Anomalous Diffusion of Lithium-Anion Clusters in Ionic Liquids

TL;DR

This study uses molecular dynamics simulations and machine learning to analyze lithium-ion transport in ionic liquids, revealing non-Poissonian bursty shell exchanges and identifying the shell-soft state as crucial for enhanced transport.

Contribution

It introduces a two-state model for lithium transport in ionic liquids and employs machine learning to identify key states influencing ion mobility.

Findings

Shell-soft state predominantly facilitates lithium transport.

Transport involves power-law distributed shell exchanges.

Lower temperatures increase the importance of the shell-soft state.

Abstract

Lithium-ion transport is significantly retarded in ionic liquids (ILs). In this work, we performed extensive molecular dynamics (MD) simulations to mimic the kinetics of lithium ions in ILs using [\emph{N}-methyl-\emph{N}-propylpyrrolidium (pyr$_{13}$)][bis(trifluoromethanesulfonyl)imide (Ntf$_{2}$)] with added LiNtf$_{2}$ salt. And we analyzed their transport, developing a two-state model and comparing it to the machine learning-identified states. The transport of lithium ions involves local shell exchanges of the Ntf$_{2}$ in the medium. We calculated train size distributions over various time scales. The train size distribution decays as a power law, representing non-Poissonian bursty shell exchanges. We analyzed the non-Poissonian processes of lithium ions transport as a two-state (soft and hard) model. We analytically calculated the transition probability of the two-state model, which fits well to the lifetime autocorrelation functions of LiNtf$_{2}$ shells. To identify two states, we introduced the graph neutral network incorporating local molecular structure. The results reveal that the shell-soft state mainly contributes to the transport of the lithium ions, and their contribution is more important in low temperatures. Hence, it is the key for enhanced lithium ion transport to increase the fraction of the shell-soft state.