (ILs) that are suitable for metal extraction. We proposed classification and regression models to unravel hidden patterns between IL structures and their specific properties, i.e., metal selectivity and eco-toxicity. Evaluations of ML models using cross-validation indicate that the models were reliable, as described by the accuracy score (0.82) and R2 value (0.76). The models also revealed that the metal selectivity of ILs was determined by the cation and anion structures, and the eco-toxicity level was primarily affected by the cation structures. Guided by predictions from the trained models, we selected three ILs (out of the 150 IL structures we initially proposed) that have extraction selectivity toward platinum, lithium, and neodymium as well as low eco-toxicity. We then prepared the ILs in the laboratory and assessed their performance by standard solvent extraction. The experiments indicate that the recommended ILs from ML could selectively extract the targeted metals with high extraction efficiency (>80%), which demonstrates the feasibility of ML as a promising toolkit that can help accelerate innovations in metal extraction.