Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models

Abstract

For discovering uncharted chemical space of ionic liquids (ILs) for CO₂ dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO₂ solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO₂ solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO₂ solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO₂ solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.