Publications

Abstract not provided.

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350-500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. We also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

Abstract not provided.