Coarse-Grained Molecular Dynamics of Ionic Transport in Polymer Electrolyte

Solid-state conducting polymers have become promising candidates in the search of low cost and safe electrolyte materials. However, current solid polymer electrolytes only operate under limited conditions with relatively low ion conductivity. The improvement of polymer properties requires a deeper understanding of the mechanisms of ionic transport in polymer networks, where modeling and computation can play an important role. In this work, we develop a coarse-grained molecular dynamics (CGMD) model with parameters calibrated by full atomistic simulations. Our model captures the conformational evolution of polymer chains during the transport process, for both self-doped and salt-doped polymers. The correlations between the motion of ions and the structure of the polymer network are revealed using advanced machine learning algorithms. In addition, to quantify the electrolyte performance, the lithium-ion diffusivity and conductivity are extracted from the simulations. Via a high throughput computation approach, we screen the effects of both operation conditions and intrinsic polymer properties on the conduction of the polymer electrolyte, providing useful guidance for designing future polymer electrolyte materials.