Issue 10, 2023

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Abstract

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.

Graphical abstract: Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Supplementary files

Article information

Article type
Paper
Submitted
24 Cʼhwe. 2023
Accepted
31 Mae 2023
First published
13 Mezh. 2023

Mol. Syst. Des. Eng., 2023,8, 1257-1274

Author version available

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

N. S. Bobbitt, J. P. Allers, J. A. Harvey, D. Poe, J. D. Wemhoner, J. Keth and J. A. Greathouse, Mol. Syst. Des. Eng., 2023, 8, 1257 DOI: 10.1039/D3ME00033H

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